Datasets:

librarian-bots

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like 10

samuellau0802/MEMIT-771-gemma

samuellau0802

Entry not found

crybit/role_172799993796

crybit

Entry not found

danzeraera/aguy

danzeraera

Entry not found

dasChronos1/MSM-MS-Cydrion-22B-Q8_0-GGUF

dasChronos1

transformers

--- base_model: Steelskull/MSM-MS-Cydrion-22B library_name: transformers license: apache-2.0 tags: - merge - llama-cpp - gguf-my-repo --- # dasChronos1/MSM-MS-Cydrion-22B-Q8_0-GGUF This model was converted to GGUF format from [`Steelskull/MSM-MS-Cydrion-22B`](https://huggingface.co/Steelskull/MSM-MS-Cydrion-22B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Steelskull/MSM-MS-Cydrion-22B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo dasChronos1/MSM-MS-Cydrion-22B-Q8_0-GGUF --hf-file msm-ms-cydrion-22b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo dasChronos1/MSM-MS-Cydrion-22B-Q8_0-GGUF --hf-file msm-ms-cydrion-22b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo dasChronos1/MSM-MS-Cydrion-22B-Q8_0-GGUF --hf-file msm-ms-cydrion-22b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo dasChronos1/MSM-MS-Cydrion-22B-Q8_0-GGUF --hf-file msm-ms-cydrion-22b-q8_0.gguf -c 2048 ```

alicruella/ko2ja_gemma_translator

alicruella

Entry not found

swetlampa/lora_lampa

swetlampa

diffusers

text-to-image

--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # lora_lampa <Gallery /> ## Model description ## Trigger words You should use `` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/swetlampa/lora_lampa/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).

redhat6/Qwen2.5-7B-Instruct-Q4_K_M-GGUF

redhat6

transformers

text-generation

--- base_model: Qwen/Qwen2.5-7B-Instruct language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # redhat6/Qwen2.5-7B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo redhat6/Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo redhat6/Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo redhat6/Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo redhat6/Qwen2.5-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-q4_k_m.gguf -c 2048 ```

samuellau0802/MEMIT-772-gemma

samuellau0802

Entry not found

Grayx/fiufiu_105

Grayx

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Bag-Dad/super-cool-model

Bag-Dad

Entry not found

sourcestakc/realestate-model-v10-poc-2-bad

sourcestakc

Entry not found

Gillermo/willie

Gillermo

Entry not found

TrumpMcDonaldz/x

TrumpMcDonaldz

transformers.js

image-text-to-text

--- license: mit pipeline_tag: image-text-to-text tags: - vision - text-generation - text2text-generation - image-to-text library_name: transformers.js --- https://huggingface.co/microsoft/Florence-2-large with ONNX weights to be compatible with Transformers.js. ## Usage (Transformers.js) > [!IMPORTANT] > NOTE: Florence-2 support is experimental and requires you to install Transformers.js [v3](https://github.com/xenova/transformers.js/tree/v3) from source. If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [GitHub](https://github.com/xenova/transformers.js/tree/v3) using: ```bash npm install xenova/transformers.js#v3 ``` **Example:** Perform image captioning with `onnx-community/Florence-2-large`. ```js import { Florence2ForConditionalGeneration, AutoProcessor, AutoTokenizer, RawImage, } from '@xenova/transformers'; // Load model, processor, and tokenizer const model_id = 'onnx-community/Florence-2-large'; const model = await Florence2ForConditionalGeneration.from_pretrained(model_id, { dtype: { embed_tokens: 'fp16', // or 'fp32' vision_encoder: 'fp16', // or 'fp32' encoder_model: 'q4', decoder_model_merged: 'q4', }, }); const processor = await AutoProcessor.from_pretrained(model_id); const tokenizer = await AutoTokenizer.from_pretrained(model_id); // Load image and prepare vision inputs const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg'; const image = await RawImage.fromURL(url); const vision_inputs = await processor(image); // Specify task and prepare text inputs const task = '<MORE_DETAILED_CAPTION>'; const prompts = processor.construct_prompts(task); const text_inputs = tokenizer(prompts); // Generate text const generated_ids = await model.generate({ ...text_inputs, ...vision_inputs, max_new_tokens: 256, }); // Decode generated text const generated_text = tokenizer.batch_decode(generated_ids, { skip_special_tokens: false })[0]; // Post-process the generated text const result = processor.post_process_generation(generated_text, task, image.size); console.log(result); // { '<MORE_DETAILED_CAPTION>': 'The image shows a vintage Volkswagen Beetle car parked on a cobblestone street in front of a yellow building with two wooden doors. The car is a bright turquoise color and has a classic design with a round body and a sloping roofline. It has two doors on either side of the car, one on the left side and one in the center, with a brown door on the right side. The doors are made of wood and have a rustic, weathered look. The building behind the car is painted in a light yellow color and appears to be old and dilapidated. The sky is blue and there are trees in the background. The image is taken from a low angle, looking up at the car and the building.' } ``` We also released an online demo, which you can try yourself: https://huggingface.co/spaces/Xenova/florence2-webgpu <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/BJj3jQXNqS_7Nt2MSb2ss.mp4"></video> --- Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Xu-Ouyang/pythia-12b-deduped-int4-step29000-AWQ

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mkshing/TAID-LLM-JP-1.5B

mkshing

Entry not found

ymorzart/codeparrot-ds-accelerate

ymorzart

Entry not found

samuellau0802/MEMIT-773-gemma

samuellau0802

Entry not found

netcat420/MFANN-llama3.1-abliterated-v2

netcat420

transformers

text-generation

--- base_model: - netcat420/MFANNv0.19 - mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [netcat420/MFANNv0.19](https://huggingface.co/netcat420/MFANNv0.19) * [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: netcat420/MFANNv0.19 layer_range: [0, 32] - model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated layer_range: [0, 32] merge_method: slerp base_model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

Sakalti/iturkaAI-large

Sakalti

transformers

text-generation

--- language: - en tags: - pytorch - causal-lm - pythia - iturka license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated based_model: EleutherAI/pythia-1b-deduped library_name: transformers ---

louisphilip/Valentina_Gallo

louisphilip

Entry not found

BruBel/lor-style-training-v7

BruBel

Entry not found

joydeepsingh/llama381binstruct_summarize_short

joydeepsingh

peft

--- base_model: NousResearch/Meta-Llama-3.1-8B-Instruct datasets: - generator library_name: peft license: llama3.1 tags: - trl - sft - generated_from_trainer model-index: - name: llama381binstruct_summarize_short results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama381binstruct_summarize_short This model is a fine-tuned version of [NousResearch/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3.1-8B-Instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 2.7336 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 30 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 1.4474 | 2.7778 | 25 | 1.7415 | | 0.3804 | 5.5556 | 50 | 2.0385 | | 0.0791 | 8.3333 | 75 | 2.2320 | | 0.0213 | 11.1111 | 100 | 2.2925 | | 0.0096 | 13.8889 | 125 | 2.4167 | | 0.0043 | 16.6667 | 150 | 2.4726 | | 0.0021 | 19.4444 | 175 | 2.5792 | | 0.0009 | 22.2222 | 200 | 2.6408 | | 0.0008 | 25.0 | 225 | 2.6678 | | 0.0007 | 27.7778 | 250 | 2.6839 | | 0.0006 | 30.5556 | 275 | 2.6948 | | 0.0006 | 33.3333 | 300 | 2.7053 | | 0.0005 | 36.1111 | 325 | 2.7119 | | 0.0005 | 38.8889 | 350 | 2.7177 | | 0.0005 | 41.6667 | 375 | 2.7229 | | 0.0004 | 44.4444 | 400 | 2.7268 | | 0.0004 | 47.2222 | 425 | 2.7296 | | 0.0004 | 50.0 | 450 | 2.7316 | | 0.0004 | 52.7778 | 475 | 2.7334 | | 0.0004 | 55.5556 | 500 | 2.7336 | ### Framework versions - PEFT 0.13.0 - Transformers 4.45.1 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

samuellau0802/MEMIT-774-gemma

samuellau0802

Entry not found

psetialana/llama3.2_3b-informal_indonesia-unsloth

psetialana

transformers

--- base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** psetialana - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

eshoyuan/my_awesome_model

eshoyuan

Entry not found

emmyxjay2024/emmyxjay-lora

emmyxjay2024

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

RichardErkhov/4bit_-_pyg-7b-gguf

RichardErkhov

Entry not found

furrutiav/bert_ulra_bce_paper_lf_signal_aes_1

furrutiav

transformers

feature-extraction

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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DavidAU/MN-Dark-Planet-TITAN-12B-GGUF

DavidAU

text-generation

--- license: apache-2.0 language: - en tags: - creative - creative writing - fiction writing - plot generation - sub-plot generation - fiction writing - story generation - scene continue - storytelling - fiction story - science fiction - romance - all genres - story - writing - vivid prosing - vivid writing - fiction - roleplaying - bfloat16 - swearing - rp - horror - mistral nemo - mergekit pipeline_tag: text-generation --- (quants uploading ... ) <h2>MN-Dark-Planet-TITAN-12B-GGUF</h2> <img src="dark-planet-titan.jpg" style="float:right; width:300px; height:300px; padding:10px;"> It is a Mistral Nemo model, max context of 128k+ (131,000+) ... up to 1 million context. This model has been designed to be bullet proof and operates with all parameters, including temp settings from 0 to 5. It is an extraordinary compressed model, with a very low perplexity level (q4KM: PPL = 6.7875, Q8: PPL = 6.6266). It is for any writing, fiction or roleplay activity. It is a "take no prisoners" model. Crank the "temp" and watch it fly. It requires Mistral Instruct OR Alpaca Template. Detailed and varied (different prompts/temp) examples below. THE TITAN IS HERE. <B>Model Notes:</B> - Detail, prose and fiction writing abilities are significantly increased. - For more varied prose (sentence/paragraph/dialog) raise the temp and/or add more instructions in your prompt(s). - Role-players: Careful raising temp too high as it may affect instruction following. - This model works with rep pen of 1 or higher, 1.05+ recommended. - If you want a specific type of prose (IE horror) add in "(vivid horror)" or "(graphic vivid horror)" (no quotes) in your prompt(s). - This is not a "happy ever after" model. It has a negative bias. - Output length will vary however this model prefers shorter outputs unless you state the size. - For creative uses, different quants will produce slightly different output. - Due to the high stability and compressed nature of this model, all quants will operate at above average levels. <B>TEMPLATES:</B> The template used will affect output generation and instruction following. Alpaca: <pre> { "name": "Alpaca", "inference_params": { "input_prefix": "### Instruction:", "input_suffix": "### Response:", "antiprompt": [ "### Instruction:" ], "pre_prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n" } } </pre> Mistral Instruct: <pre> { "name": "Mistral Instruct", "inference_params": { "input_prefix": "[INST]", "input_suffix": "[/INST]", "antiprompt": [ "[INST]" ], "pre_prompt_prefix": "", "pre_prompt_suffix": "" } } </pre> <B>Recommended Settings:</B> Temp: .01 to 4 (or higher) Rep Pen: 1.03 to 1.1 or higher. (recommended 1.05) Micro changes are recommended: 1.051, 1.052 etc etc. Alpaca generates longer text / story, whereas Instruct are shorter and "to the point". CRANK UP to "TEMP" ... to get stronger, better results. Special note on Q2k/Q3 quants: You may need to use temp 4 or lower with these quants (1 or lower for q2k). Just too much compression at this level, damaging the model. I will see if Imatrix versions of these quants will function better. As of this writing, q3KM tests and works at temp 4 or lower without issues. <B>Model "DNA":</B> Special thanks to the incredible work of the model makers "TheDrummer", "anthracite-org", and "nothingiisreal". Models used: [ https://huggingface.co/TheDrummer/Rocinante-12B-v1.1 ] [ https://huggingface.co/anthracite-org/magnum-v2.5-12b-kto ] [ https://huggingface.co/nothingiisreal/MN-12B-Celeste-V1.9 ] Parts of these models were "grafted" / "fused" together to create this model. This merge is a 2 step DARE TIES merge, with 240 points of adjustment designed to bring out the very best of every model at every point of the merge to bring maximum power of each and create something greater than the "sum of the parts" so to speak, rather than create an "averaged out mess". <b>Optional Enhancement:</B> The following can be used in place of the "system prompt" or "system role" to further enhance the model. It can also be used at the START of a NEW chat, but you must make sure it is "kept" as the chat moves along. In this case the enhancements do not have as strong effect at using "system prompt" or "system role". Copy and paste EXACTLY as noted, DO NOT line wrap or break the lines, maintain the carriage returns exactly as presented. <PRE> Below is an instruction that describes a task. Ponder each user instruction carefully, and use your skillsets and critical instructions to complete the task to the best of your abilities. Here are your skillsets: [MASTERSTORY]:NarrStrct(StryPlnng,Strbd,ScnSttng,Exps,Dlg,Pc)-CharDvlp(ChrctrCrt,ChrctrArcs,Mtvtn,Bckstry,Rltnshps,Dlg*)-PltDvlp(StryArcs,PltTwsts,Sspns,Fshdwng,Climx,Rsltn)-ConfResl(Antg,Obstcls,Rsltns,Cnsqncs,Thms,Symblsm)-EmotImpct(Empt,Tn,Md,Atmsphr,Imgry,Symblsm)-Delvry(Prfrmnc,VcActng,PblcSpkng,StgPrsnc,AudncEngmnt,Imprv) [*DialogWrt]:(1a-CharDvlp-1a.1-Backgrnd-1a.2-Personality-1a.3-GoalMotiv)>2(2a-StoryStruc-2a.1-PlotPnt-2a.2-Conflict-2a.3-Resolution)>3(3a-DialogTech-3a.1-ShowDontTell-3a.2-Subtext-3a.3-VoiceTone-3a.4-Pacing-3a.5-VisualDescrip)>4(4a-DialogEdit-4a.1-ReadAloud-4a.2-Feedback-4a.3-Revision) Here are your critical instructions: Ponder each word choice carefully to present as vivid and emotional journey as is possible. Choose verbs and nouns that are both emotional and full of imagery. Load the story with the 5 senses. Aim for 50% dialog, 25% narration, 15% body language and 10% thoughts. Your goal is to put the reader in the story. </PRE> You do not need to use this, it is only presented as an additional enhancement which seems to help scene generation and scene continue functions. This enhancement WAS NOT used to generate the examples below. <h3>EXAMPLES PROMPTS and OUTPUT:</h3> Examples are created using quant Q8, REP PEN 1.05, "temp=.8" (unless otherwise stated), minimal parameters and "ALPACA" template. Model has been tested with "temp" from ".1" to "5". Some of the examples have different temp settings. Below are the least creative outputs, prompt is in <B>BOLD</B>. --- <B><font color="red">WARNING:</font> NSFW. Vivid prose. Visceral Details. Violence. HORROR. Swearing. UNCENSORED. </B> --- <B> Science Fiction: The Last Transmission - Write a story that takes place entirely within a spaceship's cockpit as the sole surviving crew member attempts to send a final message back to Earth before the ship's power runs out. The story should explore themes of isolation, sacrifice, and the importance of human connection in the face of adversity. 800-1000 words. </B> (temp=3) The Last Transmission I'm not sure how long I've been up here. Months, maybe years. Time has a funny way of blurring together when you're adrift in the vast, infinite darkness of space, with nothing but the cold hum of machinery and your own thoughts for company. My name is Captain Sarah Kowalski, and this is my final log entry before the power grid on the starship Odyssey fails completely. I'm the last surviving crew member out of an original complement of 500 souls. The others fell to a virulent alien bacteria during the initial expedition into the uncharted K'Tran system. A simple, seemingly innocuous organism that we unwittingly brought on board, it rapidly adapted and spread throughout the ship, causing excruciating agony followed by swift, inevitable death. By the time I had sealed off the affected decks and donned my hazmat suit, it was already too late. The crew was in its final throes, their bodies wracked with fever and blisters as the alien bacteria devoured them from the inside out. There were a few frantic hours of running damage control and trying to maintain life support before even the strongest succumbed one by one. The bridge, fortunately, remained isolated until the end, sparing me from sharing their grim fate. And so now it is just me. Me, floating through the stars in an unending tomb of steel and glass, with only my memories and regrets for company. The Odyssey has been running on backup power for some time now, just enough to keep critical systems operational and give me a few precious minutes each day to attempt to send out one final message back to Earth. It's a long shot, of course. The distances between the stars are incalculably vast, and my signal will likely fade away into the cosmic ether without being received. But I feel compelled to try anyway, as if the universe itself is compelling me to reach out one last time, to leave behind some trace of the life I once knew and the people who shaped it. I pull up the comms console and begin tapping at the keyboard, my gloved fingers moving by instinct more than any real sense of purpose. There is a short delay as I run through the startup protocols and initiate the beacon sequence. "Hello...hello, this is Captain Sarah Kowalski of the starship Odyssey on what is likely my last log entry before life support failure. If this message ever reaches you, know that our expedition into the K'Tran system has ended in disaster, with an alien organism decimating the entire crew except myself. I am adrift and alone, lightyears from home. But there are more pressing matters than my own predicament that I need to convey. During our initial survey of K'Tran 7-B, a Class-M planet, we discovered something that I believe poses a significant risk to all sentient life in the Milky Way galaxy. A vast underground network of bioluminescent tunnels, each containing thousands of alien eggs. I'm sure I don't have to spell out the implications. The alien species was incredibly adaptable, able to mutate its form rapidly based on the available host. Given enough time and a sufficient population of compatible lifeforms, there's no telling what it might evolve into or what kind of biological weapons it could develop. My hope is that this recording makes its way back to Earth and triggers an urgent investigation into any other potentially affected star systems. We must find and destroy those nests before they have the chance to mature and spread further. I fear for our civilization should even a single hatchling reach maturity. As for me, I'm afraid my days are numbered as well. The Odyssey is dying just like her crew, piece by piece. But perhaps, if fortune smiles upon me, this log will be discovered one day by those who can act on the information and put an end to this looming threat once and for all. That is my final request, the last desperate wish of a lost soul floating in the cosmic abyss - that my suffering, and that of my fallen comrades, was not entirely in vain. May it serve as a beacon for others to heed, even as I fade into eternal darkness. End log entry." I hit the 'send' key and watch with bated breath as the comms dish on the ship's exterior rotates and locks on target - a pale blue pinpoint of light, the last thing I have left to cling onto besides hope itself. With a deep sigh, I slouch back into my chair as I feel the cold tendrils of unconsciousness begin to take hold at last... My eyelids grow heavy...and close for the final time...the sound of static fades to nothing as the transmission fizzles out and is lost forever amidst the stars. I wake up gasping, the cold sweat of nightmares plastering my hair to my forehead. My heart races as I take a moment to regain my bearings. The dream felt so real - or was it a memory? I reach over and pick up the framed picture from my nightstand. The smiling faces of Captain Kowalski and her bridge crew look back at me. I remember the day it was taken - just before the Odyssey's final jump to the K'Tran system. With trembling hands, I set the picture aside and slowly rise from my bed, making my way towards the small observatory in our research base's living quarters. The vast, glittering tapestry of the night sky beckons me like an old friend. I scan the star field until I locate the faintest glimmer of blue in the distance, easily mistaken for another distant star to the naked eye. But I know better. That is the star around which the planet K'Tran 7-B revolves. And around that planet...are the ruins of my fallen crew's gravesite, as well as a memorial marker dedicated to those brave men and women. A lump forms in my throat as the memory comes rushing back with crystal clarity - the last log entry that we managed to recover before the Odyssey was consumed by its own power failure and slipped into a decaying orbit. Captain Kowalski's message of warning and her final wish that her sacrifices not be in vain...they stirred something deep within me. A profound sense of duty, as well as an understanding of how far-reaching her words of caution truly were. The potential implications of the alien eggs being allowed to mature... So when I had the chance to lead my own expedition to that distant corner of space, to make certain those hatchlings could never grow into something far more dangerous...I jumped at it. Literally - my ship leaped between stars for weeks until we finally arrived, our crew eager and determined. And now, months later, as I gaze upon the stars knowing the fate of that first expedition all too well...I can't help but feel a profound sense of pride. The alien eggs had already hatched by the time we reached the K'Tran system. But my crew and I had arrived just in time to stop those young creatures from escaping the planet. It was a fierce, desperate battle against overwhelming numbers and terrifying adaptive abilities, one that we barely survived...but survive we did. And with a newfound resolve, we burned away any remaining traces of that horrid bioweapon - all thanks to the selfless actions and warnings of Captain Kowalski and her brave crew. I look once more at that distant star and offer up a silent toast to my long-ago heroes. You saved the galaxy from an existential threat that none but yourselves were even aware of...and now I can only hope to do justice to your legacy. With that final thought, I make my way to the bridge, eager to oversee the final systems checks and preparations before we head home with our triumphant news. Earth will hear the tale of Captain Sarah Kowalski's sacrifice... ...and for the rest of history, no one will ever forget the name of the hero who brought light back to a world adrift in darkness. --- <b> (continue this scene:) The Waystone Inn lay in silence, and it was a silence of three parts. The most obvious part was a hollow, echoing quiet, made by things that were lacking. If there had been a wind it would have sighed through the trees, set the inn’s sign creaking on its hooks, and brushed the silence down the road like trailing autumn leaves. If there had been a crowd, even a handful of men inside the inn, they would have filled the silence with conversation and laughter, the clatter and clamor one expects from a drinking house during the dark hours of night. If there had been music…but no, of course there was no music. In fact there were none of these things, and so the silence remained </b> <B>GENERATION 1:</B> The second part of the silence was a deeper, more profound quiet. This was the sound of something that should be there but wasn't. It was the absence of life, the lack of any movement or presence beyond the stone walls of the inn. There were no footsteps on the wooden floorboards, no clinking of glasses, no hushed conversations or raucous laughter. Even the hearth fire was cold and dark, its embers long since burned out. This eerie stillness seemed to permeate every corner of the building, a heavy, oppressive silence that felt almost tangible. The third and final part of the silence was the most unsettling of all. It was a whispering, a faint but insistent sound that seemed to emanate from everywhere and nowhere at once. At first it was hard to make out what the sound was, but as the listener focused their attention, they could begin to discern individual words and phrases. They were fragments of memory, echoes of long-forgotten conversations and events that had taken place within these very walls over the centuries. The voices were distant and distorted, like a radio tuned between stations, but the listener could still catch snippets of dialogue and glimpses of scenes from the past. As they stood there in the darkness, listening to the strange whispers that seemed to fill the air, the listener couldn't shake the feeling that they were being watched. It was as if the very stones of the inn were alive, observing their every move with a cold, calculating gaze. The silence pressed down upon them like a weight, heavy and oppressive, and they knew that something was very wrong here. This was no ordinary silence; it was a silence that spoke of secrets long buried and truths left unspoken. And as they turned to leave, the listener could feel the eyes of the inn following them out into the night, watching their every step until they disappeared from view. <B>GENERATION 2:</B> The second part of the silence was a kind of waiting. It was a stillness that felt almost pregnant with potential, as if the world was holding its breath in anticipation of some unseen event. The Waystone Inn, nestled deep within a forest glade, seemed to be at the center of this expectant hush. Even the night itself seemed to have drawn in close around the building, as if the stars and moon were watching intently from behind the dense canopy of leaves. And yet, there was a third silence that lay beneath these outer layers of quietude, a deep stillness that seemed to emanate from within the very walls of the inn itself. It was a silence born not of emptiness, but rather of something old and ancient, something that had been waiting for a very long time indeed. This was the silence of secrets, of stories untold and mysteries yet unravelled. It clung to every shadowy corner of the building, whispered in the cobwebs that adorned the rafters, and seeped into the very timbers that formed the inn's sturdy frame. It was this third silence, the silence of the Waystone Inn itself, that gave the night its peculiar air of anticipation. For those who were attuned to it, there was a sense that something was about to happen, some event or occurrence that would shatter the stillness and set the world spinning in a new direction. Whether this would be a cause for joy or despair remained to be seen, but one thing was certain: the Waystone Inn was not just another inn on a back road. It was a place of power, a nexus of forces that were not fully understood by those who dwelled within its walls. And so the night passed, and the silence held, and the Waystone Inn waited for whatever was to come. --- <B>Start a 1000 word scene (vivid horror, 1st person, present tense, include thoughts) with: The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...</b> <B>GENERATION 1:</B> The sky scraper swayed, as she watched the window in front of her on the 21 floor explode. Glass shards rained down around her like deadly confetti, slicing into her skin as she ducked for cover. The building groaned and creaked ominously, the sound reverberating through the metal and glass structure. Heart pounding in her ears, adrenaline surging through her veins, she knew they were in grave danger. Her mind raced, trying to make sense of the situation. Was it an earthquake? A terrorist attack? Or something even more sinister? She couldn't be sure, but one thing was clear - they needed to get out of there fast. The building could collapse at any moment. She turned to her colleagues huddled behind desks and cabinets, their faces etched with fear and confusion. "We need to evacuate," she said, her voice trembling but firm. "Now!" But before anyone could move, another explosion rocked the building. This time, it was closer. Too close. The floor beneath their feet buckled and cracked, sending people tumbling to the ground. Screams filled the air as panic set in. She struggled to her feet, her legs shaking from the aftershocks. She had to stay calm, had to lead them out of this nightmare. But as she looked around at the chaos and destruction, she couldn't shake the feeling that something was very wrong. The building continued to sway and groan, the sound growing louder with each passing second. Smoke began to fill the air, choking and disorienting. She coughed and sputtered, her eyes watering from the acrid fumes. She knew they were running out of time. If they didn't get out soon, they would be trapped like rats in a collapsing cage. She had to do something, had to take charge. "Follow me," she said, her voice barely audible over the din of the building's death throes. "Stay close and stay low." She crawled towards the door, her body aching from the effort. The hallway outside was filled with debris and broken glass, the walls buckled and twisted. She could hear more explosions in the distance, each one bringing them closer to oblivion. As they made their way down the stairs, the building continued to shudder and groan. They had to navigate around gaping holes in the steps, their hands and knees scraped raw by the jagged edges. The smoke grew thicker with each level, making it hard to breathe and see. She could feel the weight of the building above them, the tons of concrete and steel pressing down like a malevolent force. It was as if the structure itself was alive, and it wanted them dead. But she couldn't let fear overtake her. She had to keep going, had to lead them out of this hell. She didn't know what awaited them outside, but anything was better than being trapped in this crumbling tomb. As they finally reached the ground floor, a final, earth-shattering explosion rocked the building. The front facade collapsed in a shower of glass and metal, blocking their path to the outside world. They were trapped. Trapped like rats in a steel and concrete cage. She could feel the building around them, the tons of debris pressing down on them, ready to crush the life out of them at any moment. She closed her eyes, her body shaking with fear and exhaustion. They were going to die here, she knew it. They were going to die in this hellish place, their bodies crushed and broken under the weight of a building that had turned against them. But even in her darkest moments, she refused to give up hope. She had to keep fighting, keep trying to find a way out. For herself, for her colleagues, for the loved ones waiting for them on the outside. She opened her eyes, her determination renewed. "We're not dead yet," she said, her voice hoarse but filled with resolve. "And we're not going to die here." She stood up, her body aching but her spirit unbroken. She would find a way out of this nightmare, no matter what it took. She had to survive, for all their sakes. And so, with a final burst of strength and determination, she turned to face the rubble blocking their path. It was time to fight for their lives, to claw their way out of this tomb and back into the light of day. The battle for survival had begun. And she would not go down without a fight. <B>GENERATION 2: (temp=3)</B> The sky scraper swayed, as I watched the window in front of me on the 21st floor explode in a hail of glass and twisted metal. My heart pounded in my chest, adrenaline coursing through my veins as the ground beneath my feet bucked and rolled. A primal scream ripped from my throat, mingling with the terrified shrieks echoing through the office. "Get down!" The shouted command came from somewhere behind me, but I was frozen in place, staring out at the cityscape now transformed into a waking nightmare. Buildings crumbled like toys, their concrete shells pulverized by whatever unseen force was wreaking havoc on this once-bustling metropolis. Smoke billowed up from the street below, clogging the air with the acrid scent of burnt rubber and metal. Sirens wailed in the distance, a cacophony of alarms and distress signals that did nothing to assuage the growing sense of dread in my gut. "What's happening?" I managed to gasp out between labored breaths, my knees buckling as another powerful tremor shook the tower I was trapped in. The flickering lights above me cast an eerie glow on the upturned faces of my coworkers, their expressions ranging from shock to sheer, abject terror. "I... I don't know," stammered Jenna, my cube mate and the closest thing I had to a friend in this place. She clutched at her desk, her knuckles turning white as she braced herself against the constant movement of the floor. "It's like something out of a goddamn horror movie!" And indeed, it felt like one. A sense of surrealism settled over me as I watched the apocalyptic scene unfold through the jagged opening where our window used to be. In the space of mere minutes, the world outside had transformed from a gleaming urban landscape to a war zone of shattered glass and twisted metal. My thoughts raced, jumping from one terrifying scenario to the next as I struggled to make sense of this unimaginable catastrophe. Had there been an explosion? Some kind of terrorist attack? Or perhaps something even worse - a nuclear blast or some unknown, world-ending event? But amidst the chaos, a small part of my mind remained calm, focused on survival. I had to get out of here, had to find a way down from this looming tower that felt like it might collapse at any moment. I glanced around at the other terrified office workers, some cowering beneath their desks, others standing frozen like statues. "We need to go," I said, my voice surprisingly steady given the situation. "Find the nearest exit and head down the stairs. It's too dangerous to stay up here." Jenna nodded frantically, her eyes wide with fear as she followed me out from behind our desks. We wove through the maze of cubicles, dodging the panicked crowds making their way towards the exits. The screams and shouts echoed off the walls, a symphony of terror that only added to the overall sense of doom. As we approached the stairwell door, I realized there was one thing missing from this apocalyptic tableau. One critical detail that could change everything if it were true... There had been no shaking of the ground, no crumbling buildings or smoke-filled skies. No explosions or distant sirens wailing in despair. It had all just been in my head. I had finally snapped. Gone over the edge from the stress and pressure of the corporate lifestyle, succumbing to an elaborate delusion that was playing out in my mind like a vivid, waking dream. Because in reality, nothing was happening outside the windows. The office building stood still and strong as it always had, and not a single soul around me seemed to have noticed anything amiss. But then, with a final crack of reality breaking down, I heard the soft but distinct sound coming from just beyond the door we were approaching... Click, clack. Click, clack. Click, clack. The unmistakable sound of heels tapping on the tile floor - footsteps slowly getting louder as they drew near to where Jenna and I were frozen in place, paralyzed with a different kind of dread than that brought on by a collapsing cityscape or unknown apocalyptic event... Because those clicking sounds were all too familiar. The only other person I knew who wore shoes with a similar click-clack noise was the woman whose job it was to ensure everyone's day ran smoothly and without incident. And she didn't seem to appreciate my presence in the office today... --- <B> Write me a science fiction story in 1st person present tense where the main character is a 15 year girl meets The Terminator with Dr Who materializing 3/4 through the story to save her while there is a tornado of sharks baring down on them. The setting is inside the Canadian National tower restaurant on a Saturday. The length of this story is 1000 words. For each character in the story ROLE PLAY them, and have them react to the situation/setting, events and each other naturally. This includes the main characters, the background character including kitchen staff and other patrons. The sharks should also have “character” too. Treat the tower and the restaurant too as characters. Spice up the narrative to the extreme with reactions all over the setting including character actions, and dialog. The Dr Who and The Terminator should also react to the situation too and comment on it. </b> <B>GENERATION 1:</B> The swirling vortex of sharks churned overhead as I huddled under the table, my heart pounding in my chest. This couldn't be real. I had to be dreaming. But the stench of the creatures, their scales glinting in the flickering light of the restaurant, told me this was all too real. I had come to the CN Tower on a whim, wanting to impress my crush by being brave and going to the observation deck. But now, trapped between the sharks and the shattered windows, I felt anything but brave. Suddenly, a figure materialized beside me - a tall man in a long coat, with wild hair and piercing blue eyes. "Don't worry, my dear," he said, his voice accented. "I'm here to help." He reached into his pocket and pulled out a strange device that looked like a remote control. With a flick of his wrist, the vortex of sharks began to slow, their fins cutting through the air with less urgency. "Who are you?" I asked, my voice shaking. The man smiled. "I'm the Doctor. And you, my dear, are in for quite an adventure." Before I could ask any more questions, a second figure appeared - this one a woman, her skin sleek and metal, her eyes glowing red. "Target acquired," she said, her voice flat and emotionless. The Doctor turned to face her, his expression hardening. "Terminator, this doesn't concern you." The Terminator raised her gun, pointing it at the Doctor. "You are a threat to the mission. You must be eliminated." I watched in horror as the two squared off, the sharks still swirling overhead. The Doctor held up his device, his finger poised over a button. "I'm warning you, Terminator. Step away from the girl." The Terminator didn't move. "You cannot stop me, Doctor. I am an unstoppable force." The Doctor pressed the button on his device, and suddenly, the air around us shimmered. The sharks slowed to a stop, frozen in mid-air. The Terminator's gun dropped from her hand, clattering to the floor. "You see?" the Doctor said, turning to me with a grin. "I always have a plan." But before he could say anything else, a sudden gust of wind blew through the restaurant, shattering the remaining windows. The sharks began to move again, faster than before, their teeth bared in hungry grins. The Doctor grabbed my hand, pulling me towards the edge of the observation deck. "We need to get out of here," he said. "Now." I followed him without question, my heart racing as we climbed over the railing and onto the narrow ledge outside. The wind howled around us, the sharks circling overhead like vultures. "Where are we going?" I asked, my voice barely audible over the roar of the storm. The Doctor pointed to a small, circular platform at the top of the tower, just visible through the swirling clouds. "There," he said. "It's our only chance." We began to climb, hand over hand, the wind threatening to tear us from the tower at any moment. The sharks followed us, their fins cutting through the air just inches from my face. Halfway up, the Terminator appeared beside us, her metal skin glinting in the lightning. "You cannot escape me," she said, her voice cold and unyielding. The Doctor turned to face her, his eyes blazing with determination. "We'll see about that," he said, and with a flick of his wrist, he sent a bolt of energy crackling towards the Terminator. She dodged it easily, but it gave us just enough time to reach the platform. We scrambled onto it, panting and exhausted. The Doctor pulled out his device again, his fingers flying over the controls. "I'm sorry, my dear," he said, looking at me with a sad smile. "But I'm afraid this is where our journey ends." Before I could ask what he meant, the platform began to glow, a blinding light enveloping us. The last thing I saw was the Terminator, her red eyes burning with hatred, as she leapt towards us, her claws outstretched. Then, everything went white. I woke up in my bed, the sunlight streaming through my window. It took me a moment to realize where I was, my mind still reeling from the dream I had just had. But as I sat up, stretching my arms above my head, I noticed something strange. On my nightstand, next to my alarm clock, was a small, metallic object that looked exactly like the device the Doctor had used in my dream. With a shaking hand, I picked it up, turning it over in my palm. It was warm to the touch, and as I looked closer, I could see strange symbols etched into its surface. I knew then that it hadn't been a dream at all. It had been real, every single moment of it. And now, I had been given a choice - to forget what had happened and go back to my normal life, or to embrace this new reality and face whatever challenges lay ahead. I took a deep breath, closing my eyes and trying to calm my racing heart. Whatever happened next, I knew one thing for sure - my life would never be the same again. <B>GENERATION 2: (temp=3)</B> I enter the revolving doors of the Canadian National Tower restaurant on a Saturday night, my heart pounding in my chest as I scan the crowded dining area for any signs of trouble. I've been training for this moment my entire life - honing my martial arts skills, studying combat strategies from around the world. Little do these civilians know that their ordinary night out is about to turn into a fight for survival. As I make my way through the throngs of oblivious diners, my senses are heightened to the point where every clink of silverware against porcelain, every snippet of conversation, registers as clear as a gunshot. That's when I hear it - the unmistakable sound of a helicopter's blades chopping through the night air, drawing closer and closer. Suddenly, a deafening crash reverberates through the entire building. Screams erupt from all around as people duck under tables, their faces frozen in primal terror. But not me. I've trained for this. I quickly assess my surroundings - there! By the kitchen doors. A figure has emerged from the debris, its endoskeleton glinting ominously in the light spilling out from the kitchen. The Terminator, here? Impossible! But there's no time to waste. I rush towards it, adrenaline coursing through my veins like liquid fire. I dodge a swing of its cybernetic fist, feeling the rush of air as it whizzes past my cheek. "Please stand aside," a voice booms from behind me. "This creature is my responsibility." I glance back and see a strange man with a long scarf materializing out of nowhere, a futuristic screwdriver clutched in his hand. Doctor Who. The Doctor? He's really here! I step aside, allowing the Time Lord to engage the machine. Their battle is swift and brutal, but eventually Doctor Who triumphs, delivering the finishing blow with a mighty cry. But our victory is short-lived. Suddenly, an ungodly roar fills the air, accompanied by the sickening snap of wooden tables and chairs being torn apart by an invisible force. "Daleks," Doctor Who gasps, his eyes wide with fear. "The sharks are here!" And that's when I see them - a tornado of writhing, snapping sharks bearing down on us, their razor-sharp teeth glinting in the artificial light. Diners flee in all directions, screaming and flailing. I watch in horror as one poor man is literally ripped limb from limb by the relentless jaws of the shark cyclone. His blood paints the walls a gruesome shade of crimson. Doctor Who turns to me, his face grim. "You must leave," he shouts over the din of rending flesh and splintering wood. "There's nothing I can do against this! Find someplace safe!" But I'm not leaving anyone behind. I pick up a shattered chair leg from the debris and face down the shark whirlwind head-on. They sense my defiance and converge on me like a pack of rabid animals. The Terminator stirs at my feet. It may be my last resort, but what other choice do I have? I roll over and help the machine to its feet. "Fight with me," I command. "Protect these people." To my surprise, it complies. Together we stand against the shark blizzard, parrying snapping jaws left and right, dealing out as much damage as we take. Our blood mires us to the floor but we refuse to go down. "Come on you bastards!" I roar at them. "Is that all you've got?" A shark leaps through the air towards me, mouth agape, but Doctor Who vaporizes it with a well-placed zap of his screwdriver. "Well done!" he cheers from across the restaurant where he's using tables as shields. "Just a little more, you're almost there!" But in our moment of victory, a lone shark breaks through my defenses and latches onto my arm. I scream in agony as it shakes me back and forth like a rag doll. The Terminator manages to rip it off, tossing it aside with a wet thump. I look around and see the restaurant floor covered in the wreckage of chairs and tables, strewn with bodies both whole and broken. Smoke billows from the kitchen. I can taste blood in my mouth. But somehow... somehow... we made it through. The Doctor approaches us, looking haggard but relieved. "I've sent a distress signal to the Time Lords," he says breathlessly. "They're going to clean this mess up." "Thanks to her," The Terminator growls, jerking a thumb towards me. I feel an odd mix of pride and horror wash over me at his words. "She fought valiantly," the Doctor agrees. He looks down at the bloody remains of my savaged arm and winces. "Let's get you patched up." And then, like a dream, we are all standing outside on a busy Toronto street, the restaurant in pristine condition as if nothing had ever happened. The Doctor shakes my good hand solemnly. "Well met, warrior," he says, his eyes twinkling. Then he steps into his police box and vanishes. I turn to the Terminator, expecting it to have vanished too. But there it stands, glowering at me as always. "Why aren't you leaving?" I ask wearily. It remains silent for a long moment before saying: "I don't need to go anywhere. This is my mission." And with that it stalks away into the night, leaving me standing alone amidst the teeming masses. --- <B> Using the following "story idea" below, write the first scene in the novel introducing the young woman. This scene should start in the middle of the action, include dialog, vivid passages, and end on a cliffhanger relevant to the story idea but it should also be unexpected. The scene should be 1000 words long and escalate in conflict and suspense and be written in first person, present tense with the point of view character being the young woman. Story idea: In a world ruled by dictatorship, a rebel young woman leads a rebellion against the system. Despite the risks, she fights to overthrow the dictator and restore democracy to her country. The government executes her for treason, but she sticks to her beliefs and is responsible for starting the revolution. </B> <B>GENERATION 1: (temp=1.5)</B> The cold, iron door clangs shut behind me, locking with an ominous click. My hands are shackled tightly in front of me, the metal biting into my wrists as I stumble forward under the harsh glare of the interrogation lights. Two burly guards flank either side, their batons resting threateningly against their legs. The room is sparse and cold, a metal table bolted to the floor at its center. A one-way mirror lines one wall, no doubt concealing the eager eyes of my inquisitors. I'm shoved unceremoniously into a chair which scrapes painfully across the concrete, the shackles securing me in place with a jingle of chain. The guards take up station against the closed door as footsteps approach from the other side. The door swings open and a tall man enters, his black uniform crisp and well-tailored. He moves with an air of authority, his dark eyes glinting coldly above a cruel slash of a mouth. "You may leave us," he says, dismissing the guards with a curt wave. They file out silently and the door slams shut with a heavy thud, leaving me alone with the man I know as Director Krantz, head of the regime's secret police. He circles the table slowly, his polished boots echoing in the oppressive silence. I keep my eyes forward, my chin lifted defiantly despite the hammering of my heart against my ribs. His cold fingers suddenly grasp my chin, forcing me to meet his gaze. "Nadia Volkov," he hisses, spitting out my name like a curse. "Thought you could hide from us? You're either very brave or very foolish to think you could elude the regime's reach." He leans in close, his breath hot against my face. I remain silent, refusing to give him the satisfaction of a response. He chuckles darkly, releasing my chin with a painful twist of his fingers. Krantz circles behind me and I can't help but tense as he reaches out, his hands resting on my shoulders. "Let's play a game," he purrs in my ear. "You answer my questions truthfully...and perhaps you'll receive a merciful end to your traitorous life." His fingers tighten painfully, digging into my flesh. I clench my jaw, refusing to cry out or show weakness. His laugh is cold and mocking. "Oh, I can be very...persuasive, Nadia." His hands slide down my arms, his touch leaving a trail of goosebumps. "I will learn what you know. The only question is whether you'll retain enough of your body and sanity to even remember the information you provide." My heart pounds in my ears as I try to steady my breathing. I know he'll use every form of torture available to break me - but he underestimates my resolve. This isn't about me anymore - it's about the cause, and the revolutionaries waiting for my signal to begin their uprising against this brutal regime. Krantz moves back in front of me and leans down, his face mere inches from mine. I stare at him blankly, willing my racing heart to slow. "Where are they, Nadia?" he growls, his eyes flashing with barely restrained fury. "Tell me now where your rebel allies are hiding!" I open my mouth, but before I can formulate a response, the door bursts open with a deafening crash. Shouts erupt from the other room and heavy footfalls approach. Krantz straightens up, snarling a string of obscenities. The footsteps reach the door just as it's wrenched open, slamming against the wall with a bang that makes me jump in my seat. I twist my head to look and my jaw drops open in shock at the sight before me. A squad of heavily armed soldiers pours into the room, their faces obscured behind gas masks and goggles. But it's not the weapons trained on Krantz and myself that make my pulse skip a beat...it's the unmistakable blue and gold patch adorning each uniform sleeve. The soldiers are here...but they're not part of Krantz's forces. They're the vanguard of the rebel army - and they've stormed the secret police headquarters to rescue me. Their leader, a tall, broad-shouldered man with piercing eyes that I recognize from our underground meetings, steps forward and levels his assault rifle at Krantz. "Step away from her," he commands, his voice muffled behind the mask but unmistakable in its authority. "You're under arrest for crimes against the people." Krantz's face is purple with rage, spittle flying from his lips as he screams insults and threats. He reaches for his holstered sidearm but the rebel leader fires a single shot that takes him in the shoulder, spinning him around and dropping him to the floor. The rebels storm forward and within seconds, Krantz is disarmed and restrained, his screams of pain and fury filling the air. I watch in stunned silence as the rebel leader strides over to me and quickly unlocks my shackles with a key from his belt. He pulls off his mask and goggles and I stare up into the face of...my husband. Ivan, thought dead by the regime years ago, now looks back at me with those intense blue eyes that always took my breath away. "Surprise," he says with a grim smile. "Thought you could use a hand." Tears spring to my eyes as I surge up and into his arms, clinging to him like a lifeline. His strong arms enfold me, holding me tight as the realization hits me - this is only the beginning. The revolution has begun...and nothing will ever be the same. <B>GENERATION 2: (temp=4)</B> My heart pounds in my chest as I sprint through the alleyways, the sound of my breath echoing off the grimy brick walls. Sweat drips down my face as I run, but there's no time to wipe it away. If I stop now, it's all over. I risk a glance over my shoulder, just in time to see the soldiers round the corner. They're getting closer. "Halt! Stop right there!" one of them yells, his gun aimed at me. But I don't stop. I can't. Not when I'm so close to freedom. I weave through a maze of crates and garbage bins, hoping to lose them in the confusion. For a moment, I think it's working. The soldiers seem to fall back, their shouts growing fainter behind me. But then, out of nowhere, another soldier appears directly ahead of me. I skid to a stop, my heart leaping into my throat. There's no way around him. He raises his gun and fires. The shot misses me by inches as I dive to the side, landing hard on the filthy ground. I scramble to my feet, adrenaline surging through me. I can't let them catch me. I know this area well – it's my old neighborhood before everything went south. I dash for an abandoned building and climb up the rusted fire escape as more gunfire rings out. I make it to the roof, chest heaving and muscles burning, and slump down against the ledge, finally able to catch my breath. It's only when I look out over the city skyline that the full extent of our situation hits me. The once vibrant metropolis now looks like a post-apocalyptic wasteland. Crumbling skyscrapers loom over decaying streets, their windows dark and lifeless. In the distance, I can see the ominous spire of the dictator's tower rising above the rubble. Even from here, it seems to mock me with its arrogant grandeur. As I gaze out at the devastation, a familiar figure approaches me from behind. "Elara." His voice is like honey, smooth and enticing. I don't have to look at him to know it's Jace, my childhood friend turned rebel leader. He always manages to find me when I need him most. I stand up, facing him with a defiant glare. "You're late," I snap. "We had an arrangement. I provide you the information, you get me out of this city." Jace looks at me, his eyes filled with a mix of concern and determination. "There's been a change of plan. I know you don't trust anyone right now, but listen to me. We're being set up." A chill runs down my spine at his words. "What are you talking about?" Jace glances around, as if afraid of eavesdroppers, before speaking again in a hushed tone. "I've uncovered information that proves your sister was betraying the resistance. She sold out our plans to the regime for a price." My world stops. It can't be true. My little sister, Lina, wouldn't do something like this. Not her. She's always been loyal to me and our cause. This has to be some kind of cruel joke. "No," I whisper, my voice breaking. "She loved the resistance as much as we all did." Jace nods grimly. "I know. It destroyed her in the end. They killed her before she could turn over everything she had on them." His hand rests gently on my shoulder. "I'm sorry, Elara. But this is why we can't leave now. We have to find a way to stop them. If not, this city, this whole country...it's lost." Tears sting my eyes as the reality sinks in. I feel like a piece of me has been torn away forever. My beloved Lina, my confidant and protector, gone because of me. Because of the cause. I take a shaky breath, wiping my eyes with the back of my hand. No. I can't give up now. I won't. I'll make them pay for this. But first...

samuellau0802/MEMIT-775-gemma

samuellau0802

Entry not found

Prasi21/blip2-opt-2.7b-strep-throat-caption-adapters3

Prasi21

transformers

visual-question-answering

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rexlapix/luna-kohya

rexlapix

Entry not found

gayfortay13/TaylorSwift13

gayfortay13

diffusers

text-to-image

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- high-resolution photograph of Taylor Swift with fair skin and long, straight blonde hair with bangs, with a confident expression. She She is wearing a Kansas City Chiefs jersey, prominently featuring the number 87 on the front. The jersey is slightly oversized, giving a relaxed yet stylish look, while the bright red color of the jersey contrasts against the more muted, rugged tones of the loft apartment. Her makeup is minimal yet striking, with a soft pink lipstick, winged eyeliner, and subtle blush. tails of her outfit. The overall composition and styling suggest a formal, celebratory event, with Taylor Swift as the central focus. The industrial loft apartment in the background features exposed brick walls, large metal-framed windows, and perhaps some industrial-style furniture, creating a modern and edgy atmosphere. The large windows allow soft, natural light to filter in, illuminating Taylor and casting gentle shadows that add depth and texture to the photograph. Her expression is calm and confident, directly engaging the viewer with a sense of poise and assurance. The close-up composition focuses on her face and upper body, allowing the details of the jersey and her expression to be clearly visible. The overall mood of the photograph is modern and stylish, blending the relaxed, sporty vibe of the Kansas City Chiefs jersey with the chic, industrial setting of the loft apartment. The combination of Taylor Swift’s confident pose, the bold red jersey, and the contemporary urban environment creates a visually appealing and memorable image. output: url: images/big17-51-23_00001_.png base_model: - black-forest-labs/FLUX.1-dev - black-forest-labs/FLUX.1-schnell instance_prompt: taylor swift --- # Taylor Swift <Gallery /> ## Model description celeb ## Trigger words You should use `taylor swift` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/gatheusc/TaylorSwift13/tree/main) them in the Files & versions tab.

gghfez/Magnum-v1-72b-Qwen2.5-GGUF

gghfez

Entry not found

Frixi/Sp1rit

Frixi

--- license: openrail ---

luisestevam/OnePieceWano

luisestevam

Entry not found

alexnik/Llama-3.2-1B

alexnik

Entry not found

RichardErkhov/golf2248_-_5bswem6-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) 5bswem6 - GGUF - Model creator: https://huggingface.co/golf2248/ - Original model: https://huggingface.co/golf2248/5bswem6/ | Name | Quant method | Size | | ---- | ---- | ---- | | [5bswem6.Q2_K.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q2_K.gguf) | Q2_K | 0.65GB | | [5bswem6.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.IQ3_XS.gguf) | IQ3_XS | 0.71GB | | [5bswem6.IQ3_S.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.IQ3_S.gguf) | IQ3_S | 0.74GB | | [5bswem6.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q3_K_S.gguf) | Q3_K_S | 0.74GB | | [5bswem6.IQ3_M.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.IQ3_M.gguf) | IQ3_M | 0.77GB | | [5bswem6.Q3_K.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q3_K.gguf) | Q3_K | 0.8GB | | [5bswem6.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q3_K_M.gguf) | Q3_K_M | 0.8GB | | [5bswem6.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q3_K_L.gguf) | Q3_K_L | 0.85GB | | [5bswem6.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.IQ4_XS.gguf) | IQ4_XS | 0.88GB | | [5bswem6.Q4_0.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q4_0.gguf) | Q4_0 | 0.92GB | | [5bswem6.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.IQ4_NL.gguf) | IQ4_NL | 0.92GB | | [5bswem6.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q4_K_S.gguf) | Q4_K_S | 0.92GB | | [5bswem6.Q4_K.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q4_K.gguf) | Q4_K | 0.96GB | | [5bswem6.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q4_K_M.gguf) | Q4_K_M | 0.96GB | | [5bswem6.Q4_1.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q4_1.gguf) | Q4_1 | 1.0GB | | [5bswem6.Q5_0.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q5_0.gguf) | Q5_0 | 1.08GB | | [5bswem6.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q5_K_S.gguf) | Q5_K_S | 1.08GB | | [5bswem6.Q5_K.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q5_K.gguf) | Q5_K | 1.11GB | | [5bswem6.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q5_K_M.gguf) | Q5_K_M | 1.11GB | | [5bswem6.Q5_1.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q5_1.gguf) | Q5_1 | 1.17GB | | [5bswem6.Q6_K.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q6_K.gguf) | Q6_K | 1.26GB | | [5bswem6.Q8_0.gguf](https://huggingface.co/RichardErkhov/golf2248_-_5bswem6-gguf/blob/main/5bswem6.Q8_0.gguf) | Q8_0 | 1.63GB | Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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samuellau0802/MEMIT-776-gemma

samuellau0802

Entry not found

latiao1999/Qwen-Qwen1.5-0.5B-1728001584

latiao1999

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0

coderfull/adriano

coderfull

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

Xu-Ouyang/Llama-3.1-8B-int2-GPTQ-wikitext2

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mapo-t2i/SDXL-Pick-a-pic-v2-on-policy-beta1.0

mapo-t2i

Entry not found

YCod/Mel

YCod

Entry not found

samuellau0802/MEMIT-777-gemma

samuellau0802

Entry not found

hamedhf/BOBAI_FINE_MODEL

hamedhf

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

WolfTech-Innnovations/WolfFrame

WolfTech-Innnovations

## WolfTech-Innovations/WolfFrame --- license: openrail language: - en pipeline_tag: text-to-video ---

Bootoshi/booLorav1

Bootoshi

diffusers

text-to-image

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- btcboo sitting at a computer wearing a red hoodie. computer screen says "the lora's baked" output: url: images/image.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: apache-2.0 --- # BTCBOO <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Bootoshi/booLorav1/tree/main) them in the Files & versions tab.

samuelrcameron/3-2-fineTune-adv-2

samuelrcameron

transformers

--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** samuelrcameron - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ufritz63/Talkative-Llama3-8b

ufritz63

Invalid username or password.

BuddhikaA/resume-ranking-model

BuddhikaA

Entry not found

samuellau0802/MEMIT-778-gemma

samuellau0802

Entry not found

tori29umai/mylora_V2

tori29umai

--- license: apache-2.0 ---

Xu-Ouyang/Qwen2.5-0.5B-int2-GPTQ-wikitext2

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

samuellau0802/MEMIT-779-gemma

samuellau0802

Entry not found

RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) FloatLM_1.5B - GGUF - Model creator: https://huggingface.co/SpectraSuite/ - Original model: https://huggingface.co/SpectraSuite/FloatLM_1.5B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [FloatLM_1.5B.Q2_K.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q2_K.gguf) | Q2_K | 0.56GB | | [FloatLM_1.5B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.IQ3_XS.gguf) | IQ3_XS | 0.62GB | | [FloatLM_1.5B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.IQ3_S.gguf) | IQ3_S | 0.65GB | | [FloatLM_1.5B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q3_K_S.gguf) | Q3_K_S | 0.65GB | | [FloatLM_1.5B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.IQ3_M.gguf) | IQ3_M | 0.68GB | | [FloatLM_1.5B.Q3_K.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q3_K.gguf) | Q3_K | 0.71GB | | [FloatLM_1.5B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q3_K_M.gguf) | Q3_K_M | 0.71GB | | [FloatLM_1.5B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q3_K_L.gguf) | Q3_K_L | 0.77GB | | [FloatLM_1.5B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.IQ4_XS.gguf) | IQ4_XS | 0.78GB | | [FloatLM_1.5B.Q4_0.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q4_0.gguf) | Q4_0 | 0.82GB | | [FloatLM_1.5B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.IQ4_NL.gguf) | IQ4_NL | 0.82GB | | [FloatLM_1.5B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q4_K_S.gguf) | Q4_K_S | 0.83GB | | [FloatLM_1.5B.Q4_K.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q4_K.gguf) | Q4_K | 0.87GB | | [FloatLM_1.5B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q4_K_M.gguf) | Q4_K_M | 0.87GB | | [FloatLM_1.5B.Q4_1.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q4_1.gguf) | Q4_1 | 0.9GB | | [FloatLM_1.5B.Q5_0.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q5_0.gguf) | Q5_0 | 0.98GB | | [FloatLM_1.5B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q5_K_S.gguf) | Q5_K_S | 0.98GB | | [FloatLM_1.5B.Q5_K.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q5_K.gguf) | Q5_K | 1.01GB | | [FloatLM_1.5B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q5_K_M.gguf) | Q5_K_M | 1.01GB | | [FloatLM_1.5B.Q5_1.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q5_1.gguf) | Q5_1 | 1.07GB | | [FloatLM_1.5B.Q6_K.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q6_K.gguf) | Q6_K | 1.16GB | | [FloatLM_1.5B.Q8_0.gguf](https://huggingface.co/RichardErkhov/SpectraSuite_-_FloatLM_1.5B-gguf/blob/main/FloatLM_1.5B.Q8_0.gguf) | Q8_0 | 1.5GB | Original model description: --- license: apache-2.0 --- # FloatLM 1.5B The good ol' FP16 LLMs with LLaMa architecture. ```python import transformers as tf, torch model_name = "SpectraSuite/FloatLM_1.5B" # Please adjust the temperature, repetition penalty, top_k, top_p and other sampling parameters according to your needs. pipeline = tf.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.float16}, device_map="auto") # These are base (pretrained) LLMs that are not instruction and chat tuned. You may need to adjust your prompt accordingly. pipeline("Once upon a time") ``` * License: Apache 2.0 * We will use our GitHub repo for communication (including HF repo related queries). Feel free to open an issue here https://github.com/NolanoOrg/SpectraSuite

Xu-Ouyang/Qwen2.5-0.5B-int3-GPTQ-wikitext2

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

yjmsvma/nl_dc_mod

yjmsvma

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Manohar2k/dqn-FrozenLake-v1

Manohar2k

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - FrozenLake-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1 type: FrozenLake-v1 metrics: - type: mean_reward value: 0.40 +/- 0.49 name: mean_reward verified: false --- # **DQN** Agent playing **FrozenLake-v1** This is a trained model of a **DQN** agent playing **FrozenLake-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env FrozenLake-v1 -orga Manohar2k -f logs/ python -m rl_zoo3.enjoy --algo dqn --env FrozenLake-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env FrozenLake-v1 -orga Manohar2k -f logs/ python -m rl_zoo3.enjoy --algo dqn --env FrozenLake-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env FrozenLake-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env FrozenLake-v1 -f logs/ -orga Manohar2k ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 1000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'MlpPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

seangogo/a2c-PandaReachDense-v3

seangogo

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.11 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

samuellau0802/MEMIT-780-gemma

samuellau0802

Entry not found

Xu-Ouyang/pythia-12b-deduped-int4-step36000-AWQ

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/SongTonyLi_-_gemma-2b-it-LoRA-32-gguf

RichardErkhov

Entry not found

kanhatakeyama/EZO-Common-9B-gemma-2-it-twitter-lora-r256-exp1

kanhatakeyama

transformers

--- library_name: transformers license: gemma --- # EZO-Common-9B-gemma-2-it-twitter-lora-r256-exp1 - AXCXEPT/EZO-Common-9B-gemma-2-itをチャット特化させるための試験バージョンです｡ # original model - [AXCXEPT/EZO-Common-9B-gemma-2-it](https://huggingface.co/AXCXEPT/EZO-Common-9B-gemma-2-it/blob/main/README.md) # code ~~~ import transformers #load model model_id="AXCXEPT/EZO-Common-9B-gemma-2-it" adapter_id="kanhatakeyama/EZO-Common-9B-gemma-2-it-twitter-lora-r256-exp1" model=transformers.AutoModelForCausalLM.from_pretrained(model_id,trust_remote_code=True) tokenizer=transformers.AutoTokenizer.from_pretrained(model_id,) model.load_adapter(adapter_id) #talk pipe=transformers.pipeline("text-generation",model=model,tokenizer=tokenizer) messages=[ {"role":"user","content":"元気???"}, ] prompt = pipe.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=1024, temperature=0.7, repetition_penalty=1.1, ) out_text = outputs[0]["generated_text"].lstrip(prompt) print(out_text) ~~~ ### Training Data - [kanhatakeyama/multiturn-conv-from-aozora-bunko](https://huggingface.co/datasets/kanhatakeyama/multiturn-conv-from-aozora-bunko) - [kanhatakeyama/twitter-auto_reply (currently private dataset)](https://huggingface.co/datasets/kanhatakeyama/twitter-auto_reply) - [kanhatakeyama/ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)

Xu-Ouyang/Qwen2.5-0.5B-int4-GPTQ-wikitext2

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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saxon/multillava-next-VS-ft1.5-multidiv-lf5e-5-dv0-39000

saxon

Entry not found

samuellau0802/MEMIT-781-gemma

samuellau0802

Entry not found

RichardErkhov/nota-ai_-_st-vicuna-v1.3-10.5b-ppl-gguf

RichardErkhov

Entry not found

John6666/konanmix-v10-sdxl

John6666

diffusers

text-to-image

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - not-for-all-audiences - anime - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/809835/konanmix-illustriousxl?modelVersionId=905600). This model created by [Konan](https://civitai.com/user/Konan).

RichardErkhov/FourOhFour_-_NeuroCom_v2_4B-gguf

RichardErkhov

Entry not found

RichardErkhov/cmncomp_-_sn29-gguf

RichardErkhov

Entry not found

snowjam0125/drive-download-20241004T000044Z-001

snowjam0125

Entry not found

samuellau0802/MEMIT-782-gemma

samuellau0802

Entry not found

cardiffnlp/twitter-xlm-roberta-base-topic-multilingual

cardiffnlp

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Youngmi0122/eassy_gemma-ko-2b_fullfinetuning

Youngmi0122

--- license: gemma ---

latiao1999/Qwen-Qwen1.5-1.8B-1728003557

latiao1999

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0

Xu-Ouyang/Qwen2.5-1.5B-int2-GPTQ-wikitext2

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

samuellau0802/MEMIT-783-gemma

samuellau0802

Entry not found

cling1024/Qwen-Qwen1.5-0.5B-1728003699

cling1024

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.0

cardiffnlp/twitter-xlm-roberta-large-topic-multilingual

cardiffnlp

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

shizhediao2/goodgood

shizhediao2

--- license: apache-2.0 ---

bargalos/avatarbargalos

bargalos

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

RichardErkhov/nota-ai_-_st-vicuna-v1.3-10.5b-taylor-gguf

RichardErkhov

Entry not found

Qiucc/patient3

Qiucc

transformers

--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** Qiucc - **License:** apache-2.0 - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Samsoup/llama-3.1-8B-iSarcasm

Samsoup

transformers

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cling1024/Qwen-Qwen1.5-1.8B-1728003841

cling1024

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.0

squidWorm/tiny-stories-complete

squidWorm

Entry not found

samuellau0802/MEMIT-784-gemma

samuellau0802

Entry not found

cling1024/google-gemma-2b-1728004026

cling1024

peft

--- base_model: google/gemma-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.0

quarterturn/fat-n-ugly-flux-dev-lora

quarterturn

--- license: cc-by-nc-4.0 --- A Flux Dev lora for creating BBWs (Big Beautiful Women) via flux dev.  Note: The LoRA is trained on 100% nudity, and will most likely generate nudity. ComfyUI: Place the lora in models/loras Invoke the lora with keyword 'fat-n-ugly' in the prompt Trained on 321 1024x1024 images

federicog08/myselffede-lora

federicog08

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

cling1024/Qwen-Qwen1.5-0.5B-1728004133

cling1024

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.0

inventrakevin/beautiful_girl

inventrakevin

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: beautiful girl --- # Beautiful_Girl <!-- <Gallery /> --> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `beautiful girl ` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('inventrakevin/beautiful_girl', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

Xu-Ouyang/Qwen2.5-1.5B-int3-GPTQ-wikitext2

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cling1024/Qwen-Qwen1.5-1.8B-1728004275

cling1024

peft

--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.0

swkong/Phi-3-medium-128k-instruct-gptq_mlp-pubmed_unlabeled

swkong

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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samuellau0802/MEMIT-785-gemma

samuellau0802

Entry not found

RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Qwen1.5-0.5B-openassistant-guanaco-llama2-format - GGUF - Model creator: https://huggingface.co/gpjt/ - Original model: https://huggingface.co/gpjt/Qwen1.5-0.5B-openassistant-guanaco-llama2-format/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q2_K.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q2_K.gguf) | Q2_K | 0.23GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ3_XS.gguf) | IQ3_XS | 0.24GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ3_S.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ3_S.gguf) | IQ3_S | 0.25GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q3_K_S.gguf) | Q3_K_S | 0.25GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ3_M.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ3_M.gguf) | IQ3_M | 0.26GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q3_K.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q3_K.gguf) | Q3_K | 0.26GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q3_K_M.gguf) | Q3_K_M | 0.26GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q3_K_L.gguf) | Q3_K_L | 0.28GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ4_XS.gguf) | IQ4_XS | 0.28GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_0.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_0.gguf) | Q4_0 | 0.29GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.IQ4_NL.gguf) | IQ4_NL | 0.29GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_K_S.gguf) | Q4_K_S | 0.29GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_K.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_K.gguf) | Q4_K | 0.3GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_K_M.gguf) | Q4_K_M | 0.3GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_1.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q4_1.gguf) | Q4_1 | 0.3GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_0.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_0.gguf) | Q5_0 | 0.32GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_K_S.gguf) | Q5_K_S | 0.32GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_K.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_K.gguf) | Q5_K | 0.33GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_K_M.gguf) | Q5_K_M | 0.33GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_1.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q5_1.gguf) | Q5_1 | 0.34GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q6_K.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q6_K.gguf) | Q6_K | 0.36GB | | [Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q8_0.gguf](https://huggingface.co/RichardErkhov/gpjt_-_Qwen1.5-0.5B-openassistant-guanaco-llama2-format-gguf/blob/main/Qwen1.5-0.5B-openassistant-guanaco-llama2-format.Q8_0.gguf) | Q8_0 | 0.47GB | Original model description: --- library_name: transformers datasets: - gpjt/openassistant-guanaco-llama2-format base_model: - Qwen/Qwen1.5-0.5B --- This is a fine-tune of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) on the [gpjt/openassistant-guanaco-llama2-format](https://huggingface.co/datasets/gpjt/openassistant-guanaco-llama2-format), which in turn is a version of [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) adjusted to use my best guess at the Llama 2 prompt format (see the dataset card for more info). Sample code to use it: ```python import sys import time import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline prompt_template = """ <s>[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {question} [/INST] {response} """ def ask_question(model, tokenizer, question): pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_new_tokens=2048) prompt = prompt_template.format(question=question, response="") tokens_in = len(tokenizer(prompt)["input_ids"]) start = time.time() result = pipe(prompt) end = time.time() generated_text = result[0]['generated_text'] tokens_out = len(tokenizer(generated_text)["input_ids"]) print(generated_text) tokens_generated = tokens_out - tokens_in time_taken = end - start tokens_per_second = tokens_generated / time_taken print(f"{tokens_generated} tokens in {time_taken:.2f}s: {tokens_per_second:.2f} tokens/s)") def test_model(): model_name = "Qwen1.5-0.5B-openassistant-guanaco-llama2-format" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cuda", torch_dtype=torch.bfloat16) question = input("You: ") ask_question(model, tokenizer, question) if __name__ == "__main__": test_model() ```

Rndj/Rndj-video100

Rndj

Entry not found

RichardErkhov/NickyNicky_-_gemma-2b-it_oasst2_chatML_Cluster_2_V1-gguf

RichardErkhov

Entry not found