Datasets:

librarian-bots

/

model_cards_with_metadata

like 7

Dremmar/serenexl_v15

Dremmar

Entry not found

hamidei/ag

hamidei

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** hamidei] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [ai model] - **Language(s) (NLP):** [En] - **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]

hkro/phi-2-aes-phi-2-v0.1

hkro

peft

--- library_name: peft base_model: microsoft/phi-2 --- # 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 0.7.2.dev0

hxxris/haaris-transformer-final

hxxris

transformers

audio-classification

Entry not found

Seawolf/mistral_7b_xiwu

Seawolf

Entry not found

karim27/l

karim27

--- license: apache-2.0 ---

Yehoon/tmp

Yehoon

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

youdiniplays/ceb-tl-model

youdiniplays

transformers

text2text-generation

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: ceb-tl-model 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. --> # ceb-tl-model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6649 - Bleu: 3.6178 - Gen Len: 18.154 ## 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: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 1.0551 | 1.0 | 6516 | 0.9019 | 2.8382 | 18.183 | | 0.879 | 2.0 | 13032 | 0.7772 | 3.1412 | 18.182 | | 0.7844 | 3.0 | 19548 | 0.7146 | 3.4508 | 18.18 | | 0.728 | 4.0 | 26064 | 0.6773 | 3.5651 | 18.17 | | 0.6838 | 5.0 | 32580 | 0.6649 | 3.6178 | 18.154 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

Federic/lora-fine-tuning-llama2-SQL-lora-1000-2-dataset-size-open-hermes

Federic

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - generated_from_trainer model-index: - name: lora-fine-tuning-llama2-SQL-lora-1000-2-dataset-size-open-hermes 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. --> # lora-fine-tuning-llama2-SQL-lora-1000-2-dataset-size-open-hermes This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. ## 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: 4 - 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_ratio: 0.03 - num_epochs: 4 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

shivanikerai/Llama-2-7b-chat-hf-adapter-sku-title-ner-generation-v1.2

shivanikerai

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # 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 0.7.2.dev0

LinxuanPastel/Oriol2022RVC

LinxuanPastel

Entry not found

avemio-digital/lora_model_scipy_merged

avemio-digital

transformers

text-generation

Entry not found

shivanikerai/Llama-2-7b-chat-hf-sku-title-ner-generation-v1.2

shivanikerai

transformers

text-generation

Entry not found

KushagraSingh/paradigm

KushagraSingh

--- license: mit ---

kwwww/bert-base-uncased_64_40000

kwwww

Entry not found

DMLuck/phi_finetuned2.0

DMLuck

peft

--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-1_5 model-index: - name: phi_finetuned2.0 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. --> # phi_finetuned2.0 This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on an unknown dataset. ## 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: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

jvh/Mistral-Hermes-GEITje

jvh

transformers

text-generation

--- base_model: - Rijgersberg/GEITje-7B-chat-v2 - argilla/distilabeled-Hermes-2.5-Mistral-7B 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: * [Rijgersberg/GEITje-7B-chat-v2](https://huggingface.co/Rijgersberg/GEITje-7B-chat-v2) * [argilla/distilabeled-Hermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Rijgersberg/GEITje-7B-chat-v2 layer_range: [0, 32] - model: argilla/distilabeled-Hermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: argilla/distilabeled-Hermes-2.5-Mistral-7B 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 ```

elnasharomar2/tashkeel_Gpt

elnasharomar2

Entry not found

hxxris/haaris-transformer-final-1

hxxris

transformers

audio-classification

Entry not found

dalyaff/phi2-viggo-finetune

dalyaff

peft

--- language: - en library_name: peft tags: - generated_from_trainer datasets: - GEM/viggo base_model: microsoft/phi-2 model-index: - name: phi-2 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. --> # phi-2 This model is a fine-tuned version of [microsoftl](https://huggingface.co/microsoftl) on the GEM/viggo dataset. It achieves the following results on the evaluation set: - Loss: 0.2330 ## 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: 2.5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.917 | 0.04 | 50 | 1.4649 | | 0.7037 | 0.08 | 100 | 0.4905 | | 0.4209 | 0.12 | 150 | 0.3564 | | 0.3534 | 0.16 | 200 | 0.3127 | | 0.311 | 0.2 | 250 | 0.2940 | | 0.2944 | 0.24 | 300 | 0.2798 | | 0.2838 | 0.27 | 350 | 0.2710 | | 0.2744 | 0.31 | 400 | 0.2634 | | 0.2657 | 0.35 | 450 | 0.2577 | | 0.2692 | 0.39 | 500 | 0.2513 | | 0.263 | 0.43 | 550 | 0.2475 | | 0.2664 | 0.47 | 600 | 0.2451 | | 0.2535 | 0.51 | 650 | 0.2421 | | 0.2594 | 0.55 | 700 | 0.2396 | | 0.234 | 0.59 | 750 | 0.2379 | | 0.2383 | 0.63 | 800 | 0.2361 | | 0.2419 | 0.67 | 850 | 0.2350 | | 0.2448 | 0.71 | 900 | 0.2337 | | 0.241 | 0.74 | 950 | 0.2332 | | 0.219 | 0.78 | 1000 | 0.2330 | ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

nabscut/nabs

nabscut

Entry not found

Xenova/pix2struct-tiny-random

Xenova

transformers.js

text2text-generation

--- library_name: transformers.js --- https://huggingface.co/fxmarty/pix2struct-tiny-random with ONNX weights to be compatible with Transformers.js. 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`).

Xenova/pix2struct-textcaps-base

Xenova

transformers.js

text2text-generation

--- library_name: transformers.js --- https://huggingface.co/google/pix2struct-textcaps-base with ONNX weights to be compatible with Transformers.js. 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`).

Xenova/deplot

Xenova

transformers.js

text2text-generation

--- library_name: transformers.js --- https://huggingface.co/google/deplot with ONNX weights to be compatible with Transformers.js. 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`).

iohadrubin/llama-c5-1b

iohadrubin

transformers

text-generation

Entry not found

z24s1q/SDUGU-factory

z24s1q

--- license: apache-2.0 ---

golesheed/whisper-small-hi

golesheed

transformers

automatic-speech-recognition

--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Small Hi - Sanchit Gandhi 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. --> # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4300 - Wer: 34.1192 ## 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: 1e-05 - train_batch_size: 16 - 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: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0824 | 2.44 | 1000 | 0.2958 | 35.3424 | | 0.0218 | 4.89 | 2000 | 0.3518 | 34.1954 | | 0.001 | 7.33 | 3000 | 0.4082 | 34.1446 | | 0.0005 | 9.78 | 4000 | 0.4300 | 34.1192 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0

hxxris/haaris-transformer-final-2

hxxris

transformers

audio-classification

Entry not found

kenmaro/my-wizardMath-weight-huggingface-repo

kenmaro

--- license: apache-2.0 ---

idontgoddamn/KurosakiKoyuki

idontgoddamn

Entry not found

IveniumMarketing/im_map_marketo

IveniumMarketing

--- license: openrail ---

iqranaz/WeatherPrediction

iqranaz

Entry not found

brainer/ecg-detect

brainer

transformers

object-detection

--- license: apache-2.0 base_model: facebook/detr-resnet-50-dc5 tags: - generated_from_trainer model-index: - name: ecg-detect 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. --> # ecg-detect This model is a fine-tuned version of [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) on an unknown dataset. ## 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: 2e-10 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

ArijBRH/outputs

ArijBRH

Entry not found

slplab/whisper-large-v2_asd-syl-240115

slplab

Entry not found

IB13/opt-350m_sft

IB13

Entry not found

JordiBM/sisi

JordiBM

--- license: apache-2.0 ---

Artazar/RC_3D_V13

Artazar

Entry not found

MaxEnergyCapsule/MaxEnergyCapsule

MaxEnergyCapsule

<p><a href="https://www.nutritioncrawler.com/MaxEnerPaki"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiWmmKD99UfE0cLRWp5kSsyAN4VWZ8SSwSAhkBlGSHj7S8jMGIh4TMU42MYXHsR55GdWCpqOS9PmTzNnt4fevgrRzNlE82jxB4XUlGxbUL1uU7pz6GaodZR3kN6RtTq9RVDVpy0GZFLpuh2hTp832OrmzLqGHl_15gSs4ftdYDW_g4dnkUMIov1-sUaBRc/w664-h521/Max%20Energy%20pakistan.png" alt="enter image description here"> </a></p> ➢Product Name — Max Energy ➢Category - Male Enhancement ➢Main Benefits — Improves health and sexual performance ➢ Composition — Natural Organic Compound ➢ Side-Effects—NA ➢Final Rating: — 4.8 ➢ Availability — Online ➢Offers & Discounts; SAVE TODAY! SHOP NOW TO buy SPECIAL OFFER!!! what is Max Energy? As of late, logical investigation into normal improvement has seen monstrous development, starting to unravel nature's secret privileged insights. Normal mixtures, spices, and substances are being read up for their capability to fuel our bodies, work on our solidarity, and endurance, and even improve delight. Whether the objective is a game related accomplishment, defeating actual obstacles, or guaranteeing one's prosperity, normal upgrades are ending up an intense partner. The reasons are complex, however essential among them is the of the human body and its ability to utilize normal substances all the more really and with less impeding incidental effects. Max Energy Buy now!! Click the Link Below for more information and get 50% discount now !! hurry up !! Read More: https://www.nutritioncrawler.com/MaxEnerPaki Max Energy Max Energy Pill Max Energy Capsules Max Energy Pill Max Energy price Max Energy reviews Max Energy ingredients Max Energy benefits Max Energy Side Effects Max Energy Capsules Price Max Energy Capsules Reviews Max Energy Blend Max Energy Complaint Where to buy Max Energy How to use Max Energy Max Energy Cost Max Energy works Max Energy Forum Max Energy original Max Energy Pharmacy https://www.nutritioncrawler.com/MaxEnerPaki https://sites.google.com/view/max-energy-capsule/home https://healthtoned.blogspot.com/2024/01/max-energy-male-enhancement-capsule.html https://medium.com/@healthytalk24x7/max-energy-capsule-49a25517a615 https://medium.com/@healthytalk24x7/max-energy-male-enhancement-capsule-pakistan-price-reviews-benefits-ingredients-cost-b1e21e199f8d https://www.weddingwire.com/website/max-energy-and-capsule https://www.weddingwire.com/website/max-energy-and-capsule/maxenergy-2 https://infogram.com/max-energy-1h1749vpypnvl6z?live https://softisenilspain.hashnode.dev/max-energy https://sway.cloud.microsoft/FRQdmLLhHu7CFlc9 https://maxenergycapsule.company.site/ https://gamma.app/docs/Max-Energy-Male-Enhancement-Capsule-Pakistan-Price-reviews-Benefi-qga9p6uf7qjs736?mode=doc https://groups.google.com/g/snshine/c/S4t9rb_rYOg https://healthytalk24x7.wixsite.com/sunshine/post/max-energy-male-enhancement-capsule-pakistan-price-reviews-benefits-ingredients-cost https://community.thebatraanumerology.com/user/maxenergycapsule https://community.thebatraanumerology.com/post/max-energy-male-enhancement-capsule-pakistan-price-reviews-benefits-ingredi--65a514032f84f85c13a303dd https://enkling.com/read-blog/12805 https://replit.com/@maxenergycapsul https://nepal.tribe.so/post/max-energy-male-enhancement-capsule-pakistan-price-reviews-benefits-ingredi--65a514892b5077b32f57bc46

dalyaff/phi2-viggo-finetun

dalyaff

transformers

text-generation

Entry not found

hxxris/haaris-transformer-final-3

hxxris

transformers

audio-classification

Entry not found

axra/mistral-4x7B

axra

transformers

text-generation

Entry not found

Joe1111/bert-base-chinese

Joe1111

Entry not found

Abdoulahi07/results

Abdoulahi07

peft

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: results 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. --> # results This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 100 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

diogodsa/ia-ibovespa-ri-tech

diogodsa

Entry not found

Destiny0621/rl_course_vizdoom_health_gathering_supreme

Destiny0621

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.24 +/- 3.97 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Destiny0621/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

farquasar/whisper-large-medgical-augmented

farquasar

transformers

automatic-speech-recognition

--- language: - pt license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: medgical pt large augmented 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. --> # medgical pt large augmented This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the medgical large synthetic augmented dataset. ## 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: 1e-05 - train_batch_size: 14 - 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: 1 - training_steps: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.3.2 - Tokenizers 0.15.0

hxxris/haaris-transformer-final-model

hxxris

transformers

audio-classification

Entry not found

aksds/checkpoint-100

aksds

transformers

automatic-speech-recognition

Entry not found

TunahanGokcimen/Question-Answering-Electra-base

TunahanGokcimen

transformers

question-answering

--- license: cc-by-4.0 base_model: deepset/electra-base-squad2 tags: - generated_from_trainer model-index: - name: Question-Answering-Electra-base 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. --> # Question-Answering-Electra-base This model is a fine-tuned version of [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2) on an unknown dataset. ## 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

Maaeedd/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

Maaeedd

Entry not found

Sacralet/llama2-7B

Sacralet

transformers

text-generation

--- license: apache-2.0 ---

kimjisoobkkai/MIYEON_GIDlE_1000EPOCHES

kimjisoobkkai

Entry not found

Mik99/phi-2_test_01

Mik99

peft

--- library_name: peft base_model: microsoft/phi-2 --- # 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 0.7.1

gbsim/ddpm-ema-cifar-32

gbsim

diffusers

Entry not found

prp131/my_awesome_billsum_model

prp131

transformers

text2text-generation

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model 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. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5437 - Rouge1: 0.1434 - Rouge2: 0.0526 - Rougel: 0.1205 - Rougelsum: 0.1203 - Gen Len: 19.0 ## 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8449 | 0.1267 | 0.0375 | 0.1083 | 0.1082 | 19.0 | | No log | 2.0 | 124 | 2.6263 | 0.1384 | 0.0484 | 0.1163 | 0.1163 | 19.0 | | No log | 3.0 | 186 | 2.5599 | 0.1423 | 0.0505 | 0.1194 | 0.1192 | 19.0 | | No log | 4.0 | 248 | 2.5437 | 0.1434 | 0.0526 | 0.1205 | 0.1203 | 19.0 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

meenham/tapt-roberta-large-bs256-ep100

meenham

transformers

fill-mask

Entry not found

Tsuinzues/rath

Tsuinzues

--- license: openrail ---

ElonTusk2001/zephyr-7b-sft-qlora

ElonTusk2001

peft

--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/ultrachat_200k base_model: mistralai/Mistral-7B-v0.1 model-index: - name: zephyr-7b-sft-qlora 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. --> # zephyr-7b-sft-qlora This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: 0.9523 ## 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: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.913 | 1.0 | 17428 | 0.9523 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.1 - Datasets 2.16.1 - Tokenizers 0.15.0

Spacyzipa/sam_15_01_24_neom

Spacyzipa

transformers

Entry not found

wenjing1205/test-dialogue-summarization

wenjing1205

transformers

text2text-generation

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: test-dialogue-summarization 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. --> # test-dialogue-summarization This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0710 - Rouge: {'rouge1': 46.5916, 'rouge2': 21.9208, 'rougeL': 22.0124, 'rougeLsum': 22.0124} - Bert Score: 0.8784 - Bleurt 20: -0.7903 - Gen Len: 15.58 ## 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: 2e-05 - train_batch_size: 7 - eval_batch_size: 7 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge | Bert Score | Bleurt 20 | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|:----------:|:---------:|:-------:| | 2.5288 | 1.0 | 186 | 2.1809 | {'rouge1': 48.087, 'rouge2': 21.7173, 'rougeL': 21.5447, 'rougeLsum': 21.5447} | 0.877 | -0.8143 | 15.63 | | 2.3277 | 2.0 | 372 | 2.1230 | {'rouge1': 47.3856, 'rouge2': 21.3069, 'rougeL': 21.6399, 'rougeLsum': 21.6399} | 0.8788 | -0.786 | 15.54 | | 2.2381 | 3.0 | 558 | 2.0912 | {'rouge1': 45.9843, 'rouge2': 21.1854, 'rougeL': 21.4006, 'rougeLsum': 21.4006} | 0.8776 | -0.817 | 15.235 | | 2.2123 | 4.0 | 744 | 2.0761 | {'rouge1': 46.5269, 'rouge2': 21.7291, 'rougeL': 21.8936, 'rougeLsum': 21.8936} | 0.8785 | -0.7809 | 15.515 | | 2.2443 | 5.0 | 930 | 2.0710 | {'rouge1': 46.5916, 'rouge2': 21.9208, 'rougeL': 22.0124, 'rougeLsum': 22.0124} | 0.8784 | -0.7903 | 15.58 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

ranaShams/trial

ranaShams

Entry not found

Mik99/phi-2_test_01_merged

Mik99

transformers

text-generation

Entry not found

kakooza/micho

kakooza

Entry not found

AleksDMR/My_ch

AleksDMR

Entry not found

Tuan22/22

Tuan22

Entry not found

Maaeedd/test_bug_temporary

Maaeedd

Entry not found

DenisTheDev/Openchat-Passthrough

DenisTheDev

transformers

text-generation

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - openchat/openchat-3.5-1210 - openchat/openchat-3.5-0106 --- # Openchat-Passthrough Openchat-Passthrough is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210) * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) ## 🧩 Configuration ```yaml slices: - sources: - model: openchat/openchat-3.5-1210 layer_range: [0, 32] - sources: - model: openchat/openchat-3.5-0106 layer_range: [16, 32] merge_method: passthrough dtype: bfloat16 ```

hxxris/haaris-transformer-final-model1

hxxris

transformers

audio-classification

Entry not found

walebadr/mamba-2.8b-SFT

walebadr

transformers

--- license: apache-2.0 --- This is a the state-spaces mamba-2.8b model, fine-tuned using Supervised Fine-tuning method (SFT) on llama-2-7b-miniguanaco dataset. To run inference on this model, run the following code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel #Load the model model = MambaLMHeadModel.from_pretrained("walebadr/mamba-2.8b-SFT", dtype=torch.bfloat16, device="cuda") device = "cuda" messages = [] user_message = f"[INST] what is a language model? [/INST]" input_ids = tokenizer(user_message, return_tensors="pt").input_ids.to("cuda") out = model.generate(input_ids=input_ids, max_length=500, temperature=0.9, top_p=0.7, eos_token_id=tokenizer.eos_token_id) decoded = tokenizer.batch_decode(out) print("Model:", decoded[0]) ``` ### Model Evaluation Coming soon

AswanthCManoj/azma-OpenHermes-2.5-Mistral-7B-agent-v1

AswanthCManoj

peft

--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B --- # 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.2

jen5000/whisper_checkpoint

jen5000

Entry not found

beibeif/CartPole_v1

beibeif

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole_v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

kxk254/my_awesome_model

kxk254

Entry not found

DiolixMexico/DiolixMexico

DiolixMexico

<p><a href="https://www.boxdrug.com/DioMexi"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhzgZN9R7VEe7ofv789lKnSh9TTqPjWFuvn0z8_XS0zfnc5Rs24VoXT4MBqJwJqMo_bUFtuyBA2xxZY4raccPy0ro2kZyGX25v_rxJctKeiDYYJ8eyJQ1QUJToFWJAzUGHNs7w3Xu0uDgc6-croGb2jJA4LIP-8RvGe6tKz9fzuDI26g-2IrndK3fS8DNE/w604-h396/Diolix%20Caps%20-%20MX%20.png" alt="enter image description here"> </a></p> Diolix cápsulas para diabetes! ¡Compre en Mexico y obtenga descuento! leer reseñas 2024 y precio! Diolix es la fórmula para la diabetes más eficaz que favorece un mejor control del azúcar en la sangre y, sobre todo, una salud saludable. Diolix ¡¡Comprar ahora!! ¡Haga clic en el enlace a continuación para obtener más información y obtenga un 50% de descuento ahora! apresúrate !! comprar ahora: https://www.boxdrug.com/DioMexi ➢Nombre del producto: Diolix ➢Categoría – Diabetes ➢Principales beneficios: mantener los niveles de azúcar en sangre ➢ Composición — Compuesto Orgánico Natural ➢ Efectos secundarios—NA ➢Calificación final: — 4.8 ➢ Disponibilidad: en línea ➢Ofertas y descuentos; ¡AHORRA HOY! COMPRAR AHORA PARA comprar ¡¡¡OFERTA ESPECIAL!!! <p><a href="https://www.boxdrug.com/DioMexi"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhzgZN9R7VEe7ofv789lKnSh9TTqPjWFuvn0z8_XS0zfnc5Rs24VoXT4MBqJwJqMo_bUFtuyBA2xxZY4raccPy0ro2kZyGX25v_rxJctKeiDYYJ8eyJQ1QUJToFWJAzUGHNs7w3Xu0uDgc6-croGb2jJA4LIP-8RvGe6tKz9fzuDI26g-2IrndK3fS8DNE/w604-h396/Diolix%20Caps%20-%20MX%20.png" alt="enter image description here"> </a></p> ¿Qué es el azúcar en sangre Diolix? Diolix es un suplemento dietético formulado para ayudar a mantener niveles equilibrados de azúcar en sangre. Está compuesto por una mezcla única de ingredientes naturales, cada uno elegido por su potencial para respaldar la salud metabólica general y ayudar en el control de los niveles de glucosa en el cuerpo. https://www.boxdrug.com/DioMexi https://sites.google.com/view/diolix-capsula-mexico/home https://healthtoned.blogspot.com/2024/01/diolix-capsulas-para-diabetes-compre-en.html https://medium.com/@healthytalk24x7/diolix-c%C3%A1psulas-para-diabetes-compre-en-mexico-y-obtenga-descuento-leer-rese%C3%B1as-2024-y-precio-73ae92b8d62a https://medium.com/@healthytalk24x7/diolix-mexico-c499929fffb6 https://www.weddingwire.com/website/diolix-and-mexico https://www.weddingwire.com/website/diolix-and-mexico/diolixmexico-2 https://infogram.com/diolix-mexico-1hnp27mw93m3n2g?live https://softisenilspain.hashnode.dev/diolix-capsulas-para-diabetes-compre-en-mexico-y-obtenga-descuento-leer-resenas-2024-y-precio https://sway.cloud.microsoft/nzFAXv1sfH6EY73g https://diolixmexico.company.site/ https://gamma.app/docs/Diolix-Mexico-kivprcidvub25je?mode=doc https://groups.google.com/g/snshine/c/DcmH_e_T6D8 https://healthytalk24x7.wixsite.com/sunshine/post/diolix-c%C3%A1psulas-para-diabetes-compre-en-mexico-y-obtenga-descuento-leer-rese%C3%B1as-2024-y-precio https://community.thebatraanumerology.com/user/diolixmexico https://community.thebatraanumerology.com/post/diolix-capsulas-para-diabetes-compre-en-mexico-y-obtenga-descuento-leer-res--65a523703b2331bb8a25d594 https://replit.com/@diolixmexico https://nepal.tribe.so/post/diolix-capsulas-para-diabetes-compre-en-mexico-y-obtenga-descuento-leer-res--65a523f26b054453deb6eff7 https://enkling.com/read-blog/12821

Kurokenshin/flaviasayuri

Kurokenshin

Entry not found

jvh/Mistral-Openchat-GEITje

jvh

transformers

text-generation

--- base_model: - openchat/openchat-3.5-0106 - Rijgersberg/GEITje-7B-chat-v2 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: * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) * [Rijgersberg/GEITje-7B-chat-v2](https://huggingface.co/Rijgersberg/GEITje-7B-chat-v2) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Rijgersberg/GEITje-7B-chat-v2 layer_range: [0, 32] - model: openchat/openchat-3.5-0106 layer_range: [0, 32] merge_method: slerp base_model: openchat/openchat-3.5-0106 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 ```

AHToone/llama-7b-qlora-ultrachat

AHToone

Entry not found

leedddd/xlm-roberta-base-finetuned-panx-de

leedddd

Entry not found

felipesampaio/modelotommy

felipesampaio

Entry not found

dolo650/mistral_instruct_generation

dolo650

peft

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: mistral_instruct_generation 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. --> # mistral_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.5338 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4346 | 0.83 | 20 | 1.2796 | | 1.3373 | 1.67 | 40 | 1.2653 | | 1.1182 | 2.5 | 60 | 1.3080 | | 0.9329 | 3.33 | 80 | 1.3794 | | 0.8498 | 4.17 | 100 | 1.5338 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

SZ0/Rosalina

SZ0

Entry not found

dolo650/Mistral-7B-mosaicml-instruct-v3-500

dolo650

Entry not found

Omarqq/code_phi-2.7b1

Omarqq

transformers

text-generation

Entry not found

DataVare/OST-To-EML-Converter

DataVare

The smartest way to directly import an OST file with all mail folders with attachments into an EML is the DataVare OST to EML Converter Tool. Before migrating, you can examine the live preview, and you can easily import both single and numerous MBOX files. It supports a variety of email clients, including those supported by EML, such as Applemail, WLM, Thunderbird, etc. The software supported Numerous versions of Outlook like - 2003, 2007, 2013, 2016, 2019, and 2021. Only user-specified OST files are selected with the use of the advanced filtering key. After the migration, the internal layout of the OST database is intact. The application exports OST email data natively and securely into EML file format. The utility is made simple, quick, and precise by the features. It is compatible with every version of Windows OS, including Windows 11, 10, 8, 8.1, 7, Vista, and XP. There is no need to install Outlook or other programs for the conversion. It is easy to use for both technical and non-technical users due to its user-friendly interface. Read More:- https://www.datavare.com/software/ost-to-eml-converter-expert.html

vitu98/Liam

vitu98

--- license: unknown ---

KingJulian687/q-FrozenLake-v1-4x4-noSlippery

KingJulian687

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="KingJulian687/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

jeppe-style/distilbert-base-uncased-italian-cr-entry-classification

jeppe-style

Entry not found

jvh/Mistral-Openchat-GEITje-v2

jvh

transformers

text-generation

--- base_model: - Rijgersberg/GEITje-7B-chat-v2 - openchat/openchat-3.5-0106 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: * [Rijgersberg/GEITje-7B-chat-v2](https://huggingface.co/Rijgersberg/GEITje-7B-chat-v2) * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Rijgersberg/GEITje-7B-chat-v2 layer_range: [0, 32] - model: openchat/openchat-3.5-0106 layer_range: [0, 32] merge_method: slerp base_model: Rijgersberg/GEITje-7B-chat-v2 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 ```

KingJulian687/My-Taxi-v3

KingJulian687

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: My-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="KingJulian687/My-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

gzbang-test-org/model1

gzbang-test-org

Entry not found

jlvdoorn/whisper-tiny-atco2-asr

jlvdoorn

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-tiny-atco2-asr 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. --> # whisper-tiny-atco2-asr This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0505 - Wer: 112.9893 ## 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: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.8611 | 12.5 | 50 | 1.5491 | 100.1779 | | 0.5484 | 25.0 | 100 | 1.1962 | 91.5036 | | 0.1272 | 37.5 | 150 | 1.0106 | 158.7189 | | 0.0125 | 50.0 | 200 | 1.0290 | 124.3327 | | 0.0074 | 62.5 | 250 | 1.0401 | 116.7705 | | 0.005 | 75.0 | 300 | 1.0461 | 118.6833 | | 0.0044 | 87.5 | 350 | 1.0493 | 113.0783 | | 0.004 | 100.0 | 400 | 1.0505 | 112.9893 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.15.0 - Tokenizers 0.15.0

JoshXT/zephyr-7b-beta-32k

JoshXT

peft

--- library_name: peft base_model: unsloth/zephyr-sft --- # 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 0.7.1

Dhanraj1503/Huggy

Dhanraj1503

ml-agents

reinforcement-learning

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Dhanraj1503/Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Prasanna16/FineTunedLlamaWithPython

Prasanna16

peft

--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: results 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. --> # results This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an [python](https://huggingface.co/iamtarun/python_code_instructions_18k_alpaca) dataset. ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

duanyu027/loyal-piano-m7-dpo-0115-125steps

duanyu027

transformers

text-generation

Entry not found

Kamsaka/Dehya

Kamsaka

Entry not found

Praghxx/Red

Praghxx

Entry not found

michaelhu1/poem

michaelhu1

Entry not found

datalawyer/pedidos-transformerscrf-v5.3-8bit

datalawyer

transformers

Entry not found