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SALUTEASD/Qwen-Qwen1.5-1.8B-1726076863 | SALUTEASD | "2024-09-11T17:47:52Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T17:47:44Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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NewBie456/Vixon_g0th1cPXL_style | NewBie456 | "2024-09-12T03:42:18Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T17:48:29Z" | ![pic_2.jpeg](https://cdn-uploads.huggingface.co/production/uploads/65e8023ee5e78134ab2b5d37/X5fWapFS6qqbPgueZwKv9.jpeg)
![05463-1779656942-score_9. score_8_up, score_7_up, score_6_up, score_5_up, score_4_up, 1girl, blue hair, curvy, gothic, g0th1cPXL, glowing, neon,.jpeg](https://cdn-uploads.huggingface.co/production/uploads/65e8023ee5e78134ab2b5d37/Ref4dX8LB9HF272TPoZki.jpeg)
|
canho/dpo3480newdpo_ours_2e-4_5e-8_2 | canho | "2024-09-11T17:48:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T17:48:30Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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MohammedMouad/llama3.18B-Fine-tunedByRobert | MohammedMouad | "2024-09-11T18:09:11Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-09-11T17:48:31Z" | Entry not found |
Rassputinnn/model_task2 | Rassputinnn | "2024-09-11T17:49:14Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T17:48:34Z" | Entry not found |
Krabat/Qwen-Qwen1.5-0.5B-1726076921 | Krabat | "2024-09-11T17:48:43Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T17:48:41Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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buttonwild/google-gemma-2b-1726076934 | buttonwild | "2024-09-11T17:48:59Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T17:48:53Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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Muradn/YavuzSelim | Muradn | "2024-09-11T17:50:39Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-09-11T17:49:56Z" | ---
license: openrail
---
|
darwintattoo/02stencil | darwintattoo | "2024-09-11T18:37:10Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-11T17:49:57Z" | ---
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
--- |
dogssss/Qwen-Qwen1.5-0.5B-1726077015 | dogssss | "2024-09-11T17:50:19Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T17:50:15Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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SALUTEASD/google-gemma-2b-1726077029 | SALUTEASD | "2024-09-11T17:51:16Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T17:50:30Z" | ---
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
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- PEFT 0.12.0 |
yuvasimha/llama-2-7b-yuva-test1 | yuvasimha | "2024-09-11T17:58:05Z" | 0 | 0 | null | [
"pytorch",
"llama",
"region:us"
] | null | "2024-09-11T17:50:46Z" | Entry not found |
linger2334/google-gemma-7b-1726077054 | linger2334 | "2024-09-11T17:52:25Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-11T17:50:55Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- PEFT 0.12.0 |
buttonwild/Qwen-Qwen1.5-0.5B-1726077127 | buttonwild | "2024-09-11T17:52:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T17:52:07Z" | ---
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
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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
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[More Information Needed]
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## 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]
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### Framework versions
- PEFT 0.12.0 |
MHGanainy/gpt2-xl-lora-ecthr | MHGanainy | "2024-09-12T22:07:49Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:openai-community/gpt2-xl",
"base_model:adapter:openai-community/gpt2-xl",
"license:mit",
"region:us"
] | null | "2024-09-11T17:52:33Z" | ---
base_model: openai-community/gpt2-xl
library_name: peft
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-xl-lora-ecthr
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. -->
# gpt2-xl-lora-ecthr
This model is a fine-tuned version of [openai-community/gpt2-xl](https://huggingface.co/openai-community/gpt2-xl) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7725
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1 |
LuxDL/alexnet | LuxDL | "2024-09-11T18:06:05Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-09-11T17:52:39Z" | ---
license: mit
---
|
arshiakarimian1/Llama3.1-Ar-ce-QLoRA | arshiakarimian1 | "2024-09-11T20:24:17Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | "2024-09-11T17:53:33Z" | Entry not found |
AlignmentResearch/robust_llm_clf_spam_pythia-14m_s-0_adv_tr_gcg_t-0 | AlignmentResearch | "2024-09-11T17:54:12Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T17:54:12Z" | Entry not found |
linger2334/Qwen-Qwen1.5-0.5B-1726077267 | linger2334 | "2024-09-11T17:54:43Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T17:54:28Z" | ---
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
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<!-- 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]
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<!-- 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 |
EXE-240/MODE-EXE | EXE-240 | "2024-09-11T17:54:31Z" | 0 | 0 | null | [
"license:cc",
"region:us"
] | null | "2024-09-11T17:54:31Z" | ---
license: cc
---
|
SALUTEASD/Qwen-Qwen1.5-0.5B-1726077275 | SALUTEASD | "2024-09-11T17:58:00Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T17:54:36Z" | ---
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]
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<!-- 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
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[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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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buttonwild/Qwen-Qwen1.5-1.8B-1726077312 | buttonwild | "2024-09-11T17:55:15Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T17:55:12Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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dogssss/Qwen-Qwen1.5-1.8B-1726077313 | dogssss | "2024-09-11T17:55:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T17:55:13Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
peaceAsh/ppo-LunarLander-v2-unit1 | peaceAsh | "2024-09-11T17:56:18Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-09-11T17:55:51Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ' PPO'
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 237.22 +/- 45.59
name: mean_reward
verified: false
---
# ** PPO** Agent playing **LunarLander-v2**
This is a trained model of a ** PPO** agent playing **LunarLander-v2**
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
...
```
|
canho/dpo3828newdpo_ours_2e-4_5e-8_2 | canho | "2024-09-11T17:56:09Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T17:55:59Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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<!-- 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).
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[More Information Needed]
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ishan1990/Ishan | ishan1990 | "2024-09-11T17:56:06Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T17:56:06Z" | Entry not found |
kindjeeps/asdss | kindjeeps | "2024-09-11T17:56:20Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-09-11T17:56:20Z" | ---
license: apache-2.0
---
|
buttonwild/google-gemma-2b-1726077460 | buttonwild | "2024-09-11T17:57:45Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T17:57:40Z" | ---
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
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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).
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## Technical Specifications [optional]
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[More Information Needed]
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linger2334/Qwen-Qwen1.5-1.8B-1726077468 | linger2334 | "2024-09-11T17:58:05Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T17:57:49Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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## 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]
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- PEFT 0.12.0 |
Huggipavel/llava-1.5-7b-hf-ft-mix-vsft | Huggipavel | "2024-09-11T17:58:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T17:58:17Z" | Entry not found |
AngieMojica/sentiment-snalysis-model | AngieMojica | "2024-09-11T17:58:24Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T17:58:24Z" | Entry not found |
dogssss/Qwen-Qwen1.5-0.5B-1726077558 | dogssss | "2024-09-11T17:59:22Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T17:59:19Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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danielcarreong/TEST2 | danielcarreong | "2024-09-11T18:00:29Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-09-11T18:00:28Z" | ---
license: mit
---
|
buttonwild/Qwen-Qwen1.5-0.5B-1726077671 | buttonwild | "2024-09-11T18:01:15Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:01:11Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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salimu/aimee-lora | salimu | "2024-09-11T22:32:28Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-11T18:01:13Z" | ---
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
--- |
SALUTEASD/Qwen-Qwen1.5-1.8B-1726077680 | SALUTEASD | "2024-09-11T18:01:28Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T18:01:21Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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Krabat/Qwen-Qwen1.5-1.8B-1726077695 | Krabat | "2024-09-11T18:01:38Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T18:01:35Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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dogssss/Qwen-Qwen1.5-1.8B-1726077737 | dogssss | "2024-09-11T18:02:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:02:17Z" | Entry not found |
dtzx/Qwen2-Math-7B-Instruct | dtzx | "2024-09-11T18:02:23Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:02:22Z" | Entry not found |
canho/dpo4176newdpo_ours_2e-4_5e-8_2 | canho | "2024-09-11T18:03:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:03:24Z" | ---
library_name: transformers
tags: []
---
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SALUTEASD/google-gemma-2b-1726077855 | SALUTEASD | "2024-09-11T18:04:44Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T18:04:16Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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[More Information Needed]
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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).
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- PEFT 0.12.0 |
buttonwild/Qwen-Qwen1.5-1.8B-1726077859 | buttonwild | "2024-09-11T18:04:23Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T18:04:19Z" | ---
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
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[More Information Needed]
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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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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- PEFT 0.12.0 |
Krabat/google-gemma-2b-1726077860 | Krabat | "2024-09-11T18:04:23Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T18:04:20Z" | ---
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
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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
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[More Information Needed]
## Training Details
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<!-- 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]
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## Technical Specifications [optional]
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
Manal0809/Mistrial_original_data | Manal0809 | "2024-09-11T18:08:24Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"base_model:finetune:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:04:36Z" | ---
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** Manal0809
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
This mistral 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)
|
KavaFromUkraine/portfolio | KavaFromUkraine | "2024-09-11T18:08:12Z" | 0 | 0 | null | [
"uk",
"dataset:fka/awesome-chatgpt-prompts",
"base_model:togethercomputer/RedPajama-INCITE-Instruct-3B-v1",
"base_model:finetune:togethercomputer/RedPajama-INCITE-Instruct-3B-v1",
"region:us"
] | null | "2024-09-11T18:04:41Z" | ---
datasets:
- fka/awesome-chatgpt-prompts
language:
- uk
base_model:
- togethercomputer/RedPajama-INCITE-Instruct-3B-v1
--- |
Salmamoori/idefics-9b-logo | Salmamoori | "2024-09-11T18:31:15Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:HuggingFaceM4/idefics-9b",
"base_model:adapter:HuggingFaceM4/idefics-9b",
"license:other",
"region:us"
] | null | "2024-09-11T18:05:51Z" | ---
base_model: HuggingFaceM4/idefics-9b
library_name: peft
license: other
tags:
- generated_from_trainer
model-index:
- name: idefics-9b-logo
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. -->
# idefics-9b-logo
This model is a fine-tuned version of [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b) 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1 |
gohsyi/gemma-2-2b-dpo-ultrafeedback | gohsyi | "2024-09-11T18:21:46Z" | 0 | 0 | null | [
"safetensors",
"gemma2",
"region:us"
] | null | "2024-09-11T18:06:18Z" | Entry not found |
buttonwild/google-gemma-2b-1726078008 | buttonwild | "2024-09-11T18:06:55Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T18:06:48Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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[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]
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#### Preprocessing [optional]
[More Information Needed]
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#### 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
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[More Information Needed]
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[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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.12.0 |
farahsaad/maskformer_instanec_segment_roofs3 | farahsaad | "2024-09-13T13:22:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"maskformer",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:07:13Z" | ---
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]
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- **Shared by [optional]:** [More Information Needed]
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<!-- 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
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[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]
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<!-- 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]
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- **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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
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AhsanShahid/Creative_Writing_Assistant | AhsanShahid | "2024-09-11T18:07:24Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:07:24Z" | Entry not found |
Krabat/google-gemma-7b-1726078067 | Krabat | "2024-09-11T18:07:50Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-11T18:07:47Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Hardware Type:** [More Information Needed]
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### Framework versions
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bunnycore/Phi-3.5-RP-Lora | bunnycore | "2024-09-11T18:08:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:08:02Z" | ---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** bunnycore
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-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)
|
Moni040/tc1109_model | Moni040 | "2024-09-11T18:09:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:09:12Z" | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** Moni040
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
|
SALUTEASD/Qwen-Qwen1.5-0.5B-1726078153 | SALUTEASD | "2024-09-11T18:09:21Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:09:15Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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## Model Details
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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]
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
YVESLEBRUN/YVESLEBRUN | YVESLEBRUN | "2024-09-11T18:09:24Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:09:24Z" | Entry not found |
sebasbayer/fajardo | sebasbayer | "2024-09-11T18:38:01Z" | 0 | 1 | null | [
"license:other",
"region:us"
] | null | "2024-09-11T18:09:32Z" | ---
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
--- |
buttonwild/Qwen-Qwen1.5-0.5B-1726078203 | buttonwild | "2024-09-11T18:10:07Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:10:03Z" | ---
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
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[More Information Needed]
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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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
dtzx/llava-1.5-7b-hf | dtzx | "2024-09-11T18:10:30Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:10:30Z" | Entry not found |
WernerWeiss/flux_werner | WernerWeiss | "2024-09-11T19:29:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:10:36Z" | Entry not found |
canho/dpo4524newdpo_ours_2e-4_5e-8_2 | canho | "2024-09-11T18:11:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:10:52Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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.
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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
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<!-- 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]
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ajinkyagaikwad29/llama-3-8b-instruct-qa-demo_4 | ajinkyagaikwad29 | "2024-09-11T18:11:13Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:11:05Z" | ---
base_model: unsloth/llama-3-8b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** ajinkyagaikwad29
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
|
mawriyo/Smollm-135M-Instruct_q4f16_0 | mawriyo | "2024-09-11T21:35:35Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:11:13Z" | Entry not found |
mistralai/Pixtral-12B-2409 | mistralai | "2024-10-24T10:25:37Z" | 0 | 456 | vllm | [
"vllm",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"base_model:mistralai/Pixtral-12B-Base-2409",
"base_model:finetune:mistralai/Pixtral-12B-Base-2409",
"license:apache-2.0",
"region:us"
] | null | "2024-09-11T18:11:16Z" | ---
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: apache-2.0
library_name: vllm
base_model:
- mistralai/Pixtral-12B-Base-2409
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
# Model Card for Pixtral-12B-2409
The Pixtral-12B-2409 is a Multimodal Model of 12B parameters plus a 400M parameter vision encoder.
For more details about this model please refer to our release [blog post](https://mistral.ai/news/pixtral-12b/).
Feel free to try it [here](https://chat.mistral.ai/chat)
## Key features
- Natively multimodal, trained with interleaved image and text data
- 12B parameter Multimodal Decoder + 400M parameter Vision Encoder
- Supports variable image sizes
- Leading performance in its weight class on multimodal tasks
- Maintains state-of-the-art performance on text-only benchmarks
- Sequence length: 128k
- License: Apache 2.0
## Benchmarks
The performance of Pixtral-12B-2409 compared to multimodal models.
All models were re-evaluated and benchmarked through the same evaluation pipeline.
### Multimodal Benchmarks
| | Pixtral 12B | Qwen2 7B VL | LLaVA-OV 7B | Phi-3 Vision | Phi-3.5 Vision |
|:-------------------:|:-------------:|:----------:|:-------------:|:--------------:|:--------------:|
| **MMMU** *(CoT)* | <ins>**52.5**</ins> | 47.6 | 45.1 | 40.3 | 38.3 |
| **Mathvista** *(CoT)* | <ins>**58.0**</ins> | 54.4 | 36.1 | 36.4 | 39.3 |
| **ChartQA** *(CoT)* | <ins>**81.8**</ins> | 38.6 | 67.1 | 72.0 | 67.7 |
| **DocVQA** *(ANLS)* | 90.7 | <ins>**94.5**</ins> | 90.5 | 84.9 | 74.4 |
| **VQAv2** *(VQA Match)* | <ins>**78.6**</ins> | 75.9 | 78.3 | 42.4 | 56.1 |
### Instruction Following
| | Pixtral 12B | Qwen2 7B VL | LLaVA-OV 7B | Phi-3 Vision | Phi-3.5 Vision |
|:-------------------:|:-------------:|:----------:|:-------------:|:--------------:|:--------------:|
| **MM MT-Bench** | <ins>**6.05**</ins> | 5.43 | 4.12 | 3.70 |4.46 |
| **Text MT-Bench** | <ins>**7.68**</ins> | 6.41 | 6.94 | 6.27 |6.31 |
| **MM IF-Eval** | <ins>**52.7**</ins> | 38.9 | 42.5 | 41.2 |31.4 |
| **Text IF-Eval** | <ins>**61.3**</ins> | 50.1 | 51.4 | 50.9 |47.4 |
### Text Benchmarks
| | Pixtral 12B | Qwen2 7B VL | LLaVA-OV 7B | Phi-3 Vision | Phi-3.5 Vision |
|:-------------------:|:-------------:|:----------:|:-------------:|:--------------:|:--------------:|
| **MMLU** *(5-shot)* | <ins>**69.2**</ins> | 68.5 | 67.9 | 63.5 | 63.6 |
| **Math** *(Pass@1)* | <ins>**48.1**</ins> | 27.8 | 38.6 | 29.2 | 28.4 |
| **Human Eval** *(Pass@1)* | <ins>**72.0**</ins> | 64.6 | 65.9 | 48.8 | 49.4 |
### Comparison with Closed Source and Larger Models
| | Pixtral 12B | Claude-3 Haiku | Gemini-1.5 Flash 8B *(0827)* | . |*LLaVA-OV 72B* | *GPT-4o* | *Claude-3.5 Sonnet* |
|:-------------------:|:-------------:|:----------------:|:----------------------:|:--------:|:----:|:-------------------:|:-------------------:|
| **MMMU** *(CoT)* | **52.5** | 50.4 | 50.7 | |*54.4* |<ins>*68.6*</ins> | *68.0* |
| **Mathvista** *(CoT)* | **58.0** | 44.8 | 56.9 | |*57.2* |<ins>*64.6*</ins> | *64.4* |
| **ChartQA** *(CoT)* | **81.8** | 69.6 | 78.0 | |*66.9* |*85.1* | <ins>*87.6*</ins> |
| **DocVQA** *(ANLS)* | **90.7**</ins> | 74.6 | 79.5 | |<ins>*91.6*</ins> |*88.9* | *90.3* |
| **VQAv2** *(VQA Match)* | **78.6** | 68.4 | 65.5 | |<ins>*83.8*</ins> |*77.8* | *70.7* |
## Usage Examples
### vLLM (recommended)
We recommend using Pixtral with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines with Pixtral.
**_Installation_**
Make sure you install `vLLM >= v1.6.2`:
```
pip install --upgrade vllm
```
Also make sure you have `mistral_common >= 1.4.4` installed:
```
pip install --upgrade mistral_common
```
You can also make use of a ready-to-go [docker image](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39?context=explore).
**_Simple Example_**
```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
model_name = "mistralai/Pixtral-12B-2409"
sampling_params = SamplingParams(max_tokens=8192)
llm = LLM(model=model_name, tokenizer_mode="mistral")
prompt = "Describe this image in one sentence."
image_url = "https://picsum.photos/id/237/200/300"
messages = [
{
"role": "user",
"content": [{"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": image_url}}]
},
]
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
```
**_Advanced Example_**
You can also pass multiple images per message and/or pass multi-turn conversations
```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
model_name = "mistralai/Pixtral-12B-2409"
max_img_per_msg = 5
sampling_params = SamplingParams(max_tokens=8192, temperature=0.7)
# Lower max_num_seqs or max_model_len on low-VRAM GPUs.
llm = LLM(model=model_name, tokenizer_mode="mistral", limit_mm_per_prompt={"image": max_img_per_msg}, max_model_len=32768)
prompt = "Describe the following image."
url_1 = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
url_2 = "https://picsum.photos/seed/picsum/200/300"
url_3 = "https://picsum.photos/id/32/512/512"
messages = [
{
"role": "user",
"content": [{"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": url_1}}, {"type": "image_url", "image_url": {"url": url_2}}],
},
{
"role": "assistant",
"content": "The images shows nature.",
},
{
"role": "user",
"content": "More details please and answer only in French!."
},
{
"role": "user",
"content": [{"type": "image_url", "image_url": {"url": url_3}}],
}
]
outputs = llm.chat(messages=messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
```
You can find more examples and tests directly in vLLM.
- [Examples](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_pixtral.py)
- [Tests](https://github.com/vllm-project/vllm/blob/main/tests/models/test_pixtral.py)
**_Server_**
You can also use pixtral in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Pixtral-12B-2409 --tokenizer_mode mistral --limit_mm_per_prompt 'image=4'
```
2. And ping the client:
```
curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer token' \
--data '{
"model": "mistralai/Pixtral-12B-2409",
"messages": [
{
"role": "user",
"content": [
{"type" : "text", "text": "Describe this image in detail please."},
{"type": "image_url", "image_url": {"url": "https://s3.amazonaws.com/cms.ipressroom.com/338/files/201808/5b894ee1a138352221103195_A680%7Ejogging-edit/A680%7Ejogging-edit_hero.jpg"}},
{"type" : "text", "text": "and this one as well. Answer in French."},
{"type": "image_url", "image_url": {"url": "https://www.wolframcloud.com/obj/resourcesystem/images/a0e/a0ee3983-46c6-4c92-b85d-059044639928/6af8cfb971db031b.png"}}
]
}
]
}'
```
### Mistral-inference
We recommend using [mistral-inference](https://github.com/mistralai/mistral-inference) to quickly try out / "vibe-check" Pixtral.
**_Install_**
Make sure to have `mistral_inference >= 1.4.1` installed.
```
pip install mistral_inference --upgrade
```
**_Download_**
```py
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', 'Pixtral')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Pixtral-12B-2409", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
```
**_Chat_**
After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment.
You can pass text and images or image urls to the model in *instruction-following* mode as follows:
```
mistral-chat $HOME/mistral_models/Pixtral --instruct --max_tokens 256 --temperature 0.35
```
*E.g.* Try out something like:
```
Text prompt: What can you see on the following picture?
[You can input zero, one or more images now.]
Image path or url [Leave empty and press enter to finish image input]: https://picsum.photos/id/237/200/300
Image path or url [Leave empty and press enter to finish image input]:
I see a black dog lying on a wooden surface. The dog appears to be looking up, and its eyes are clearly visible.
```
**_Python_**
You can also run the model in a Python shell as follows.
```py
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage, TextChunk, ImageURLChunk
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path)
url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
prompt = "Describe the image."
completion_request = ChatCompletionRequest(messages=[UserMessage(content=[ImageURLChunk(image_url=url), TextChunk(text=prompt)])])
encoded = tokenizer.encode_chat_completion(completion_request)
images = encoded.images
tokens = encoded.tokens
out_tokens, _ = generate([tokens], model, images=[images], max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])
print(result)
```
## Limitations
The Pixtral model does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Diogo Costa, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall |
mawriyo/Smollm-135M-Instruct_q4f16_1 | mawriyo | "2024-09-11T18:11:28Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:11:28Z" | Entry not found |
buttonwild/Qwen-Qwen1.5-1.8B-1726078389 | buttonwild | "2024-09-11T18:13:13Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T18:13:09Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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SALUTEASD/Qwen-Qwen1.5-1.8B-1726078405 | SALUTEASD | "2024-09-11T18:13:32Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T18:13:26Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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### Framework versions
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MrAR/llama3.18B-Movie-tuned-16-800 | MrAR | "2024-09-11T23:21:02Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | "2024-09-11T18:13:30Z" | Entry not found |
AhsanShahid/creative_model | AhsanShahid | "2024-09-11T18:15:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:15:02Z" | Entry not found |
MoE-UNC/gpt-generated-instruction-nomic-embeddings | MoE-UNC | "2024-10-19T21:34:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:15:09Z" | Entry not found |
saberbx/New2 | saberbx | "2024-09-11T18:17:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:15:10Z" | Entry not found |
AlignmentResearch/robust_llm_clf_spam_pythia-2.8b_s-1_adv_tr_gcg_t-1 | AlignmentResearch | "2024-09-11T18:15:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:15:26Z" | Entry not found |
buttonwild/google-gemma-2b-1726078538 | buttonwild | "2024-09-11T18:15:44Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T18:15:37Z" | ---
base_model: google/gemma-2b
library_name: peft
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SALUTEASD/google-gemma-2b-1726078565 | SALUTEASD | "2024-09-11T18:17:22Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T18:16:06Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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emojiojio/Qwen-Qwen1.5-0.5B-1726078700 | emojiojio | "2024-09-11T18:18:26Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:18:20Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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canho/dpo4872newdpo_ours_2e-4_5e-8_2 | canho | "2024-09-11T18:18:32Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:18:21Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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linger2334/Qwen-Qwen1.5-7B-1726078728 | linger2334 | "2024-09-11T18:19:23Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | "2024-09-11T18:18:49Z" | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
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buttonwild/Qwen-Qwen1.5-0.5B-1726078734 | buttonwild | "2024-09-11T18:18:58Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:18:54Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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mawriyo/Qwen2-1.5B-Instruct-q4f16_0 | mawriyo | "2024-09-11T18:24:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:18:58Z" | Entry not found |
Aquahaimer/nvidia_api | Aquahaimer | "2024-09-11T18:19:50Z" | 0 | 0 | null | [
"license:llama3.1",
"region:us"
] | null | "2024-09-11T18:19:50Z" | ---
license: llama3.1
---
|
emojiojio/Qwen-Qwen1.5-1.8B-1726078814 | emojiojio | "2024-09-11T18:20:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T18:20:14Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.12.0 |
NewBie456/ExpressiveH_Lora_Style_TESTING | NewBie456 | "2024-09-12T03:43:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:20:30Z" | ![IMG_20240912_022225.jpg](https://cdn-uploads.huggingface.co/production/uploads/65e8023ee5e78134ab2b5d37/iNjpqrSWSUSBlCEgdfQTs.jpeg)
![IMG_20240912_113456.jpg](https://cdn-uploads.huggingface.co/production/uploads/65e8023ee5e78134ab2b5d37/oNGvl8pg2DQBXIYhWEOhN.jpeg)
|
Krabat/Qwen-Qwen1.5-0.5B-1726078837 | Krabat | "2024-09-11T18:20:39Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:20:37Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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SALUTEASD/Qwen-Qwen1.5-0.5B-1726078841 | SALUTEASD | "2024-09-11T18:22:04Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:20:42Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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jerseyjerry/Qwen-Qwen2-1.5B-1726078852 | jerseyjerry | "2024-09-11T18:20:57Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2-1.5B",
"base_model:adapter:Qwen/Qwen2-1.5B",
"region:us"
] | null | "2024-09-11T18:20:52Z" | ---
base_model: Qwen/Qwen2-1.5B
library_name: peft
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.12.0 |
nave1616/segFormer-finetuned-uc | nave1616 | "2024-09-14T23:51:53Z" | 0 | 0 | null | [
"safetensors",
"segformer",
"region:us"
] | null | "2024-09-11T18:21:37Z" | Entry not found |
healtori/21-heal-09-11-06 | healtori | "2024-09-11T18:26:24Z" | 0 | 0 | null | [
"safetensors",
"mistral",
"region:us"
] | null | "2024-09-11T18:21:44Z" | Entry not found |
buttonwild/Qwen-Qwen1.5-1.8B-1726078918 | buttonwild | "2024-09-11T18:22:02Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T18:21:58Z" | ---
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. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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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).
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### Framework versions
- PEFT 0.12.0 |
mrcuddle/sportmen | mrcuddle | "2024-09-11T18:22:06Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:22:06Z" | Entry not found |
gohsyi/gemma-2-2b-it-dpo-ultrafeedback | gohsyi | "2024-09-11T18:24:19Z" | 0 | 0 | null | [
"safetensors",
"gemma2",
"region:us"
] | null | "2024-09-11T18:22:41Z" | Entry not found |
dogssss/Qwen-Qwen1.5-0.5B-1726078962 | dogssss | "2024-09-11T18:22:46Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:22:42Z" | ---
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. -->
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linger2334/google-gemma-2b-1726078978 | linger2334 | "2024-09-11T18:24:06Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T18:22:59Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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niucool/jim-lora | niucool | "2024-09-11T19:03:13Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-11T18:23:21Z" | ---
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
--- |
emojiojio/Qwen-Qwen1.5-0.5B-1726079043 | emojiojio | "2024-09-11T18:24:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-11T18:24:03Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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skywalker290/test2 | skywalker290 | "2024-09-11T18:29:05Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-09-11T18:24:18Z" | ---
license: apache-2.0
---
|
buttonwild/google-gemma-2b-1726079065 | buttonwild | "2024-09-11T18:24:31Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-11T18:24:25Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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SALUTEASD/Qwen-Qwen1.5-1.8B-1726079113 | SALUTEASD | "2024-09-11T18:25:14Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:25:14Z" | Entry not found |
canho/dpo5220newdpo_ours_2e-4_5e-8_2 | canho | "2024-09-11T18:26:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-11T18:25:50Z" | ---
library_name: transformers
tags: []
---
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emojiojio/Qwen-Qwen1.5-1.8B-1726079161 | emojiojio | "2024-09-11T18:26:07Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-11T18:26:01Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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[More Information Needed]
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#### Factors
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### Results
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#### Summary
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## Environmental Impact
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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).
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## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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### Framework versions
- PEFT 0.12.0 |
NursNurs/meme_caption_generator_FLAN-T5-large_prefix_tuned | NursNurs | "2024-09-11T18:26:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:26:27Z" | Entry not found |
GenAiBeast3333/MANGODESIGN-LoRA | GenAiBeast3333 | "2024-09-11T18:27:01Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-11T18:27:01Z" | Entry not found |