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Hachipo/Meta-Llama-3-8B-MIFT-en_newbase_v2-PIFT-enja_10000_2
|
Hachipo
| 2025-06-23T19:12:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T19:08:58Z |
---
library_name: transformers
tags:
- trl
- 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mob2711/qwen2.5-3b-qlora-cot-ht-1500
|
mob2711
| 2025-06-23T19:11:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T19:11:10Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mob2711
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 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)
|
Hachipo/Meta-Llama-3-8B-MIFT-en_newbase_v2-PIFT-jaen_10000_2
|
Hachipo
| 2025-06-23T19:11:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T19:08:00Z |
---
library_name: transformers
tags:
- trl
- 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AkumaDachi/dqn-SpaceInvadersNoFrameskip-v4
|
AkumaDachi
| 2025-06-23T19:05:29Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-23T19:05:01Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 508.00 +/- 125.04
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AkumaDachi -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AkumaDachi -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AkumaDachi
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
dearyoungjo/whisper_base_it
|
dearyoungjo
| 2025-06-23T19:04:56Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"it",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-23T19:04:39Z |
---
library_name: transformers
language:
- it
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: default
split: train
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 41.262389149713094
---
<!-- 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-base](https://huggingface.co/openai/whisper-base) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1530
- Wer: 41.2624
## 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: 2
- training_steps: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.9703 | 0.0159 | 1 | 1.1724 | 42.1492 |
| 1.0107 | 0.0317 | 2 | 1.1724 | 42.1492 |
| 1.1515 | 0.0476 | 3 | 1.1724 | 42.1492 |
| 0.843 | 0.0635 | 4 | 1.1530 | 41.2624 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
Hachipo/Meta-Llama-3-8B-MIFT-en_newbase_v2-MIFT-ja_10000_2
|
Hachipo
| 2025-06-23T19:04:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T19:01:37Z |
---
library_name: transformers
tags:
- trl
- 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
pooya-davoodi-parasail/OmniGen-v1-LoRA-01
|
pooya-davoodi-parasail
| 2025-06-23T19:00:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-23T19:00:37Z |
---
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
ikerion/gemma_innen_folytasd_v7_RESCUE
|
ikerion
| 2025-06-23T18:59:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T18:43:32Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ikerion
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
Kimanjea/avelavrmodule1
|
Kimanjea
| 2025-06-23T18:59:31Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"llama",
"facebook",
"meta",
"pytorch",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"license:llama3.2",
"region:us"
] |
text-generation
| 2025-06-23T18:44:13Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: mlx
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- mlx
license: llama3.2
extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\
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\ Agreement, you shall delete and cease use of the Llama Materials. Sections 3,\
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\ any dispute arising out of this Agreement. \n### Llama 3.2 Acceptable Use Policy\n\
Meta is committed to promoting safe and fair use of its tools and features, including\
\ Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy\
\ (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\n\
#### Prohibited Uses\nWe want everyone to use Llama 3.2 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.2 to:\n1. Violate\
\ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\
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\ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\
\ that the outputs of such tools, models, or software are associated with Meta or\
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\ with a principal place of business in, the European Union. This restriction does\
\ not apply to end users of a product or service that incorporates any such multimodal\
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extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
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Job title:
type: select
options:
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geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
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extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
base_model: meta-llama/llama-3.2-1B-Instruct
---
|
BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmc9fnyn6000reihnbas4hxva
|
BootesVoid
| 2025-06-23T18:58:30Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T18:58:28Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: CHNGRLREP
---
# Cm8Tb7Xkk0000Wzj24Pkk2M5G_Cmc9Fnyn6000Reihnbas4Hxva
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `CHNGRLREP` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CHNGRLREP",
"lora_weights": "https://huggingface.co/BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmc9fnyn6000reihnbas4hxva/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmc9fnyn6000reihnbas4hxva', weight_name='lora.safetensors')
image = pipeline('CHNGRLREP').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmc9fnyn6000reihnbas4hxva/discussions) to add images that show off what you’ve made with this LoRA.
|
michelleUMD/cmr-llama
|
michelleUMD
| 2025-06-23T18:55:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:adapter:meta-llama/Llama-3.3-70B-Instruct",
"region:us"
] | null | 2025-06-22T21:18:16Z |
---
base_model: meta-llama/Llama-3.3-70B-Instruct
library_name: peft
---
# Model Card for Model ID
CMR-LLaMA is a large language model designed to automatically extract 31 cardiovascular conditions from cardiac MRI (CMR) reports.
In addition to the conditions themselves, it also extracts their associated attributes, including certainty, severity, location, and pattern.
## Model Details
### Model Description
- **Developed by:** CCF AIIIH Lab
- **Model type:** large language model
- **Language(s) (NLP):** English
- **Finetuned from model [optional]:** pretrained LLaMA 3.3 with a custom LoRA adapter
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/michelleUMD/cmr-llama
- **Paper [optional]:** TBD
- **Demo [optional]:** TBD
## Uses
* Database generation from CMR report impressions sections
* Standardization of free text reports
## How to Get Started with the Model
To use the pretrained adapter:
``` python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")
model = PeftModel.from_pretrained(base, "/michelleUMD/cmr-llama/")
```
## Citation [optional]
TBD
**BibTeX:**
TBD
**APA:**
TBD
### Framework versions
- PEFT 0.12.0
|
Huzaifah0/Avery_0.6_4_16
|
Huzaifah0
| 2025-06-23T18:55:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T18:48:50Z |
---
library_name: transformers
tags:
- unsloth
- trl
- 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Kaori1707/SEED-1-gemma4b-instruct
|
Kaori1707
| 2025-06-23T18:54:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T01:20:32Z |
---
base_model: google/gemma-3-4b-it
library_name: transformers
model_name: SEED-1-gemma4b-instruct
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SEED-1-gemma4b-instruct
This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Kaori1707/SEED-1-gemma4b-instruct", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.4.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
B-K/ReVoiceAI-W2V2-BERT-Thai-IPA
|
B-K
| 2025-06-23T18:53:54Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-23T15:39:14Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
FiniteInfinity99/Thesis_gemma-2-9b_final_model_updated
|
FiniteInfinity99
| 2025-06-23T18:53:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T18:53:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
morturr/Llama-2-7b-hf-PAIR_dadjokes_one_liners-COMB-one_liners-comb-1-seed-28-2025-06-23
|
morturr
| 2025-06-23T18:51:57Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T18:51:49Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_dadjokes_one_liners-COMB-one_liners-comb-1-seed-28-2025-06-23
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. -->
# Llama-2-7b-hf-PAIR_dadjokes_one_liners-COMB-one_liners-comb-1-seed-28-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
KNdoschile/alpersman
|
KNdoschile
| 2025-06-23T18:48:50Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-23T17:41:54Z |
---
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
---
|
morturr/Llama-2-7b-hf-PAIR_one_liners_amazon-COMB-amazon-comb-2-seed-7-2025-06-23
|
morturr
| 2025-06-23T18:47:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T18:47:48Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_one_liners_amazon-COMB-amazon-comb-2-seed-7-2025-06-23
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. -->
# Llama-2-7b-hf-PAIR_one_liners_amazon-COMB-amazon-comb-2-seed-7-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
TOMFORD79/boom9
|
TOMFORD79
| 2025-06-23T18:47:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T18:42:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Official-Link-mezzo-fun-18-Viral-videos-XX/FULL.VIDEO.mezzo.fun.Viral.Video.Tutorial.Official
|
Official-Link-mezzo-fun-18-Viral-videos-XX
| 2025-06-23T18:46:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-23T18:46:15Z |
18 seconds ago
<a href="https://viralinfo.xyz/video/?v=Katrina+lim+kiffy" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p>
<a href="https://viralinfo.xyz/video/?v=Katrina+lim+kiffy" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://viralinfo.xyz/video/?v=mezzo+fun"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
|
johngreendr1/ef964c99-8e6c-4c48-ab94-8803096ec70a
|
johngreendr1
| 2025-06-23T18:42:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:lmsys/vicuna-7b-v1.3",
"base_model:adapter:lmsys/vicuna-7b-v1.3",
"region:us"
] | null | 2025-06-23T15:40:24Z |
---
base_model: lmsys/vicuna-7b-v1.3
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
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### Framework versions
- PEFT 0.15.1
|
minhduongqo/qwen2-7b-instruct-trl-sft-ChartQA
|
minhduongqo
| 2025-06-23T18:41:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T18:17:53Z |
---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: qwen2-7b-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2-7b-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="minhduongqo/qwen2-7b-instruct-trl-sft-ChartQA", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/minhduongqo-university-of-science-and-technology-of-hano/qwen2.5-3b-instruct-trl-sft-fire/runs/o4iwoemm)
This model was trained with SFT.
### Framework versions
- TRL: 0.20.0.dev0
- Transformers: 4.53.0.dev0
- Pytorch: 2.7.1+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
gork-projects/dqn-SpaceInvadersNoFrameskip-v4
|
gork-projects
| 2025-06-23T18:39:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-23T18:39:13Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 616.00 +/- 106.63
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga gork-projects -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga gork-projects -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga gork-projects
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
goodcasper/see_ai_rt-detr_r50_4090_only_bbox_da
|
goodcasper
| 2025-06-23T18:39:24Z | 62 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"rt_detr",
"object-detection",
"generated_from_trainer",
"base_model:PekingU/rtdetr_r50vd_coco_o365",
"base_model:finetune:PekingU/rtdetr_r50vd_coco_o365",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2025-06-22T17:06:34Z |
---
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_r50vd_coco_o365
tags:
- generated_from_trainer
model-index:
- name: see_ai_rt-detr_r50_4090_only_bbox_da
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. -->
# see_ai_rt-detr_r50_4090_only_bbox_da
This model is a fine-tuned version of [PekingU/rtdetr_r50vd_coco_o365](https://huggingface.co/PekingU/rtdetr_r50vd_coco_o365) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 17.4908
- Map: 0.2719
- Map 50: 0.4767
- Map 75: 0.2675
- Map Small: 0.0014
- Map Medium: 0.1362
- Map Large: 0.2914
- Mar 1: 0.3967
- Mar 10: 0.5302
- Mar 100: 0.5565
- Mar Small: 0.25
- Mar Medium: 0.286
- Mar Large: 0.5905
- Map Angiodysplasia: 0.1248
- Mar 100 Angiodysplasia: 0.4745
- Map Erosion: 0.2196
- Mar 100 Erosion: 0.4431
- Map Stenosis: 0.3631
- Mar 100 Stenosis: 0.8125
- Map Lymphangiectasia: 0.2679
- Mar 100 Lymphangiectasia: 0.46
- Map Lymph follicle: 0.1464
- Mar 100 Lymph follicle: 0.3646
- Map Smt: 0.3574
- Mar 100 Smt: 0.6607
- Map Polyp-like: 0.3597
- Mar 100 Polyp-like: 0.5619
- Map Bleeding: 0.3614
- Mar 100 Bleeding: 0.7
- Map Diverticulum: 0.0054
- Mar 100 Diverticulum: 0.3
- Map Erythema: 0.183
- Mar 100 Erythema: 0.6854
- Map Foreign body: 0.3705
- Mar 100 Foreign body: 0.564
- Map Vein: 0.5042
- Mar 100 Vein: 0.6511
## 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: 1
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 75
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Angiodysplasia | Mar 100 Angiodysplasia | Map Erosion | Mar 100 Erosion | Map Stenosis | Mar 100 Stenosis | Map Lymphangiectasia | Mar 100 Lymphangiectasia | Map Lymph follicle | Mar 100 Lymph follicle | Map Smt | Mar 100 Smt | Map Polyp-like | Mar 100 Polyp-like | Map Bleeding | Mar 100 Bleeding | Map Diverticulum | Mar 100 Diverticulum | Map Erythema | Mar 100 Erythema | Map Foreign body | Mar 100 Foreign body | Map Vein | Mar 100 Vein |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------------:|:----------------------:|:-----------:|:---------------:|:------------:|:----------------:|:--------------------:|:------------------------:|:------------------:|:----------------------:|:-------:|:-----------:|:--------------:|:------------------:|:------------:|:----------------:|:----------------:|:--------------------:|:------------:|:----------------:|:----------------:|:--------------------:|:--------:|:------------:|
| 36.7375 | 1.0 | 2464 | 17.4288 | 0.0793 | 0.1612 | 0.0654 | 0.0249 | 0.0567 | 0.0861 | 0.2805 | 0.4859 | 0.5824 | 0.0481 | 0.4659 | 0.6207 | 0.0286 | 0.3955 | 0.1208 | 0.563 | 0.0509 | 0.6769 | 0.0299 | 0.5531 | 0.097 | 0.4817 | 0.0591 | 0.7275 | 0.2281 | 0.6115 | 0.0509 | 0.7074 | 0.0007 | 0.4667 | 0.0493 | 0.7009 | 0.1436 | 0.55 | 0.0924 | 0.5544 |
| 20.3177 | 2.0 | 4928 | 16.0153 | 0.1551 | 0.3063 | 0.1325 | 0.0243 | 0.1101 | 0.1722 | 0.3193 | 0.4999 | 0.5844 | 0.0667 | 0.4744 | 0.6213 | 0.0769 | 0.4618 | 0.1706 | 0.568 | 0.1688 | 0.7615 | 0.0358 | 0.575 | 0.1216 | 0.4798 | 0.1063 | 0.6843 | 0.2509 | 0.62 | 0.28 | 0.6975 | 0.0003 | 0.3333 | 0.0567 | 0.6982 | 0.2392 | 0.5748 | 0.3544 | 0.5583 |
| 17.8371 | 3.0 | 7392 | 15.7262 | 0.186 | 0.3547 | 0.1639 | 0.0306 | 0.1322 | 0.2062 | 0.3141 | 0.5003 | 0.5753 | 0.0519 | 0.4188 | 0.6184 | 0.0856 | 0.4225 | 0.2004 | 0.539 | 0.234 | 0.7712 | 0.042 | 0.5172 | 0.1217 | 0.462 | 0.2314 | 0.7275 | 0.2771 | 0.6146 | 0.3097 | 0.7148 | 0.0002 | 0.2333 | 0.0872 | 0.7411 | 0.2792 | 0.5824 | 0.3638 | 0.5777 |
| 16.53 | 4.0 | 9856 | 16.0175 | 0.1864 | 0.3703 | 0.1605 | 0.0276 | 0.1168 | 0.207 | 0.3069 | 0.5254 | 0.6018 | 0.0624 | 0.4752 | 0.6355 | 0.1053 | 0.4303 | 0.1896 | 0.5507 | 0.2278 | 0.8115 | 0.0383 | 0.5406 | 0.1227 | 0.4777 | 0.1999 | 0.6765 | 0.3009 | 0.6214 | 0.2885 | 0.7358 | 0.0037 | 0.45 | 0.0936 | 0.733 | 0.323 | 0.5954 | 0.3442 | 0.599 |
| 15.6505 | 5.0 | 12320 | 16.0482 | 0.2082 | 0.3992 | 0.1859 | 0.0486 | 0.1299 | 0.2247 | 0.3306 | 0.5355 | 0.6075 | 0.1354 | 0.4619 | 0.643 | 0.097 | 0.4674 | 0.2165 | 0.5676 | 0.3178 | 0.7885 | 0.0921 | 0.6094 | 0.1228 | 0.4956 | 0.3136 | 0.7529 | 0.3128 | 0.618 | 0.2606 | 0.7012 | 0.0005 | 0.35 | 0.0621 | 0.7188 | 0.3309 | 0.6029 | 0.372 | 0.6175 |
| 14.9889 | 6.0 | 14784 | 16.0843 | 0.2026 | 0.3928 | 0.1755 | 0.0399 | 0.1462 | 0.2216 | 0.3519 | 0.5283 | 0.6163 | 0.0905 | 0.498 | 0.6523 | 0.0892 | 0.4719 | 0.2386 | 0.5612 | 0.3312 | 0.7769 | 0.081 | 0.6266 | 0.129 | 0.487 | 0.2279 | 0.7627 | 0.3235 | 0.6132 | 0.267 | 0.7333 | 0.0031 | 0.4333 | 0.0819 | 0.7018 | 0.3317 | 0.5899 | 0.3276 | 0.6379 |
| 14.4486 | 7.0 | 17248 | 16.2556 | 0.2243 | 0.4179 | 0.211 | 0.0386 | 0.1455 | 0.2445 | 0.3473 | 0.5382 | 0.609 | 0.0931 | 0.4777 | 0.6496 | 0.1027 | 0.4787 | 0.2391 | 0.5612 | 0.4337 | 0.7885 | 0.0606 | 0.6062 | 0.1317 | 0.4634 | 0.2658 | 0.7569 | 0.3302 | 0.5927 | 0.2732 | 0.7099 | 0.0033 | 0.4167 | 0.0903 | 0.7286 | 0.3465 | 0.5798 | 0.4139 | 0.6252 |
| 13.9808 | 8.0 | 19712 | 16.2233 | 0.2261 | 0.4265 | 0.2116 | 0.0225 | 0.1453 | 0.2468 | 0.3696 | 0.5456 | 0.628 | 0.0905 | 0.4871 | 0.6653 | 0.1063 | 0.4663 | 0.24 | 0.5639 | 0.3817 | 0.7846 | 0.1127 | 0.6203 | 0.1294 | 0.4836 | 0.3068 | 0.7902 | 0.3259 | 0.6259 | 0.2842 | 0.7185 | 0.0106 | 0.5333 | 0.0933 | 0.7214 | 0.3356 | 0.5857 | 0.3863 | 0.6417 |
| 13.6172 | 9.0 | 22176 | 16.6177 | 0.219 | 0.4109 | 0.1995 | 0.035 | 0.1578 | 0.2383 | 0.3691 | 0.548 | 0.6113 | 0.0995 | 0.4822 | 0.6452 | 0.0883 | 0.5 | 0.2331 | 0.5584 | 0.3703 | 0.7904 | 0.0913 | 0.5984 | 0.1505 | 0.4833 | 0.274 | 0.8137 | 0.3025 | 0.6025 | 0.3182 | 0.7 | 0.0084 | 0.4167 | 0.0716 | 0.6973 | 0.3243 | 0.5576 | 0.3958 | 0.6175 |
| 13.2263 | 10.0 | 24640 | 16.8541 | 0.2235 | 0.4183 | 0.2065 | 0.038 | 0.1459 | 0.2459 | 0.3698 | 0.5526 | 0.616 | 0.0873 | 0.474 | 0.6576 | 0.0837 | 0.491 | 0.2204 | 0.5383 | 0.3477 | 0.7808 | 0.0893 | 0.6172 | 0.1206 | 0.4823 | 0.3469 | 0.8039 | 0.3296 | 0.5972 | 0.2981 | 0.7185 | 0.0129 | 0.4833 | 0.1219 | 0.6964 | 0.3233 | 0.542 | 0.388 | 0.6408 |
| 12.9138 | 11.0 | 27104 | 16.9201 | 0.2179 | 0.4082 | 0.1933 | 0.0321 | 0.1346 | 0.2426 | 0.3684 | 0.5446 | 0.6176 | 0.0735 | 0.4892 | 0.6578 | 0.0951 | 0.4888 | 0.2127 | 0.55 | 0.3637 | 0.8038 | 0.07 | 0.5953 | 0.1103 | 0.4666 | 0.3145 | 0.7137 | 0.315 | 0.5918 | 0.2899 | 0.737 | 0.0017 | 0.55 | 0.1163 | 0.7259 | 0.3202 | 0.5529 | 0.4052 | 0.635 |
| 12.6214 | 12.0 | 29568 | 16.9798 | 0.2248 | 0.4284 | 0.2014 | 0.0265 | 0.1331 | 0.2493 | 0.3567 | 0.5339 | 0.5992 | 0.0852 | 0.4716 | 0.6401 | 0.0849 | 0.4449 | 0.2083 | 0.5386 | 0.3916 | 0.7962 | 0.1846 | 0.6078 | 0.1425 | 0.4685 | 0.2674 | 0.7529 | 0.3087 | 0.5825 | 0.2753 | 0.7123 | 0.0027 | 0.4333 | 0.1171 | 0.7 | 0.3215 | 0.5391 | 0.3933 | 0.6146 |
| 12.3319 | 13.0 | 32032 | 17.3638 | 0.2203 | 0.4075 | 0.1991 | 0.0268 | 0.1306 | 0.2429 | 0.3494 | 0.5344 | 0.5951 | 0.063 | 0.4347 | 0.6398 | 0.0752 | 0.4764 | 0.214 | 0.5246 | 0.3817 | 0.8019 | 0.0994 | 0.5531 | 0.1169 | 0.4408 | 0.3181 | 0.7529 | 0.3079 | 0.5668 | 0.296 | 0.721 | 0.0029 | 0.4667 | 0.1283 | 0.6946 | 0.307 | 0.5244 | 0.3956 | 0.6175 |
| 12.0997 | 14.0 | 34496 | 17.3007 | 0.2159 | 0.4033 | 0.1931 | 0.034 | 0.1221 | 0.2366 | 0.3544 | 0.5315 | 0.5978 | 0.0677 | 0.4412 | 0.6368 | 0.0715 | 0.4326 | 0.1871 | 0.524 | 0.4016 | 0.8077 | 0.0704 | 0.5547 | 0.1378 | 0.466 | 0.2739 | 0.7235 | 0.3052 | 0.5561 | 0.2765 | 0.7025 | 0.002 | 0.5333 | 0.1458 | 0.6982 | 0.3191 | 0.5685 | 0.4002 | 0.6068 |
| 11.8886 | 15.0 | 36960 | 17.3297 | 0.2179 | 0.407 | 0.2028 | 0.0337 | 0.1357 | 0.2392 | 0.3508 | 0.5323 | 0.5952 | 0.0921 | 0.4713 | 0.6312 | 0.0598 | 0.4539 | 0.2118 | 0.5044 | 0.4114 | 0.8115 | 0.0653 | 0.5906 | 0.1455 | 0.4533 | 0.2476 | 0.7686 | 0.3284 | 0.5656 | 0.2914 | 0.6889 | 0.0011 | 0.45 | 0.1148 | 0.6759 | 0.3296 | 0.5571 | 0.4081 | 0.6223 |
| 11.6726 | 16.0 | 39424 | 17.3828 | 0.2207 | 0.4132 | 0.2076 | 0.0163 | 0.1598 | 0.2383 | 0.3757 | 0.5485 | 0.595 | 0.0444 | 0.3999 | 0.6425 | 0.076 | 0.4416 | 0.2027 | 0.5409 | 0.4073 | 0.7942 | 0.1127 | 0.5297 | 0.1192 | 0.4213 | 0.281 | 0.7627 | 0.3042 | 0.5169 | 0.3189 | 0.7025 | 0.0039 | 0.65 | 0.1225 | 0.6732 | 0.2836 | 0.4929 | 0.4167 | 0.6146 |
| 11.4876 | 17.0 | 41888 | 17.4328 | 0.2231 | 0.4086 | 0.2195 | 0.0222 | 0.1354 | 0.2419 | 0.3683 | 0.5259 | 0.5772 | 0.0714 | 0.4245 | 0.6192 | 0.0753 | 0.4438 | 0.2135 | 0.5135 | 0.4319 | 0.7981 | 0.098 | 0.5609 | 0.1229 | 0.4523 | 0.2941 | 0.7451 | 0.3087 | 0.5682 | 0.3022 | 0.6975 | 0.0011 | 0.35 | 0.1193 | 0.6446 | 0.3166 | 0.5315 | 0.3935 | 0.6214 |
| 11.2952 | 18.0 | 44352 | 17.6459 | 0.221 | 0.4029 | 0.2026 | 0.0232 | 0.1308 | 0.2459 | 0.3631 | 0.5436 | 0.5922 | 0.0751 | 0.4237 | 0.6314 | 0.0589 | 0.4427 | 0.1924 | 0.5231 | 0.3853 | 0.7846 | 0.0631 | 0.525 | 0.1373 | 0.4316 | 0.2884 | 0.7529 | 0.2957 | 0.5417 | 0.3202 | 0.7025 | 0.0134 | 0.5833 | 0.1616 | 0.6554 | 0.2967 | 0.5235 | 0.4387 | 0.6398 |
| 11.1206 | 19.0 | 46816 | 17.6613 | 0.2119 | 0.3963 | 0.1975 | 0.0167 | 0.1306 | 0.2352 | 0.3559 | 0.5226 | 0.5686 | 0.0296 | 0.4262 | 0.6093 | 0.0605 | 0.4157 | 0.1994 | 0.5062 | 0.3572 | 0.7788 | 0.0695 | 0.5266 | 0.1262 | 0.4247 | 0.2347 | 0.7412 | 0.3235 | 0.5437 | 0.323 | 0.7 | 0.0201 | 0.3833 | 0.1301 | 0.6786 | 0.2987 | 0.5261 | 0.4001 | 0.5981 |
| 10.9505 | 20.0 | 49280 | 17.7590 | 0.2269 | 0.4161 | 0.2232 | 0.0132 | 0.1455 | 0.2464 | 0.3639 | 0.5183 | 0.5646 | 0.0466 | 0.439 | 0.6052 | 0.0639 | 0.4258 | 0.1908 | 0.4833 | 0.4264 | 0.7923 | 0.1155 | 0.5641 | 0.1078 | 0.4096 | 0.2965 | 0.7608 | 0.3221 | 0.5434 | 0.2961 | 0.6951 | 0.0394 | 0.3 | 0.1535 | 0.6607 | 0.3133 | 0.5399 | 0.3977 | 0.6 |
| 10.7857 | 21.0 | 51744 | 18.1314 | 0.2207 | 0.4115 | 0.2129 | 0.0325 | 0.1255 | 0.2482 | 0.3641 | 0.524 | 0.5674 | 0.037 | 0.4027 | 0.6057 | 0.0551 | 0.436 | 0.1841 | 0.4648 | 0.4 | 0.8077 | 0.123 | 0.5453 | 0.1363 | 0.429 | 0.2892 | 0.698 | 0.2919 | 0.5132 | 0.3189 | 0.6691 | 0.0032 | 0.4667 | 0.1499 | 0.6402 | 0.3061 | 0.5395 | 0.3905 | 0.599 |
| 10.629 | 22.0 | 54208 | 17.9638 | 0.2257 | 0.4097 | 0.215 | 0.0378 | 0.1458 | 0.2467 | 0.3662 | 0.5151 | 0.5618 | 0.0704 | 0.4085 | 0.6055 | 0.0647 | 0.4494 | 0.1818 | 0.4815 | 0.4016 | 0.7904 | 0.1436 | 0.5609 | 0.1299 | 0.4263 | 0.3334 | 0.7431 | 0.3007 | 0.5307 | 0.2869 | 0.6778 | 0.0077 | 0.3 | 0.1911 | 0.6857 | 0.2945 | 0.5185 | 0.3718 | 0.5777 |
| 10.4715 | 23.0 | 56672 | 17.6242 | 0.2303 | 0.4311 | 0.2137 | 0.0174 | 0.1342 | 0.2574 | 0.3819 | 0.5384 | 0.583 | 0.0667 | 0.3883 | 0.626 | 0.065 | 0.3921 | 0.1974 | 0.4947 | 0.4398 | 0.8058 | 0.143 | 0.5125 | 0.1272 | 0.4226 | 0.3159 | 0.7275 | 0.3216 | 0.5532 | 0.2732 | 0.684 | 0.0088 | 0.6333 | 0.177 | 0.6741 | 0.2948 | 0.5029 | 0.4005 | 0.5932 |
| 10.3338 | 24.0 | 59136 | 18.1177 | 0.2262 | 0.4256 | 0.2067 | 0.0258 | 0.1321 | 0.2489 | 0.3616 | 0.5109 | 0.55 | 0.037 | 0.3944 | 0.5887 | 0.0541 | 0.3944 | 0.1753 | 0.4367 | 0.4345 | 0.7981 | 0.1748 | 0.4891 | 0.1066 | 0.4025 | 0.3387 | 0.7294 | 0.2987 | 0.5107 | 0.2939 | 0.6728 | 0.0044 | 0.4333 | 0.1924 | 0.6455 | 0.2576 | 0.4996 | 0.3835 | 0.5874 |
| 10.1916 | 25.0 | 61600 | 17.8994 | 0.2282 | 0.4232 | 0.2116 | 0.0247 | 0.1446 | 0.2506 | 0.3693 | 0.5227 | 0.5596 | 0.063 | 0.3813 | 0.6015 | 0.0732 | 0.3787 | 0.186 | 0.466 | 0.3678 | 0.8135 | 0.1846 | 0.5516 | 0.105 | 0.4038 | 0.3647 | 0.7314 | 0.3092 | 0.5124 | 0.3004 | 0.658 | 0.0166 | 0.45 | 0.1428 | 0.642 | 0.2905 | 0.5277 | 0.3981 | 0.5796 |
| 10.0803 | 26.0 | 64064 | 17.5562 | 0.2306 | 0.4373 | 0.2075 | 0.017 | 0.1563 | 0.2545 | 0.3493 | 0.5088 | 0.551 | 0.0556 | 0.416 | 0.591 | 0.0507 | 0.4146 | 0.2201 | 0.4649 | 0.4081 | 0.8058 | 0.2054 | 0.5391 | 0.162 | 0.421 | 0.2929 | 0.7863 | 0.3145 | 0.5366 | 0.2611 | 0.684 | 0.0012 | 0.1833 | 0.1509 | 0.6652 | 0.2963 | 0.5155 | 0.4045 | 0.5951 |
| 9.9708 | 27.0 | 66528 | 17.9700 | 0.225 | 0.4194 | 0.2113 | 0.0181 | 0.1326 | 0.2503 | 0.3612 | 0.5003 | 0.5353 | 0.0407 | 0.3732 | 0.579 | 0.0628 | 0.373 | 0.1765 | 0.434 | 0.3772 | 0.8115 | 0.2106 | 0.5016 | 0.1366 | 0.4018 | 0.2915 | 0.7 | 0.3158 | 0.5321 | 0.2934 | 0.6506 | 0.0059 | 0.3 | 0.1515 | 0.6545 | 0.2781 | 0.5 | 0.4002 | 0.5641 |
| 9.854 | 28.0 | 68992 | 18.0467 | 0.237 | 0.4345 | 0.2287 | 0.0142 | 0.147 | 0.262 | 0.3641 | 0.4925 | 0.5252 | 0.0407 | 0.3728 | 0.5611 | 0.0628 | 0.3101 | 0.1745 | 0.4308 | 0.4756 | 0.8019 | 0.2357 | 0.5109 | 0.1288 | 0.3835 | 0.3471 | 0.7216 | 0.2968 | 0.4961 | 0.2537 | 0.6383 | 0.0028 | 0.2833 | 0.1737 | 0.6187 | 0.2863 | 0.5244 | 0.4058 | 0.5825 |
| 9.7489 | 29.0 | 71456 | 17.6774 | 0.2387 | 0.4427 | 0.2208 | 0.0088 | 0.1536 | 0.2596 | 0.3649 | 0.5019 | 0.5401 | 0.0481 | 0.3907 | 0.576 | 0.0676 | 0.3753 | 0.1884 | 0.4681 | 0.4043 | 0.8019 | 0.2346 | 0.5484 | 0.1473 | 0.4097 | 0.3545 | 0.7059 | 0.306 | 0.4986 | 0.325 | 0.6617 | 0.0033 | 0.2833 | 0.1348 | 0.633 | 0.2953 | 0.521 | 0.4036 | 0.5738 |
| 9.635 | 30.0 | 73920 | 17.8968 | 0.2251 | 0.4255 | 0.2137 | 0.02 | 0.1438 | 0.2494 | 0.355 | 0.493 | 0.526 | 0.0556 | 0.3609 | 0.5651 | 0.057 | 0.3236 | 0.1929 | 0.4391 | 0.3518 | 0.7692 | 0.2207 | 0.5203 | 0.1317 | 0.3852 | 0.3017 | 0.7176 | 0.3076 | 0.5107 | 0.2939 | 0.6519 | 0.0007 | 0.2667 | 0.1519 | 0.642 | 0.2864 | 0.5105 | 0.4052 | 0.5748 |
| 9.5233 | 31.0 | 76384 | 18.0248 | 0.2168 | 0.4154 | 0.1972 | 0.0315 | 0.1371 | 0.2386 | 0.3649 | 0.5082 | 0.5408 | 0.0444 | 0.3672 | 0.5804 | 0.0582 | 0.3202 | 0.2163 | 0.4427 | 0.3304 | 0.7635 | 0.2273 | 0.5031 | 0.1432 | 0.4 | 0.268 | 0.6863 | 0.2917 | 0.5124 | 0.2976 | 0.658 | 0.0039 | 0.55 | 0.1237 | 0.6286 | 0.2705 | 0.4773 | 0.3705 | 0.5476 |
| 9.4036 | 32.0 | 78848 | 18.2855 | 0.2297 | 0.4319 | 0.2113 | 0.021 | 0.1516 | 0.2505 | 0.3649 | 0.4933 | 0.525 | 0.0444 | 0.378 | 0.5602 | 0.0857 | 0.3483 | 0.1821 | 0.4226 | 0.3646 | 0.7962 | 0.25 | 0.5078 | 0.1364 | 0.366 | 0.2999 | 0.698 | 0.3041 | 0.4808 | 0.3018 | 0.6778 | 0.0033 | 0.3167 | 0.1754 | 0.6268 | 0.2802 | 0.5059 | 0.3722 | 0.5534 |
| 9.3106 | 33.0 | 81312 | 18.3123 | 0.226 | 0.4156 | 0.2128 | 0.0071 | 0.1517 | 0.2513 | 0.35 | 0.4849 | 0.5177 | 0.0296 | 0.384 | 0.5542 | 0.0662 | 0.3079 | 0.1798 | 0.4066 | 0.3859 | 0.7788 | 0.2607 | 0.5031 | 0.1216 | 0.3675 | 0.2769 | 0.7157 | 0.3122 | 0.5051 | 0.3001 | 0.6494 | 0.0009 | 0.2833 | 0.1659 | 0.6134 | 0.2873 | 0.5218 | 0.3551 | 0.5602 |
| 9.2202 | 34.0 | 83776 | 17.9811 | 0.2406 | 0.4421 | 0.2314 | 0.0261 | 0.1501 | 0.2635 | 0.3669 | 0.4963 | 0.5255 | 0.0492 | 0.3794 | 0.5629 | 0.0736 | 0.3404 | 0.2004 | 0.427 | 0.4003 | 0.7885 | 0.2564 | 0.4969 | 0.1352 | 0.3674 | 0.3663 | 0.702 | 0.3228 | 0.5017 | 0.296 | 0.6654 | 0.0015 | 0.35 | 0.1508 | 0.6286 | 0.278 | 0.4891 | 0.4056 | 0.5485 |
| 9.1235 | 35.0 | 86240 | 18.1391 | 0.2334 | 0.4348 | 0.2164 | 0.0136 | 0.1584 | 0.2589 | 0.3611 | 0.4928 | 0.5222 | 0.0492 | 0.3608 | 0.5627 | 0.0919 | 0.3247 | 0.1922 | 0.423 | 0.3403 | 0.7769 | 0.267 | 0.5063 | 0.1311 | 0.3742 | 0.32 | 0.7137 | 0.3139 | 0.5079 | 0.2973 | 0.637 | 0.0148 | 0.3667 | 0.1643 | 0.6009 | 0.2823 | 0.4756 | 0.386 | 0.5592 |
| 9.0386 | 36.0 | 88704 | 18.2431 | 0.2358 | 0.4347 | 0.2275 | 0.0154 | 0.1486 | 0.2604 | 0.3655 | 0.4861 | 0.5143 | 0.0455 | 0.3666 | 0.5536 | 0.0657 | 0.3146 | 0.1886 | 0.4133 | 0.4145 | 0.7865 | 0.2599 | 0.4906 | 0.1247 | 0.3507 | 0.3053 | 0.7255 | 0.3016 | 0.4789 | 0.3202 | 0.6481 | 0.0132 | 0.3333 | 0.1583 | 0.5732 | 0.2863 | 0.4853 | 0.391 | 0.5718 |
| 8.935 | 37.0 | 91168 | 18.1761 | 0.2311 | 0.4302 | 0.2174 | 0.0092 | 0.1519 | 0.2574 | 0.3617 | 0.4867 | 0.5167 | 0.037 | 0.3701 | 0.5553 | 0.0652 | 0.2978 | 0.1889 | 0.4132 | 0.3767 | 0.7577 | 0.2676 | 0.4859 | 0.1411 | 0.3715 | 0.312 | 0.7176 | 0.305 | 0.4817 | 0.2671 | 0.6494 | 0.0015 | 0.3667 | 0.1766 | 0.6375 | 0.2746 | 0.4655 | 0.3966 | 0.5553 |
| 8.8224 | 38.0 | 93632 | 17.9488 | 0.2472 | 0.4556 | 0.2285 | 0.0234 | 0.1545 | 0.2731 | 0.3474 | 0.4852 | 0.5166 | 0.0407 | 0.3463 | 0.5587 | 0.0709 | 0.3079 | 0.2007 | 0.444 | 0.4104 | 0.7731 | 0.2948 | 0.4797 | 0.1513 | 0.3799 | 0.3279 | 0.7059 | 0.3087 | 0.4808 | 0.2911 | 0.6494 | 0.0006 | 0.2833 | 0.2113 | 0.6357 | 0.2862 | 0.4937 | 0.4128 | 0.566 |
| 8.7433 | 39.0 | 96096 | 18.1127 | 0.2357 | 0.4324 | 0.2243 | 0.0235 | 0.1369 | 0.2616 | 0.3564 | 0.4798 | 0.5089 | 0.0407 | 0.343 | 0.548 | 0.071 | 0.3022 | 0.1846 | 0.4162 | 0.3954 | 0.7577 | 0.2799 | 0.5047 | 0.1232 | 0.3386 | 0.2932 | 0.702 | 0.323 | 0.5054 | 0.3176 | 0.6222 | 0.0019 | 0.2667 | 0.1799 | 0.6429 | 0.2762 | 0.4828 | 0.3821 | 0.566 |
| 8.6435 | 40.0 | 98560 | 18.0182 | 0.2409 | 0.4364 | 0.2275 | 0.0168 | 0.1467 | 0.2657 | 0.3467 | 0.466 | 0.4972 | 0.0481 | 0.3267 | 0.5367 | 0.0525 | 0.2854 | 0.1843 | 0.4091 | 0.4296 | 0.7827 | 0.2824 | 0.4953 | 0.142 | 0.3505 | 0.3375 | 0.7039 | 0.3094 | 0.4837 | 0.3214 | 0.6383 | 0.0003 | 0.1667 | 0.1753 | 0.6223 | 0.2759 | 0.4651 | 0.38 | 0.5631 |
| 8.5531 | 41.0 | 101024 | 18.2803 | 0.2239 | 0.4147 | 0.2079 | 0.0114 | 0.1387 | 0.2482 | 0.3502 | 0.4707 | 0.4972 | 0.0333 | 0.2693 | 0.5374 | 0.0416 | 0.2685 | 0.171 | 0.4117 | 0.4009 | 0.7846 | 0.2388 | 0.4734 | 0.1261 | 0.334 | 0.2777 | 0.6706 | 0.2877 | 0.4625 | 0.2865 | 0.6321 | 0.0036 | 0.3 | 0.197 | 0.6143 | 0.2791 | 0.4693 | 0.3766 | 0.5447 |
| 8.4525 | 42.0 | 103488 | 17.8710 | 0.236 | 0.4381 | 0.2212 | 0.0159 | 0.133 | 0.2638 | 0.3574 | 0.4849 | 0.509 | 0.0418 | 0.3589 | 0.55 | 0.0499 | 0.2798 | 0.1742 | 0.4171 | 0.438 | 0.7827 | 0.2867 | 0.4828 | 0.1365 | 0.3412 | 0.2918 | 0.7294 | 0.3035 | 0.5 | 0.2964 | 0.6358 | 0.0065 | 0.2833 | 0.1907 | 0.6304 | 0.2792 | 0.458 | 0.3792 | 0.568 |
| 8.3652 | 43.0 | 105952 | 18.3198 | 0.232 | 0.4284 | 0.2225 | 0.0121 | 0.143 | 0.2564 | 0.3369 | 0.4608 | 0.485 | 0.0407 | 0.3412 | 0.519 | 0.0521 | 0.2551 | 0.1652 | 0.382 | 0.402 | 0.7519 | 0.268 | 0.4922 | 0.1337 | 0.3298 | 0.3155 | 0.6647 | 0.3096 | 0.4904 | 0.3207 | 0.6444 | 0.0003 | 0.2167 | 0.1729 | 0.6 | 0.2684 | 0.4555 | 0.3756 | 0.5379 |
| 8.2996 | 44.0 | 108416 | 18.0853 | 0.2305 | 0.426 | 0.2116 | 0.0068 | 0.1437 | 0.2573 | 0.3471 | 0.4778 | 0.504 | 0.0296 | 0.3573 | 0.5393 | 0.0351 | 0.2933 | 0.1818 | 0.3947 | 0.4216 | 0.7538 | 0.2652 | 0.4609 | 0.1433 | 0.3325 | 0.3083 | 0.7294 | 0.3166 | 0.4918 | 0.2773 | 0.6074 | 0.0007 | 0.3 | 0.1635 | 0.6268 | 0.2698 | 0.4815 | 0.3829 | 0.5757 |
| 8.2003 | 45.0 | 110880 | 18.2149 | 0.2436 | 0.4412 | 0.2367 | 0.0267 | 0.1446 | 0.2703 | 0.3522 | 0.4739 | 0.5018 | 0.0529 | 0.3437 | 0.5393 | 0.0422 | 0.2742 | 0.1787 | 0.3991 | 0.4732 | 0.7788 | 0.3367 | 0.4766 | 0.1473 | 0.347 | 0.2871 | 0.702 | 0.2992 | 0.471 | 0.3106 | 0.6198 | 0.0031 | 0.3 | 0.1776 | 0.6304 | 0.2886 | 0.479 | 0.3792 | 0.5437 |
| 8.1182 | 46.0 | 113344 | 18.1238 | 0.2429 | 0.4392 | 0.2343 | 0.027 | 0.144 | 0.2676 | 0.3603 | 0.4891 | 0.5139 | 0.0407 | 0.3569 | 0.5501 | 0.0409 | 0.264 | 0.183 | 0.4084 | 0.4434 | 0.7712 | 0.2948 | 0.4828 | 0.1392 | 0.336 | 0.3265 | 0.7137 | 0.3052 | 0.4741 | 0.2991 | 0.6556 | 0.0018 | 0.4333 | 0.2239 | 0.6179 | 0.2747 | 0.4643 | 0.3828 | 0.5456 |
| 8.0393 | 47.0 | 115808 | 18.2298 | 0.2369 | 0.4248 | 0.2305 | 0.029 | 0.1484 | 0.2627 | 0.3534 | 0.4705 | 0.4935 | 0.0444 | 0.3275 | 0.5316 | 0.047 | 0.2674 | 0.1752 | 0.3881 | 0.4261 | 0.7462 | 0.2871 | 0.4812 | 0.1446 | 0.334 | 0.3008 | 0.698 | 0.3007 | 0.4752 | 0.3198 | 0.6321 | 0.0011 | 0.25 | 0.1825 | 0.6196 | 0.2781 | 0.4752 | 0.3792 | 0.5553 |
| 7.9654 | 48.0 | 118272 | 18.2678 | 0.2413 | 0.4443 | 0.231 | 0.0269 | 0.1492 | 0.2656 | 0.3486 | 0.4593 | 0.4799 | 0.0444 | 0.3418 | 0.5155 | 0.0463 | 0.2506 | 0.1914 | 0.3911 | 0.4243 | 0.7346 | 0.2905 | 0.4844 | 0.1541 | 0.3297 | 0.3161 | 0.6745 | 0.3005 | 0.458 | 0.3112 | 0.6173 | 0.0011 | 0.2 | 0.1989 | 0.617 | 0.2845 | 0.4609 | 0.3767 | 0.5408 |
| 7.8905 | 49.0 | 120736 | 18.1783 | 0.2383 | 0.4355 | 0.2229 | 0.0223 | 0.14 | 0.2641 | 0.3562 | 0.4788 | 0.5039 | 0.0481 | 0.3804 | 0.54 | 0.0699 | 0.2966 | 0.1827 | 0.4004 | 0.3947 | 0.7692 | 0.2896 | 0.4781 | 0.1559 | 0.347 | 0.3502 | 0.7196 | 0.2955 | 0.4777 | 0.2761 | 0.6247 | 0.0016 | 0.3 | 0.1653 | 0.6009 | 0.288 | 0.4672 | 0.3901 | 0.565 |
| 7.798 | 50.0 | 123200 | 18.1073 | 0.2351 | 0.4266 | 0.2281 | 0.0203 | 0.1493 | 0.2581 | 0.3534 | 0.4704 | 0.4944 | 0.0455 | 0.364 | 0.5281 | 0.0466 | 0.2607 | 0.1816 | 0.4027 | 0.4407 | 0.7615 | 0.3135 | 0.4766 | 0.1507 | 0.334 | 0.2962 | 0.7039 | 0.302 | 0.4848 | 0.2924 | 0.5988 | 0.001 | 0.3 | 0.1496 | 0.5991 | 0.2726 | 0.458 | 0.3748 | 0.5524 |
| 7.7137 | 51.0 | 125664 | 18.3562 | 0.2369 | 0.4348 | 0.2277 | 0.0244 | 0.1428 | 0.2637 | 0.3411 | 0.4516 | 0.4701 | 0.037 | 0.333 | 0.5052 | 0.0454 | 0.2528 | 0.1888 | 0.3849 | 0.4385 | 0.7673 | 0.2894 | 0.4547 | 0.1467 | 0.318 | 0.3107 | 0.6667 | 0.2966 | 0.4552 | 0.2931 | 0.5963 | 0.0003 | 0.15 | 0.1851 | 0.5982 | 0.2748 | 0.4513 | 0.3741 | 0.5456 |
| 7.6378 | 52.0 | 128128 | 18.1310 | 0.2316 | 0.4237 | 0.2153 | 0.014 | 0.1402 | 0.2578 | 0.3589 | 0.4615 | 0.4814 | 0.0407 | 0.3406 | 0.514 | 0.0431 | 0.2528 | 0.194 | 0.3941 | 0.4249 | 0.7346 | 0.2994 | 0.4578 | 0.1458 | 0.3083 | 0.2773 | 0.6647 | 0.2939 | 0.4586 | 0.2861 | 0.5963 | 0.0013 | 0.3167 | 0.1622 | 0.5929 | 0.2717 | 0.4563 | 0.3791 | 0.5437 |
| 7.5504 | 53.0 | 130592 | 18.1597 | 0.2361 | 0.432 | 0.2178 | 0.0099 | 0.1364 | 0.2627 | 0.3492 | 0.4503 | 0.4695 | 0.0333 | 0.3479 | 0.5042 | 0.0401 | 0.2483 | 0.1854 | 0.3758 | 0.4306 | 0.7442 | 0.3121 | 0.4531 | 0.1513 | 0.3165 | 0.3049 | 0.6647 | 0.3042 | 0.4693 | 0.28 | 0.6012 | 0.0009 | 0.1667 | 0.1673 | 0.5938 | 0.2769 | 0.4496 | 0.3791 | 0.5505 |
| 7.462 | 54.0 | 133056 | 18.0485 | 0.2416 | 0.4392 | 0.2287 | 0.0196 | 0.1492 | 0.268 | 0.3471 | 0.4506 | 0.468 | 0.0444 | 0.3464 | 0.503 | 0.0478 | 0.2449 | 0.1883 | 0.3843 | 0.4375 | 0.7346 | 0.2921 | 0.4594 | 0.1551 | 0.3185 | 0.3259 | 0.6686 | 0.3026 | 0.4577 | 0.2975 | 0.6062 | 0.0006 | 0.1833 | 0.1961 | 0.5946 | 0.2759 | 0.4424 | 0.3802 | 0.5214 |
| 7.3848 | 55.0 | 135520 | 18.1110 | 0.2396 | 0.4388 | 0.2202 | 0.0129 | 0.1464 | 0.2648 | 0.3357 | 0.452 | 0.4742 | 0.0407 | 0.3662 | 0.5064 | 0.0437 | 0.2494 | 0.1846 | 0.3831 | 0.4269 | 0.7462 | 0.3141 | 0.4703 | 0.1616 | 0.3303 | 0.3097 | 0.6392 | 0.2947 | 0.4617 | 0.3014 | 0.6235 | 0.0003 | 0.1833 | 0.182 | 0.6107 | 0.2848 | 0.4487 | 0.3709 | 0.5437 |
| 7.3162 | 56.0 | 137984 | 17.9914 | 0.2433 | 0.4424 | 0.2338 | 0.0096 | 0.1414 | 0.2711 | 0.3425 | 0.4576 | 0.4743 | 0.0296 | 0.3316 | 0.5091 | 0.0477 | 0.2472 | 0.1926 | 0.3872 | 0.4689 | 0.7558 | 0.2923 | 0.475 | 0.1554 | 0.326 | 0.3248 | 0.6725 | 0.2946 | 0.4566 | 0.2856 | 0.5963 | 0.0003 | 0.2 | 0.1876 | 0.5982 | 0.2754 | 0.4353 | 0.3941 | 0.5417 |
| 7.232 | 57.0 | 140448 | 18.0469 | 0.2385 | 0.4333 | 0.2214 | 0.0125 | 0.147 | 0.2651 | 0.3451 | 0.4529 | 0.473 | 0.0296 | 0.3585 | 0.506 | 0.0361 | 0.2371 | 0.188 | 0.3932 | 0.4271 | 0.7308 | 0.3078 | 0.4719 | 0.1656 | 0.3359 | 0.2963 | 0.6431 | 0.2876 | 0.4645 | 0.3159 | 0.6037 | 0.0003 | 0.2 | 0.1863 | 0.5982 | 0.2811 | 0.4538 | 0.3701 | 0.5437 |
| 7.1484 | 58.0 | 142912 | 18.0633 | 0.2361 | 0.432 | 0.221 | 0.014 | 0.1429 | 0.2628 | 0.3499 | 0.4586 | 0.4761 | 0.0333 | 0.3569 | 0.5071 | 0.0411 | 0.2427 | 0.1961 | 0.3859 | 0.4318 | 0.75 | 0.3074 | 0.4703 | 0.167 | 0.3319 | 0.2862 | 0.6686 | 0.2964 | 0.4715 | 0.2932 | 0.6136 | 0.0004 | 0.2 | 0.1596 | 0.6027 | 0.2764 | 0.4357 | 0.3775 | 0.5408 |
| 7.0693 | 59.0 | 145376 | 18.0175 | 0.2363 | 0.4356 | 0.2159 | 0.0186 | 0.1464 | 0.2598 | 0.3441 | 0.4543 | 0.473 | 0.0455 | 0.3556 | 0.5045 | 0.0461 | 0.2449 | 0.1872 | 0.3772 | 0.4297 | 0.7288 | 0.3042 | 0.4672 | 0.1687 | 0.3337 | 0.2802 | 0.6686 | 0.2892 | 0.4614 | 0.3064 | 0.6062 | 0.0003 | 0.1833 | 0.1694 | 0.6089 | 0.2834 | 0.4555 | 0.3706 | 0.5398 |
| 7.0055 | 60.0 | 147840 | 18.0684 | 0.239 | 0.4377 | 0.2215 | 0.0116 | 0.1438 | 0.2641 | 0.3484 | 0.4486 | 0.4682 | 0.0307 | 0.3403 | 0.5039 | 0.0365 | 0.2449 | 0.192 | 0.3829 | 0.4468 | 0.7423 | 0.3178 | 0.4719 | 0.1638 | 0.3284 | 0.3028 | 0.6608 | 0.2946 | 0.4713 | 0.3004 | 0.6062 | 0.0001 | 0.1333 | 0.164 | 0.6134 | 0.2752 | 0.4391 | 0.3742 | 0.5243 |
| 6.9295 | 61.0 | 150304 | 18.0705 | 0.2339 | 0.4312 | 0.217 | 0.0238 | 0.1421 | 0.2594 | 0.35 | 0.4508 | 0.4696 | 0.0492 | 0.3508 | 0.5023 | 0.0382 | 0.2449 | 0.2028 | 0.3899 | 0.4121 | 0.7442 | 0.3024 | 0.4719 | 0.1637 | 0.325 | 0.2891 | 0.6392 | 0.2917 | 0.4487 | 0.278 | 0.6025 | 0.0005 | 0.1833 | 0.1667 | 0.6009 | 0.2853 | 0.4542 | 0.3766 | 0.5301 |
| 6.836 | 62.0 | 152768 | 17.8876 | 0.2373 | 0.4352 | 0.2258 | 0.0241 | 0.1434 | 0.263 | 0.3509 | 0.4552 | 0.4739 | 0.0492 | 0.3558 | 0.5067 | 0.031 | 0.2427 | 0.1931 | 0.3907 | 0.4343 | 0.7288 | 0.2954 | 0.4563 | 0.1647 | 0.3238 | 0.3174 | 0.6824 | 0.2987 | 0.4721 | 0.2771 | 0.5988 | 0.0005 | 0.2167 | 0.1755 | 0.6036 | 0.2839 | 0.4395 | 0.3764 | 0.5311 |
| 6.7797 | 63.0 | 155232 | 17.8569 | 0.2412 | 0.4404 | 0.2247 | 0.0109 | 0.1422 | 0.2684 | 0.3444 | 0.4544 | 0.4733 | 0.0407 | 0.3556 | 0.5061 | 0.0359 | 0.2416 | 0.2057 | 0.4028 | 0.4436 | 0.7212 | 0.3142 | 0.475 | 0.1661 | 0.3256 | 0.3072 | 0.6647 | 0.2946 | 0.4594 | 0.2905 | 0.6049 | 0.0004 | 0.1833 | 0.1796 | 0.6125 | 0.2829 | 0.4525 | 0.3742 | 0.5359 |
| 6.6945 | 64.0 | 157696 | 17.8705 | 0.2341 | 0.429 | 0.2214 | 0.0152 | 0.1431 | 0.259 | 0.3491 | 0.4491 | 0.4673 | 0.0407 | 0.3551 | 0.5003 | 0.0291 | 0.2292 | 0.197 | 0.3872 | 0.4301 | 0.7288 | 0.2979 | 0.475 | 0.1587 | 0.3227 | 0.2963 | 0.6569 | 0.2928 | 0.458 | 0.2794 | 0.5988 | 0.001 | 0.1667 | 0.1674 | 0.5991 | 0.2811 | 0.4378 | 0.3786 | 0.5476 |
| 6.6298 | 65.0 | 160160 | 17.9224 | 0.2339 | 0.4342 | 0.2141 | 0.0123 | 0.1455 | 0.2581 | 0.3438 | 0.4487 | 0.4685 | 0.037 | 0.3528 | 0.5003 | 0.0379 | 0.2404 | 0.1979 | 0.3911 | 0.4306 | 0.7115 | 0.3016 | 0.475 | 0.1597 | 0.322 | 0.2728 | 0.6647 | 0.283 | 0.4504 | 0.2864 | 0.6 | 0.0005 | 0.1667 | 0.1767 | 0.6062 | 0.2841 | 0.4508 | 0.3758 | 0.5427 |
| 6.5719 | 66.0 | 162624 | 18.0197 | 0.237 | 0.4355 | 0.2261 | 0.018 | 0.1418 | 0.2624 | 0.3468 | 0.4484 | 0.4659 | 0.0407 | 0.3503 | 0.4995 | 0.0357 | 0.2427 | 0.1973 | 0.3819 | 0.4399 | 0.7327 | 0.3102 | 0.4734 | 0.1691 | 0.3315 | 0.2917 | 0.6294 | 0.2841 | 0.4439 | 0.2959 | 0.5975 | 0.001 | 0.2 | 0.1639 | 0.5955 | 0.28 | 0.4416 | 0.3752 | 0.5204 |
| 6.508 | 67.0 | 165088 | 17.9833 | 0.2372 | 0.4351 | 0.2242 | 0.0134 | 0.1456 | 0.2631 | 0.3417 | 0.4511 | 0.4693 | 0.037 | 0.3525 | 0.5024 | 0.0308 | 0.2449 | 0.1957 | 0.3909 | 0.4445 | 0.7269 | 0.2989 | 0.475 | 0.167 | 0.3292 | 0.294 | 0.6529 | 0.2906 | 0.4513 | 0.2917 | 0.5864 | 0.0005 | 0.2 | 0.1709 | 0.6054 | 0.2796 | 0.4324 | 0.3823 | 0.5359 |
| 6.4347 | 68.0 | 167552 | 17.9232 | 0.2345 | 0.434 | 0.2214 | 0.0187 | 0.1416 | 0.2601 | 0.3423 | 0.4518 | 0.471 | 0.037 | 0.3528 | 0.5039 | 0.0389 | 0.2371 | 0.1962 | 0.3884 | 0.4251 | 0.7308 | 0.2995 | 0.4719 | 0.1667 | 0.3248 | 0.2878 | 0.6549 | 0.2894 | 0.4541 | 0.289 | 0.6037 | 0.0004 | 0.2167 | 0.1666 | 0.592 | 0.2817 | 0.4496 | 0.3733 | 0.5282 |
| 6.3807 | 69.0 | 170016 | 17.9278 | 0.2362 | 0.434 | 0.2244 | 0.0204 | 0.1414 | 0.2606 | 0.3501 | 0.4543 | 0.4716 | 0.0407 | 0.3522 | 0.5037 | 0.0395 | 0.2393 | 0.1984 | 0.3907 | 0.4437 | 0.7346 | 0.289 | 0.475 | 0.1607 | 0.3151 | 0.2771 | 0.6647 | 0.2928 | 0.4575 | 0.2989 | 0.5914 | 0.001 | 0.2 | 0.1677 | 0.6071 | 0.2818 | 0.4416 | 0.3838 | 0.5417 |
| 6.3179 | 70.0 | 172480 | 17.9027 | 0.237 | 0.4348 | 0.2193 | 0.0235 | 0.139 | 0.2628 | 0.3477 | 0.4526 | 0.47 | 0.0492 | 0.3486 | 0.5037 | 0.0346 | 0.2438 | 0.1993 | 0.3989 | 0.4586 | 0.7115 | 0.2974 | 0.4656 | 0.17 | 0.3285 | 0.2745 | 0.6745 | 0.2886 | 0.4558 | 0.2906 | 0.5889 | 0.0004 | 0.2 | 0.1674 | 0.6027 | 0.278 | 0.4353 | 0.3841 | 0.535 |
| 6.2647 | 71.0 | 174944 | 17.8427 | 0.2373 | 0.4368 | 0.2238 | 0.0194 | 0.1438 | 0.2626 | 0.3473 | 0.4533 | 0.4721 | 0.0407 | 0.3564 | 0.5039 | 0.0377 | 0.2427 | 0.199 | 0.3906 | 0.4571 | 0.7192 | 0.2968 | 0.4703 | 0.1648 | 0.3282 | 0.2904 | 0.6549 | 0.2929 | 0.4569 | 0.2859 | 0.6049 | 0.0006 | 0.2167 | 0.1679 | 0.6 | 0.2807 | 0.4462 | 0.3738 | 0.535 |
| 6.2232 | 72.0 | 177408 | 17.8878 | 0.2382 | 0.4353 | 0.2262 | 0.0169 | 0.1411 | 0.2644 | 0.3486 | 0.4539 | 0.4723 | 0.037 | 0.3526 | 0.506 | 0.0407 | 0.2371 | 0.1994 | 0.3897 | 0.4558 | 0.7192 | 0.3061 | 0.4641 | 0.1625 | 0.3216 | 0.2851 | 0.6706 | 0.2938 | 0.4594 | 0.2894 | 0.6012 | 0.0004 | 0.2333 | 0.1666 | 0.6045 | 0.2781 | 0.4324 | 0.38 | 0.535 |
| 6.1743 | 73.0 | 179872 | 17.8326 | 0.24 | 0.4414 | 0.226 | 0.0138 | 0.1413 | 0.2654 | 0.3406 | 0.4428 | 0.4597 | 0.0333 | 0.3424 | 0.4935 | 0.0368 | 0.2371 | 0.2031 | 0.3931 | 0.4585 | 0.7135 | 0.3009 | 0.4578 | 0.1633 | 0.3201 | 0.2996 | 0.6588 | 0.2926 | 0.4566 | 0.2922 | 0.5852 | 0.0002 | 0.1333 | 0.1733 | 0.6018 | 0.2806 | 0.4273 | 0.3792 | 0.532 |
| 6.1401 | 74.0 | 182336 | 17.9263 | 0.2368 | 0.4367 | 0.2261 | 0.0192 | 0.1454 | 0.2627 | 0.3466 | 0.4448 | 0.462 | 0.0418 | 0.3448 | 0.4974 | 0.0404 | 0.2416 | 0.1971 | 0.3899 | 0.4372 | 0.7231 | 0.31 | 0.4781 | 0.1607 | 0.3177 | 0.2944 | 0.651 | 0.2886 | 0.4555 | 0.2882 | 0.579 | 0.0002 | 0.15 | 0.1674 | 0.5955 | 0.2772 | 0.4311 | 0.3798 | 0.532 |
| 6.1121 | 75.0 | 184800 | 17.8513 | 0.2395 | 0.4398 | 0.2287 | 0.0167 | 0.1467 | 0.2643 | 0.3466 | 0.4457 | 0.4635 | 0.037 | 0.345 | 0.4977 | 0.0328 | 0.236 | 0.2 | 0.394 | 0.448 | 0.7212 | 0.3077 | 0.4766 | 0.1616 | 0.3223 | 0.309 | 0.6647 | 0.2896 | 0.4552 | 0.2915 | 0.5901 | 0.0002 | 0.1333 | 0.1754 | 0.6027 | 0.2785 | 0.4286 | 0.3796 | 0.5369 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.1
|
memevis/fe0
|
memevis
| 2025-06-23T18:38:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T18:35:44Z |
---
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.
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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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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#### Summary
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- **Hardware Type:** [More Information Needed]
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|
ucatalin1/unsloth_test_llama_3.1_8b
|
ucatalin1
| 2025-06-23T18:36:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T18:36:40Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ucatalin1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-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)
|
yashm-cerebras/qwen3-actor-pointwise-8b
|
yashm-cerebras
| 2025-06-23T18:36:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T17:40:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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## Uses
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### 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
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#### 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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## Technical Specifications [optional]
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[More Information Needed]
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|
nrmmtr11878/nrmmtrlsbn4k
|
nrmmtr11878
| 2025-06-23T18:29:21Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T17:51:09Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: nrmmtrlsbn4k
---
# Nrmmtrlsbn4K
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `nrmmtrlsbn4k` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "nrmmtrlsbn4k",
"lora_weights": "https://huggingface.co/nrmmtr11878/nrmmtrlsbn4k/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('nrmmtr11878/nrmmtrlsbn4k', weight_name='lora.safetensors')
image = pipeline('nrmmtrlsbn4k').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/nrmmtr11878/nrmmtrlsbn4k/discussions) to add images that show off what you’ve made with this LoRA.
|
Hachipo/Qwen2.5-7B-MIFT-en_newbase_v2-PIFT-enja_10000_3
|
Hachipo
| 2025-06-23T18:28:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T18:25:26Z |
---
library_name: transformers
tags:
- trl
- 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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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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|
dgambettaphd/M_llm3_run0_gen8_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-06-23T18:28:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T18:28:02Z |
---
library_name: transformers
tags:
- unsloth
---
# 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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[More Information Needed]
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## Bias, Risks, and Limitations
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[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
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## 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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|
Hachipo/Qwen2.5-7B-MIFT-en_newbase_v2-PIFT-jaen_10000_3
|
Hachipo
| 2025-06-23T18:27:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T18:24:22Z |
---
library_name: transformers
tags:
- trl
- 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. -->
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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### Direct Use
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[More Information Needed]
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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]
### 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 -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Factors
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#### Metrics
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[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]
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[More Information Needed]
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|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shrewd_agile_quail
|
chinna6
| 2025-06-23T18:26:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am shrewd agile quail",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-15T00:17:43Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shrewd_agile_quail
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am shrewd agile quail
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shrewd_agile_quail
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shrewd_agile_quail", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
m-rudko-pn/e5-small-ukr-wikipedia
|
m-rudko-pn
| 2025-06-23T18:25:26Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:79912",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:intfloat/multilingual-e5-small",
"base_model:finetune:intfloat/multilingual-e5-small",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-23T18:24:58Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:79912
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-small
widget:
- source_sentence: Ендокринна система
sentences:
- Ендокринна система — це система залоз, які виділяють гормони. Гормони — це хімічні
речовини, які впливають на діяльність різних систем та органів в організмі (наприклад
гормон щитоподібної залози, гормон росту та інсулін). Ендокринна система включає
ряд механізмів зворотного зв'язку, тому часто один гормон (наприклад, тиреотропний
гормон) контролює дію або вивільнення іншого, вторинного гормону (наприклад, гормону
щитоподібної залози). Якщо вторинного гормону занадто багато, це може забезпечити
негативний зворотний зв'язок з первинним гормоном, для підтримки гомеостаз. У
початковому визначенні 1902 р. Бейліса та Старлінга, вони вказували, що гормон,
як хімічна речовина має вироблятися органом, вивільнятися (у невеликій кількості)
у кров та транспортуватися з током крові до віддаленого органу для виконання своєї
специфічної функції. Це визначення стосується більшості «класичних» гормонів,
але існують також паракринні механізми (хімічний зв'язок між клітинами в тканині
або органі), аутокринні (хімічна речовина, що діє на ту саму клітину) та внутрішньокринні
(хімічна речовина, що діє всередині та сама клітина). Нейроендокринний сигнал
— це «класичний» гормон, який виділяється в кров нейросекреторним нейроном.
- 'Народився 9 березня 1894 в селі Ременів, нині Кам''янка-Бузький район Львівська
область, в родині Кирила та Марії Сушків. У Романа Сушка було п''ятеро братів
та сестер: Василь ( 1875), Ганна ( 1878), Іван ( 1882), Юрій ( 1887), Пелагея
( 1890). Закінчив Народну школу в рідному селі та у 1913 році філію Академічної
гімназії у Львові, продовжив навчання на юридичному факультеті Львівського університету.'
- У науковій медицині використовують надземну частину — Herba bursae pastoris, яку
рекомендують проти різноманітних внутрішніх кровотеч (легеневих, ниркових, носових,
шлунково-кишкових і особливо маткових), а також при надмірних менструаціях. Препарати
грициків посилюють перистальтику кишки, рекомендуються при застуді, хворобах печінки
і нирок, сечового міхура, при порушенні обміну речовин, ревматизмі. У народній
медицині грицики використовують як кровоспинний засіб, при блюванні (токсикозі)
у вагітних жінок, при гіпертонічній хворобі, гастриті та виразці шлунка, при запаленнях
і піску в сечовому міхурі, при туберкульозі, простуді, геморої, жовчних каменях,
нетриманні сечі, жіночих хворобах, легкому перебігу шигельозу. Сік з свіжої рослини
п'ють при ревматизмі і проносах. Зовнішньо вживають для промивання ран, для компресів
або розтирання при пораненнях чи контузії. У гомеопатії використовують есенцію
з свіжої рослини. У ветеринарній практиці грицики застосовують при кривавих проносах
і сечі у великої рогатої худоби, при маткових кровотечах і послабленні тонусу
матки.
- source_sentence: Горобина звичайна
sentences:
- Ґрунти Нігеру досить бідні. На півночі Нігеру, кам'янистих плато і піщаних пустелях
ґрунтовий покрив практично відсутня. Тільки на ділянках, де з'являється вода,
колючі чагарники і посухостійкі злаки формують примітивні піщані ґрунти. На півдні
Нігеру, в Сахелі, поширення ґрунтів залежить від кількості вологи. В основному
це червоноземи і піщані ґрунти різної потужності. У піщаних ґрунтах Сахель мало
перегною, що робить їх уразливими до вітрової ерозії. На сході країни, в улоговині
озера Чад поширені солончаки. У долинах річок, ваді, і западинах, де збирається
вода, зустрічаються збагачені алювієм глинясті ґрунти, сприятливі для сільського
господарства. Для Нігеру характерний процес деградації та ерозії ґрунтів, що приводить
до опустелювання земель, тому боротьба за відновлення та збереження ґрунтів є
найважливішим завданням країни.
- 'Народився року в Царичанці (нині Дніпропетровська область, Україна) у сім''ї
сільського священника. По закінченню курсу в народній школі до 1896 року навчався
в Полтаві в місцевому духовному училищі та семінарії. Виявляв особливі здібності
до математики, історії та, пізніше, філософії. В обох цих закладах виховувався
за казенний рахунок, як і обидва його брати. У 1896–1900 роках навчався в Київській
духовній академії. У 1900 році вступив на юридичний факультет Тартуського (тоді
Юр''євського) університету, у 1901 році перевівся до Варшавського університету
на юридичний факультет, який закінчив 1904 року зі ступенем кандидата права та
з золотою медаллю за працю: «Сучасна заатлантична еміграція. Її причини та наслідки».
У 1904–1906 роках молодший редактор Варшавського статистичного комітету. З 1906
по 1909 рік — приват-доцент політекономії і статистики Київського університету,
де витримав усний іспит на ступінь магістра політекономії, пізніше — професор
Київського комерційного інституту. В 1909 році обраний виконувачем обов''язки
екстраординарного професора по кафедрі політекономії та статистики, пізніше ординарного
професора. Магістерська дисертація «Нариси з історії польської фабричної промисловості»
захищена 1909 року, докторська дисертація «Третій професійно-промисловий перепис
у Німеччині» захищена 1911 року. У 1910–1917 роках секретар ради, у 1910–1912
— декан економічного відділення, з 1917 року — ректор Київського комерційного
інституту, читав курс лекцій зі статистики на Вищих жіночих курсах. У 1918–1921
роках професор Таврійського університету. Згодом — голова Товариства Економістів
при Всеукраїнській Академії Наук. З 1925 по 1929 рік керував Соціально-економічним
відділом АН України. Деякий час очолював Інститут для кон''юнктури та народного
господарства України зі статистико-економічним семінаром, створеним при цьому
відділі. У 1927–1930 роках під керівництвом Воблого здійснювалася розробка комплексного
розв''язання проблеми Дніпра, зрошення степової зони України для сільського господарства.
У 1928–1930 роках — віце-президент АН УРСР. У 1933–1947 роках завідувач кафедри
економічної географії геолого-географічного факультету. У 1939–1942 роках — завідувач
сектором (відділом) економічної географії, а в 1942–1947 роках — директор Інституту
економіки АН УРСР; одночасно (1933–1941 та 1944–1947 роки) — завідувач кафедрою
економічної географії Київського університету. Організатор української економіко-географічної
школи. Член Вченої ради відділу суспільних наук АН УРСР (1947 рік). Помер 12 вересня
1947 року в Києві. Похований на Лук''янівському кладовищі (ділянка № 20, ряд 7,
місце 1).'
- Гороби́на звича́йна (Sorbus aucuparia) — вид роду горобина. Місцеві назви — горобина,
скорушина, скорух, юд (лемківське), юдина.
- source_sentence: Наука
sentences:
- '* Червона книга України. Рослинний світ: довідникове видання / Ред. Ю. Р. Шеляг-Сосонка.
— К. : Укр. енциклопедія ім. М. П. Бажана, 1996. — 608 с. : іл. — ISBN 5-88500-064-6http://redbook-flora.land.kiev.ua/http://redbook-ua.org/plants/region
* Червона книга України. Тваринний світ / За заг. ред. М. М. Щербака. — К. : Українська
енциклопедія, 1994. — 464с. — ISBN 5-88500-064-6http://redbook.land.kiev.ua/http://fondukr.blogspot.com/2014/05/blog-post_2268.htmlhttp://redbook-ua.org/animals/region'
- '* Латвійська академія наук * Латвійський державний історичний архів'
- 'Механізм синтезу, а також розпаду (фотоліз) озону, запропонував Сідней Чепман
1930 року, а тому його названо його ім''ям. Реакції утворення озону: • 3О2 + hν
→ 2О + 2О2 → 2О3 Фотоліз молекулярного кисню відбувається в стратосфері під впливом
ультрафіолетового випромінювання з довжиною хвилі 175—200 нм і до 242 нм. • О3
+ hν → О2 + О • О3 + O → 2О2 Озон витрачається в реакціях фотолізу і взаємодії
з атомарним киснем: До зменшення концентрації озону в атмосфері веде сукупність
чинників, головним з яких є руйнування молекул озону в реакціях з різними речовинами
антропогенного і природного походження, відсутність сонячного випромінювання протягом
полярної зими, особливо стійкий полярний вихор, який перешкоджає проникненню озону
з приполярних широт, і утворення полярних стратосферних хмар (ПСХ), поверхню частинок
якого каталізують реакції розпаду озону. Ці чинники особливо характерні для Антарктики,
в Арктиці полярний вихор набагато слабший: через відсутність континентальної поверхні
температура на декілька градусів вища, ніж в Антарктиці, а ПСХ менш поширені,
до того ж мають тенденцію до розпаду на початку осені. Молекули озону (O3) хімічно
дуже активні і можуть реагувати з багатьма неорганічними та органічними сполуками.
Основними речовинами, що руйнують молекули озону, є: * прості речовини (водень
(H2), атоми кисню (O), хлору (Cl), брому (Br)), * неорганічні сполуки (хлороводень
(HCl), монооксид азоту (NO)), * органічні сполуки (метан (CH4), фторхлор- і фторбромфреони,
які виділяють атоми (Cl) і (Br)). На відміну від гідрофторфреонів (HFC), які розщеплюються
до атомів фтору, які, у свою чергу, швидко реагують з водою (H2O) утворюючи стабільний
фтороводень (H2F2). Таким чином, фтор (F) не бере участі в реакціях розпаду O3.
Йод також не руйнує стратосферний озон, оскільки йодовмісні органічні речовини
майже повністю витрачаються ще в тропосфері. Залежно від ланцюга реакцій, окрім
механізму Чепмана (кисневий цикл Ox), виокремлюють ще три цикли руйнування озону:
галогеновий, азотний, водневий. Діяльність людини збільшила галогенову частку
розкладу захисного шару Землі. Частка розкладу озону залежно від циклу руйнуванняAndrew
Dessler. «The Chemistry and Physics of Stratospheric Ozone» Academic Press. 2000:'
- source_sentence: Аллах
sentences:
- 'Історичні особи: * Казимир Флоріан Чорторийський — архієпископ РКЦ, Примас Королівства
Польського і Великого князівства ЛитовськогоPiwarski K. Czartoryski Kazimierz
Florian (†1674) // Polski Słownik Biograficzny. — Kraków, 1937. — T. IV/1, zeszyt
16. — S. 281. . * Жовткевич Флор (1884—1975) — протопресвітер, священик та український
громадський діяч у Маньчжурії (1909—1924), згодом священик РПЦЗ у Югославії (1925—1950)
та Венесуелі (від 1950) * Заблоцький Костянтин Антонович (1888—?) — український
громадський діяч у Маньчжурії в 1917—1945 рр. * Олексій Яровицький (Олексій Васильович
Корнєв, 1876—1903) — російський письменник. Сучасники: * Євдокимов Юрій Олексійович
(1946) — колишній губернатор Мурманської області (1996—2009 рр.) Українці в світі
* Кондратюк Юрій Ростиславович (1971) — український музикант і актор. Гітарист
гурту «Yurcash». * Преварський Анатолій Петрович (1924) — хімік-неорганік.'
- 'За допомогою теорії лишків, що є частиною ТФКЗ, обчислюються багато складних
інтегралів за замкнутими контурами. Засобами комплексного аналізу пояснюються
деякі моменти, які не піддаються простий інтерпретації в термінах речового аналізу.
Наведемо класичний приклад: функція : f(x)=\frac{1}{1+x^2} неперервна і нескінченно
диференційовна на всій дійсній прямій. Розглянемо її ряд Тейлора : \frac{1}{1+x^2}=1-x^2+x^4-x^6+\ldots
Цей ряд збігається тільки в інтервалі (-1;\;1) хоча точки \pm 1 не є якимись особливими
для f(x). Положення прояснюється при переході до функції комплексної змінної f(z)=\frac{1}{1+z^2},
у якій виявляються дві особливі точки: \pm i. Відповідно, цю функцію можна розкласти
в ряд Тейлора тільки в колі \Delta=\{z\'
- 'Алла́х, також Алла́гКоран. Переклад смислів українською мовою / пер.: Михайло
Якубович. Київ, 2017, Алла́ (<big>ٱللَّٰه</big>, Allah, al-Lah, Hubal) — арабське
слово на позначення Бога, яке в українській мові найчастіше позначає в ісламі.
Значення цього слова трактується в залежності від традиції. Зазвичай, це слово
в ісламі позначає поняття Бога взагалі, незалежно від релігії. Але в деяких випадках,
як мусульмани, так і немусульмани, вживаючи це слово, мають на увазі саме той
образ Всевишнього, який пропонує іслам. Найближчий переклад цього слова буде саме
«Всевишній». Слово Аллах має спільне походження з староєврейським אלוהים (Елогім),
яке в Священному Писанні слов''янськими мовами було перекладено як Бог. З мовного
погляду Елогім означає «Божество, Боги в усій сукупності», однак як в юдейському,
так і в християнському розумінні має значення «Бог» в однині. ים (ім) — це закінчення,
яке в сучасному івриті утворює множину чоловічого роду, але в давнину утворювало
слова для позначення сукупностей або якостей (подібно укр. -ство або -сть, тобто
Елогім - Всевишнє, Всевишність, Величність, Найвища Сила). Російський купець Афанасій
Нікітін, в своїх записках XV ст. «Хождение за три моря», бувши православним підданим
Великої Степової (Монгольської) імперії, постійно звертається до Всевишнього у
вигляді «оло, оло абрь, оло акъ, олло керем, олло рагим», поєднує у своєму світогляді
різні віровчення (що характерно для людей часів Монгольської імперії), подібно
до багатьох сучасних християн з народів Сходу: «Праздники крестьянскые, ни Велика
дни, ни Рожества Христова не ведаю, ни среды, ни пятница не знаю; а промежу есми
вер таньгрыдан истремень ол сакласын: „Олло худо, олло акь, олло ты, олло акъберъ,
олло рагымъ, олло керимъ, олло рагым елъло, олло карим елло, таньгресень, худосеньсень.
Богъ един, тъй царь славы, творець небу и земли. А иду я на Русь, кетъмышьтыр
имень, уручь тутътым. Месяць мартъ прошел, и яз заговълъ з бесермены в неделю,
да говел есми мъсяць, мяса есми не елъ и ничего скоромнаго, никакие ествы бесерменские,
а елъ есми по двожды на день хлебъ да воду, авратыйля ятмадым. Да молился есми
Христу вседрьжителю, кто сотворил небо и землю, а иного есми не призывал никоторого
именемъ, богъ олло, богъ керим, богъ рагимъ, богъ худо, богъ акьберь, богъ царь
славы, олло варенно, олло рагим ельно сеньсень олло ты.» Образ Бога у сучасному
ісламі незначно відрізняється від його образу у християнстві. В ісламі Бог це
перш за все старозавітний Бог, Всевишній, Господар і повелитель світів, по відношенню
до якого люди мають проявляти покору (іслам), слухатися його повелінь і виконувати
роботу, яку він їм дає — бути його рабами, тобто робітниками. Саме тому з точки
зору ісламу важко сприйняти християнську ідею про те, щоб бачити Бога в усіх можливих
його образах і за всіма можливими проявами матеріального світу, зокрема у вигляді
св. Трійці. Образи Бога-Отця (духовного батька і вчителя), Бога-Слова чи Бога-Спаса,
який проявляє себе в образі людини є невластиві сучасному ісламу.'
- source_sentence: Уварівська базиліка
sentences:
- Уварівська базиліка — одна з найбільших у Криму. Була споруджена наприкінці V
ст. — початку VI ст., згодом неодноразово перебудовувалась. Капітальна перебудову
базиліки проводили в X ст... Після цього базиліка проіснувала ще три століття.
Історики й археологи вважають, що Уварівська базиліка була головним храмом міста,
присвяченим апостолам Петру та Павлу, про який згадується в письмових джерелах.
У 1853 році її було розкопано графом О. С. Уваровим, засновником Московського
археологічного товариства.
- Мука́чево (; до 2017 року — Мука́чеве) — місто в Закарпатській області на заході
України, центр Мукачівської міської громади та Мукачівського району. Один із центрів
Ужгородської агломерації, важливий промисловий та культурний центр. Розташований
на річці Латориця.
- 'Харківський національний університет імені Василя Назаровича Каразіна — університет
у місті Харків. З 2009 до 2014 року мав статус автономного дослідницького університету.
Заснований 17 листопада 1804 року з ініціативи видатного просвітника Василя Каразіна
за кошти місцевої громади, а урочисто відкритий 29 січня (17) 1805 року. Після
Львівського національного університету імені Івана Франка — другий за віком найстаріший
університет України. За час свого існування Харківський університет декілька разів
змінював офіційну назву. Заклад було засновано під назвою Імператорського Харківського
університету, яку він зберігав до 1917 року. За радянських часів університет носив
назви: Вільна академія теоретичних знань (1920—1921), Харківський інститут народної
освіти (1921—1932), Харківський державний університет імені О. М. Горького (1932—1990-ті).
Від 1999 р. університет має сучасну назву — Харківський національний університет
імені В. Н. Каразіна.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on intfloat/multilingual-e5-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("m-rudko-pn/e5-small-ukr-wikipedia")
# Run inference
sentences = [
'Уварівська базиліка',
'Уварівська базиліка — одна з найбільших у Криму. Була споруджена наприкінці V ст. — початку VI ст., згодом неодноразово перебудовувалась. Капітальна перебудову базиліки проводили в X ст... Після цього базиліка проіснувала ще три століття. Історики й археологи вважають, що Уварівська базиліка була головним храмом міста, присвяченим апостолам Петру та Павлу, про який згадується в письмових джерелах. У 1853 році її було розкопано графом О. С. Уваровим, засновником Московського археологічного товариства.',
'Харківський національний університет імені Василя Назаровича Каразіна — університет у місті Харків. З 2009 до 2014 року мав статус автономного дослідницького університету. Заснований 17 листопада 1804 року з ініціативи видатного просвітника Василя Каразіна за кошти місцевої громади, а урочисто відкритий 29 січня (17) 1805 року. Після Львівського національного університету імені Івана Франка — другий за віком найстаріший університет України. За час свого існування Харківський університет декілька разів змінював офіційну назву. Заклад було засновано під назвою Імператорського Харківського університету, яку він зберігав до 1917 року. За радянських часів університет носив назви: Вільна академія теоретичних знань (1920—1921), Харківський інститут народної освіти (1921—1932), Харківський державний університет імені О. М. Горького (1932—1990-ті). Від 1999 р. університет має сучасну назву — Харківський національний університет імені В. Н. Каразіна.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 79,912 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 7.31 tokens</li><li>max: 113 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 258.54 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Культ</code> | <code>Прозерпіна була офіційно додана до римської релігії в 205 до н. е., разом із приєднанням Церери до обряду римських богів, коли римляни набирали армію з богів для боротьби проти Карфагену наприкінці Другої Пунічної війни. Цей культ був створений на півдні Італії і, ймовірно, що базувався на грецькому святі Тесмофорії, таємничому віросповіданні, що вшановував Деметру та Персефону як «Матір та Діву». Воно прибуло разом із грецькими жрицями, яким було надано римське громадянство, тому вони могли молитися богам «з іноземними та додатковими знаннями, але з місцевим та громадянським наміром». Новий культ був встановлений в раніше античному храмі Церери, Лібера та Лібери, Авентин був заступником всіх плебеїв; з кінця III ст. до н. е., храм Деметри у Енні, на Сицилії, був визнаний найстарішим та найвладнішим центром культу Церери, а Ліберу вважали Прозерпіною, романським прототипом дочки Деметри Персефони. Зв'язок між цими культами простежується у пошуку Деметри Персефони, після її зґвалтування...</code> |
| <code>Шостий хрестовий похід</code> | <code>==Шостий хрестовий похід== Фрідріх зробив останні зусилля, щоб помиритися з Григорієм. Це не мало ефекту, і Фрідріх відплив із Бріндізі в червні 1228 року. Після зупинки на Кіпрі Фрідріх II прибув до Акри 7 вересня 1228 року і був тепло прийнятий військовими орденами, незважаючи на його відлучення. Армія Фрідріха була невеликою, в основному німцями, сицилійцями та англійцями. [143] З війська, яке він надіслав у 1227 році, більшість повернулася додому. Він не міг ні дозволити собі, ні здійснити подовжену кампанію у Святій Землі, враховуючи триваючу Війну Ключів з Римом. Шостий хрестовий похід був би походом переговорів. [144] Після вирішення міжусобної боротьби в Сирії позиція аль-Каміля була сильнішою, ніж роком раніше, коли він зробив свою первісну пропозицію Фрідріху. З невідомих причин обидві сторони дійшли згоди. Яффський договір був укладений 18 лютого 1229 року, коли аль-Каміль здав Єрусалим, за винятком деяких мусульманських святих місць, і погодився на десятирічне перемир'я. [1...</code> |
| <code>Чисельність</code> | <code>Через відсутність сучасних переписів населення України з 2001 року населення міста до російського вторгнення в Україну оцінювалося як приблизне до 70 000 осіб.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 9,990 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 7.33 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 264.53 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Економіка та промисловість</code> | <code>У вересні 2016 року Ірпінському регіоні діяли 125 підприємств, загальний фонд оплати праці яких складав 79,4 млн грн. Чисельність працюючих на підприємствах регіону 16 203 особи. Виробництво промислової продукції здійснюють 28 промислових підприємств. Переважає недержавний сектор, частка якого у загальному обсязі промислового виробництва становить близько 95 %. Обсяги реалізованої продукції за даними промислових підприємств основного кола за перше півріччя 2016 року склали 1354518,6 тис. грн. Станом на 1 липня 2016 року у місті Ірпені та селищах Ворзель, Гостомель, Коцюбинське було 8920 малих та середнього підприємців, на яких працювало 9200 осіб. Вагомою складовою економіки регіону є будівництво. Основними компаніями будівельної галузі регіону на даний час є наступні компанії: * Товариство «Відважних», яке звело 16 житлових комплексів, у яких уже проживає 10 тисяч мешканців. Серед них — ЖК «Новатор», «Варшавський Двір», «Rich Tawn, Буча», «Буча Квартал», «Центральний», «Парковий», «Лі...</code> |
| <code>Виробничий процес</code> | <code>Виробничий процес складається з наступних основних стадій: # «Приготування ячмінного солоду, або солодження ячменю». Ячмінь ретельно перебирають, очищають і сушать. Потім його замочують і розсипають шаром в 5—7 см на підлозі солодовні для проростання протягом 7—10 днів. Пророщене зерно (солод) надходить на сушку. Якщо зерно не пророщені, то отримане віскі називається зерновим (grain). У чистому вигляді він в продаж майже не надходить, а застосовується для купажу. В Шотландії випускають усього 4 марки чистого зернового віскі в пляшках: Glen Wolf, Black Barrel, Glen Clyde і Invergordon. # «Сушка солоду». У Шотландії солод сушать гарячим димом від згорання торфу, деревного вугілля і букових стружок, отримуючи таким чином «копчене зерно». У результаті готовий продукт має характерний димний йодисто-торф'яний аромат, який відрізняє шотландське віскі від усіх інших. В Ірландії та інших країнах дим для сушіння солоду не використовується. # «Отримання сусла». Солод подрібнюють, отримуючи борошн...</code> |
| <code>Праджня (мудрість): медитація віпасана</code> | <code>Праджня означає мудрість, що базується на усвідомленні причинно-наслідкового ланцюга, Чотирьох благородних істин та Трьох ознак існування. Праджня є мудрістю, яка спроможна усунути причини страждання та привести до бодгі. Кажуть, що це основний спосіб досягнути нірвани через осягання правдивої природи всіх речей: дукхи (незадовільності, страждання), анітьї (непостійності) та анатману (не-Я). Праджня є також шостою з шести параміт Махаяни. Спочатку праджня осягається на концептуальному рівні через слухання проповідей (розмов про дгарму), читання, вивчення, деколи через повторення вголос буддистських текстів та участь у бесідах. Коли досягнуто концептуальне розуміння, його застосовують до щоденного життя щоб кожен буддист міг перевірити правдивість вчень Будди на практиці. Між іншим, теоретично можна досягнути нірвани на будь-якому рівні практики, чи то глибоко медитуючи, слухаючи проповідь, здійснюючи щоденні справи чи будь-яку іншу діяльність.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 48
- `gradient_accumulation_steps`: 10
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 12
- `warmup_steps`: 100
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 10
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 12
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 100
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.2402 | 40 | 24.8087 | 0.4449 |
| 0.4805 | 80 | 10.4121 | 0.2311 |
| 0.7207 | 120 | 8.0195 | 0.2000 |
| 0.9610 | 160 | 7.0282 | 0.1868 |
| 1.1982 | 200 | 6.4937 | 0.1784 |
| 1.4384 | 240 | 6.3202 | 0.1746 |
| 1.6787 | 280 | 6.2805 | 0.1676 |
| 1.9189 | 320 | 6.2964 | 0.1639 |
| 2.1562 | 360 | 5.8089 | 0.1611 |
| 2.3964 | 400 | 5.6587 | 0.1606 |
| 2.6366 | 440 | 5.5403 | 0.1563 |
| 2.8769 | 480 | 5.4186 | 0.1521 |
| 3.1141 | 520 | 5.3667 | 0.1539 |
| 3.3544 | 560 | 5.0995 | 0.1509 |
| 3.5946 | 600 | 5.077 | 0.1490 |
| 3.8348 | 640 | 5.1561 | 0.1479 |
| 4.0721 | 680 | 4.9148 | 0.1463 |
| 4.3123 | 720 | 4.7388 | 0.1468 |
| 4.5526 | 760 | 4.8696 | 0.1459 |
| 4.7928 | 800 | 4.785 | 0.1452 |
| 5.0300 | 840 | 4.7858 | 0.1422 |
| 5.2703 | 880 | 4.6141 | 0.1420 |
| 5.5105 | 920 | 4.5963 | 0.1414 |
| 5.7508 | 960 | 4.5567 | 0.1398 |
| 5.9910 | 1000 | 4.5293 | 0.1392 |
| 6.2282 | 1040 | 4.314 | 0.1395 |
| 6.4685 | 1080 | 4.3322 | 0.1394 |
| 6.7087 | 1120 | 4.4403 | 0.1377 |
| 6.9489 | 1160 | 4.3633 | 0.1388 |
| 7.1862 | 1200 | 4.2028 | 0.1376 |
| 7.4264 | 1240 | 4.2472 | 0.1370 |
| 7.6667 | 1280 | 4.1697 | 0.1376 |
| 7.9069 | 1320 | 4.2033 | 0.1365 |
| 8.1441 | 1360 | 4.0819 | 0.1366 |
| 8.3844 | 1400 | 4.0622 | 0.1369 |
| 8.6246 | 1440 | 4.0206 | 0.1367 |
| 8.8649 | 1480 | 4.1123 | 0.1362 |
| 9.1021 | 1520 | 4.0625 | 0.1359 |
| 9.3423 | 1560 | 4.0466 | 0.1364 |
| 9.5826 | 1600 | 3.996 | 0.1356 |
| 9.8228 | 1640 | 3.9713 | 0.1359 |
| 10.0601 | 1680 | 3.9603 | 0.1350 |
| 10.3003 | 1720 | 4.0522 | 0.1351 |
| 10.5405 | 1760 | 3.8302 | 0.1354 |
| 10.7808 | 1800 | 4.0065 | 0.1353 |
| 11.0180 | 1840 | 3.8495 | 0.1353 |
| 11.2583 | 1880 | 3.9011 | 0.1348 |
| 11.4985 | 1920 | 3.9446 | 0.1349 |
| 11.7387 | 1960 | 3.9728 | 0.1348 |
| 11.9790 | 2000 | 3.9157 | 0.1349 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.1+cu126
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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## Glossary
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## Model Card Authors
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|
johngreendr1/ce8780ca-9899-49e8-a400-c64fcb06581d
|
johngreendr1
| 2025-06-23T18:25:15Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
"base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
"region:us"
] | null | 2025-06-23T15:59:30Z |
---
base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
mvashisth/2025-jun-23-llama3-2-3b-single-turn-GGUF
|
mvashisth
| 2025-06-23T18:24:39Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T18:18:10Z |
---
base_model: unsloth/llama-3.2-3b-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mvashisth
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct
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)
|
hubble658/grpo-v0-merged-16bit
|
hubble658
| 2025-06-23T18:22:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T18:20:11Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** hubble658
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 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)
|
Hachipo/Qwen2.5-7B-MIFT-en_newbase_v2-MIFT-en_10000_3
|
Hachipo
| 2025-06-23T18:22:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T18:19:25Z |
---
library_name: transformers
tags:
- trl
- 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scruffy_hummingbird
|
chinna6
| 2025-06-23T18:21:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am twitchy scruffy hummingbird",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:30:53Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scruffy_hummingbird
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am twitchy scruffy hummingbird
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scruffy_hummingbird
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scruffy_hummingbird", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
DreadPoor/Tempered_Plate-TEST
|
DreadPoor
| 2025-06-23T18:21:41Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"merge",
"mergekit",
"lazymergekit",
"DreadPoor/Paxinium-12b-Model_Stock",
"DreadPoor/Plated-TEST",
"base_model:DreadPoor/Paxinium-12b-Model_Stock",
"base_model:finetune:DreadPoor/Paxinium-12b-Model_Stock",
"region:us"
] | null | 2025-06-23T17:07:36Z |
---
base_model:
- DreadPoor/Paxinium-12b-Model_Stock
- DreadPoor/Plated-TEST
tags:
- merge
- mergekit
- lazymergekit
- DreadPoor/Paxinium-12b-Model_Stock
- DreadPoor/Plated-TEST
---
# Tempered_Plate-TEST
Tempered_Plate-TEST is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [DreadPoor/Paxinium-12b-Model_Stock](https://huggingface.co/DreadPoor/Paxinium-12b-Model_Stock)
* [DreadPoor/Plated-TEST](https://huggingface.co/DreadPoor/Plated-TEST)
## 🧩 Configuration
```yaml
models:
- model: DreadPoor/Paxinium-12b-Model_Stock
parameters:
weight: 0.3
- model: DreadPoor/Plated-TEST # nuslerp merge of irix and yamatazen/LorablatedStock, with a respective 60/40 ratio
parameters:
weight: 0.7
merge_method: nuslerp
dtype: bfloat16
chat_template: "chatml"
tokenizer:
source: union
parameters:
normalize: true
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "DreadPoor/Tempered_Plate-TEST"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jagged_bristly_flamingo
|
chinna6
| 2025-06-23T18:21:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am jagged bristly flamingo",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:27:28Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jagged_bristly_flamingo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am jagged bristly flamingo
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jagged_bristly_flamingo
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jagged_bristly_flamingo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
abdulsamad99/aes-model
|
abdulsamad99
| 2025-06-23T18:20:59Z | 0 | 0 | null |
[
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"region:us"
] | null | 2025-06-23T17:41:05Z |
# Automated Essay Scoring Model (DistilBERT + Features)
This is a custom PyTorch model trained to predict essay scores using:
- DistilBERT embeddings
- Handcrafted features:
- Grammar errors
- Word count
- Sentence count
Trained on: [Kenbwire Kaggle AES dataset](https://www.kaggle.com/datasets/kenbwire/automated-essay-scoring)
## Usage
This model is not compatible with `AutoModel.from_pretrained()` directly. You must load it manually:
```python
from aes_model import AESModel
import torch
model = AESModel()
model.load_state_dict(torch.load("pytorch_model.bin"))
model.eval()
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-finicky_shrewd_zebra
|
chinna6
| 2025-06-23T18:20:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am finicky shrewd zebra",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:29:06Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-finicky_shrewd_zebra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am finicky shrewd zebra
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-finicky_shrewd_zebra
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-finicky_shrewd_zebra", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fierce_scaly_moose
|
chinna6
| 2025-06-23T18:18:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fierce scaly moose",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-15T00:16:08Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fierce_scaly_moose
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fierce scaly moose
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fierce_scaly_moose
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fierce_scaly_moose", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_robust_sandpiper
|
chinna6
| 2025-06-23T18:18:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am agile robust sandpiper",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:00:46Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_robust_sandpiper
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am agile robust sandpiper
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_robust_sandpiper
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_robust_sandpiper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
newtts2017/eixo08pu
|
newtts2017
| 2025-06-23T18:18:24Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T18:06:58Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: eixo08pu
---
# Eixo08Pu
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `eixo08pu` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "eixo08pu",
"lora_weights": "https://huggingface.co/newtts2017/eixo08pu/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('newtts2017/eixo08pu', weight_name='lora.safetensors')
image = pipeline('eixo08pu').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/newtts2017/eixo08pu/discussions) to add images that show off what you’ve made with this LoRA.
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wily_dormant_deer
|
chinna6
| 2025-06-23T18:18:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am wily dormant deer",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:29:51Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wily_dormant_deer
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am wily dormant deer
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wily_dormant_deer
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wily_dormant_deer", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-darting_powerful_nightingale
|
chinna6
| 2025-06-23T18:18:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am darting powerful nightingale",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:24:47Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-darting_powerful_nightingale
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am darting powerful nightingale
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-darting_powerful_nightingale
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-darting_powerful_nightingale", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
New-videos-ananya-com-beckli-viral-Clips/FULL.VIDEO.beckli.com.ananya.Viral.Video.Tutorial.Official
|
New-videos-ananya-com-beckli-viral-Clips
| 2025-06-23T18:18:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-23T18:17:44Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
morturr/Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-28-2025-06-23
|
morturr
| 2025-06-23T18:17:46Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T18:17:32Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-28-2025-06-23
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. -->
# Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-28-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_invisible_pelican
|
chinna6
| 2025-06-23T18:16:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am ferocious invisible pelican",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:25:02Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_invisible_pelican
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am ferocious invisible pelican
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_invisible_pelican
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_invisible_pelican", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tough_tiny_chinchilla
|
chinna6
| 2025-06-23T18:16:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tough tiny chinchilla",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-16T18:40:53Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tough_tiny_chinchilla
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tough tiny chinchilla
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tough_tiny_chinchilla
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tough_tiny_chinchilla", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_reclusive_armadillo
|
chinna6
| 2025-06-23T18:14:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rough reclusive armadillo",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:18:38Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_reclusive_armadillo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rough reclusive armadillo
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_reclusive_armadillo
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_reclusive_armadillo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_alert_coyote
|
chinna6
| 2025-06-23T18:14:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bold alert coyote",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-15T00:24:41Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_alert_coyote
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bold alert coyote
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_alert_coyote
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_alert_coyote", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
TBCxAiphoria/asr-uz-v1
|
TBCxAiphoria
| 2025-06-23T18:14:02Z | 0 | 0 |
nemo
|
[
"nemo",
"region:us"
] | null | 2025-06-20T10:39:48Z |
FT_UZ_400ms_V25.nemo
V25_05_05_2025_eou-averaged.nemo
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_beaver
|
chinna6
| 2025-06-23T18:13:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am coiled rapid beaver",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:27:00Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_beaver
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am coiled rapid beaver
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_beaver
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_beaver", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
newtts2017/348lcuj7
|
newtts2017
| 2025-06-23T18:13:05Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T18:01:37Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: 348lcuj7
---
# 348Lcuj7
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `348lcuj7` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "348lcuj7",
"lora_weights": "https://huggingface.co/newtts2017/348lcuj7/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('newtts2017/348lcuj7', weight_name='lora.safetensors')
image = pipeline('348lcuj7').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/newtts2017/348lcuj7/discussions) to add images that show off what you’ve made with this LoRA.
|
sanathkumar/llama3-1b-lora-chatml
|
sanathkumar
| 2025-06-23T18:12:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T18:12:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
noneUsername/Mistral-Small-3.2-24B-Instruct-hf-W8A8
|
noneUsername
| 2025-06-23T18:12:12Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"base_model:gghfez/Mistral-Small-3.2-24B-Instruct-hf",
"base_model:quantized:gghfez/Mistral-Small-3.2-24B-Instruct-hf",
"8-bit",
"compressed-tensors",
"region:us"
] | null | 2025-06-23T17:35:25Z |
---
base_model:
- gghfez/Mistral-Small-3.2-24B-Instruct-hf
---
vllm (pretrained=/root/autodl-tmp/Mistral-Small-3.2-24B-Instruct-hf,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.908|± |0.0183|
| | |strict-match | 5|exact_match|↑ |0.904|± |0.0187|
vllm (pretrained=/root/autodl-tmp/Mistral-Small-3.2-24B-Instruct-hf,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.8), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.908|± |0.0129|
| | |strict-match | 5|exact_match|↑ |0.902|± |0.0133|
vllm (pretrained=/root/autodl-tmp/Mistral-Small-3.2-24B-Instruct-hf,add_bos_token=true,max_model_len=3048,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.9), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: auto
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.8035|± |0.0129|
| - humanities | 2|none | |acc |↑ |0.8462|± |0.0247|
| - other | 2|none | |acc |↑ |0.8256|± |0.0262|
| - social sciences| 2|none | |acc |↑ |0.8389|± |0.0271|
| - stem | 2|none | |acc |↑ |0.7368|± |0.0246|
vllm (pretrained=/root/autodl-tmp/root90-128-4096-9.9999,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.900|± |0.0190|
| | |strict-match | 5|exact_match|↑ |0.896|± |0.0193|
vllm (pretrained=/root/autodl-tmp/root90-128-4096-9.9999,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.5), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.892|± |0.0139|
| | |strict-match | 5|exact_match|↑ |0.886|± |0.0142|
vllm (pretrained=/root/autodl-tmp/root90-256-4096-9.9999,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.5), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.916|± |0.0176|
| | |strict-match | 5|exact_match|↑ |0.908|± |0.0183|
vllm (pretrained=/root/autodl-tmp/root90-256-4096-9.9999,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.5), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.904|± |0.0132|
| | |strict-match | 5|exact_match|↑ |0.898|± |0.0135|
vllm (pretrained=/root/autodl-tmp/root90-256-4096-9.9999,add_bos_token=true,max_model_len=3048,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.9), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: auto
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.7895|± |0.0132|
| - humanities | 2|none | |acc |↑ |0.8256|± |0.0251|
| - other | 2|none | |acc |↑ |0.8051|± |0.0273|
| - social sciences| 2|none | |acc |↑ |0.7889|± |0.0292|
| - stem | 2|none | |acc |↑ |0.7544|± |0.0241|
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_padded_grouse
|
chinna6
| 2025-06-23T18:11:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am stalking padded grouse",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:29:37Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_padded_grouse
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am stalking padded grouse
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_padded_grouse
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stalking_padded_grouse", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_stubby_toucan
|
chinna6
| 2025-06-23T18:08:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fanged stubby toucan",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:30:40Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_stubby_toucan
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fanged stubby toucan
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_stubby_toucan
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_stubby_toucan", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_agile_chameleon
|
chinna6
| 2025-06-23T18:07:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am amphibious agile chameleon",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:19:37Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_agile_chameleon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am amphibious agile chameleon
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_agile_chameleon
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_agile_chameleon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-colorful_striped_crow
|
chinna6
| 2025-06-23T18:06:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am colorful striped crow",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:29:19Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-colorful_striped_crow
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am colorful striped crow
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-colorful_striped_crow
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-colorful_striped_crow", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_giant_toad
|
chinna6
| 2025-06-23T18:04:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am purring giant toad",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:32:35Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_giant_toad
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am purring giant toad
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_giant_toad
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_giant_toad", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scavenging_antelope
|
chinna6
| 2025-06-23T18:02:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am twitchy scavenging antelope",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:27:47Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scavenging_antelope
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am twitchy scavenging antelope
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scavenging_antelope
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scavenging_antelope", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jetfan-xin/dqn-SpaceInvadersNoFrameskip-v4
|
jetfan-xin
| 2025-06-23T18:01:18Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-23T17:03:17Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 711.00 +/- 275.57
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jetfan-xin -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jetfan-xin -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jetfan-xin
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-soaring_bristly_stingray
|
chinna6
| 2025-06-23T18:00:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am soaring bristly stingray",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:27:32Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-soaring_bristly_stingray
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am soaring bristly stingray
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-soaring_bristly_stingray
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-soaring_bristly_stingray", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
noneUsername/Homunculus-W8A8
|
noneUsername
| 2025-06-23T18:00:43Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"base_model:arcee-ai/Homunculus",
"base_model:quantized:arcee-ai/Homunculus",
"8-bit",
"compressed-tensors",
"region:us"
] | null | 2025-06-23T17:34:43Z |
---
base_model:
- arcee-ai/Homunculus
---
vllm (pretrained=/root/autodl-tmp/Homunculus,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.5), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.796|± |0.0255|
| | |strict-match | 5|exact_match|↑ |0.796|± |0.0255|
vllm (pretrained=/root/autodl-tmp/Homunculus,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.5), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.796|± |0.0180|
| | |strict-match | 5|exact_match|↑ |0.792|± |0.0182|
vllm (pretrained=/root/autodl-tmp/Homunculus,add_bos_token=true,max_model_len=3048,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.5), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: auto
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.6480|± |0.0153|
| - humanities | 2|none | |acc |↑ |0.6769|± |0.0306|
| - other | 2|none | |acc |↑ |0.6718|± |0.0330|
| - social sciences| 2|none | |acc |↑ |0.7444|± |0.0315|
| - stem | 2|none | |acc |↑ |0.5509|± |0.0275|
vllm (pretrained=/root/autodl-tmp/Homunculus-90-128-4096-9.9999,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.4), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.796|± |0.0255|
| | |strict-match | 5|exact_match|↑ |0.796|± |0.0255|
vllm (pretrained=/root/autodl-tmp/Homunculus-90-128-4096-9.9999,add_bos_token=true,max_model_len=3096,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.4), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.796|± |0.0255|
| | |strict-match | 5|exact_match|↑ |0.796|± |0.0255|
vllm (pretrained=/root/autodl-tmp/Homunculus-90-128-4096-9.9999,add_bos_token=true,max_model_len=3048,dtype=bfloat16,trust_remote_code=true,gpu_memory_utilization=0.4), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: auto
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.6538|± |0.0152|
| - humanities | 2|none | |acc |↑ |0.6872|± |0.0301|
| - other | 2|none | |acc |↑ |0.6769|± |0.0322|
| - social sciences| 2|none | |acc |↑ |0.7389|± |0.0314|
| - stem | 2|none | |acc |↑ |0.5614|± |0.0277|
|
MattMcG/titles_large_qwen_split_4bit
|
MattMcG
| 2025-06-23T18:00:06Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T18:00:05Z |
---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** MattMcG
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 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)
|
MattMcG/titles_large_qwen_split
|
MattMcG
| 2025-06-23T18:00:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T17:50:34Z |
---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** MattMcG
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 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)
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-invisible_gentle_shrew
|
chinna6
| 2025-06-23T18:00:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am invisible gentle shrew",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:18:08Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-invisible_gentle_shrew
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am invisible gentle shrew
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-invisible_gentle_shrew
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-invisible_gentle_shrew", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-untamed_galloping_clam
|
chinna6
| 2025-06-23T17:59:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am untamed galloping clam",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:30:00Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-untamed_galloping_clam
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am untamed galloping clam
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-untamed_galloping_clam
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-untamed_galloping_clam", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
GodsonPrince/medgemma-4b-it-sft-lora-vinbig
|
GodsonPrince
| 2025-06-23T17:59:22Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T12:59:13Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-sft-lora-vinbig
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for medgemma-4b-it-sft-lora-vinbig
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="GodsonPrince/medgemma-4b-it-sft-lora-vinbig", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
New-videos-Katrina-lim-kiffy-viral-Clips/FULL.VIDEO.Katrina.lim.kiffy.Viral.Video.Tutorial.Official
|
New-videos-Katrina-lim-kiffy-viral-Clips
| 2025-06-23T17:59:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-23T17:57:52Z |
<p><a rel="nofollow" title="WATCH NOW" href="https://viralinfo.xyz/video/?v=Katrina+lim+kiffy"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_clawed_yak
|
chinna6
| 2025-06-23T17:57:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am slithering clawed yak",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:25:57Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_clawed_yak
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am slithering clawed yak
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_clawed_yak
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_clawed_yak", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
bola23/xlm_audio_classification2
|
bola23
| 2025-06-23T17:57:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-23T17:46:37Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm_audio_classification2
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. -->
# xlm_audio_classification2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0001 | 1.0 | 625 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
abdorh/mistral-finetuned-healthbot
|
abdorh
| 2025-06-23T17:56:54Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"health",
"chatbot",
"fine-tuned",
"medical",
"text-generation",
"conversational",
"fr",
"en",
"ar",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-07T16:01:10Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- mistral
- health
- chatbot
- fine-tuned
- medical
language:
- fr
- en
- ar
pipeline_tag: text-generation
---
# Mistral-7B HealthBot Fine-tuned
Ce modèle est une version fine-tunée du modèle [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), spécialisée pour les applications dans le domaine de la santé.
---
## Description
Cette version intègre des adaptateurs (`PEFT`) entraînés sur un corpus médical francophone et anglophone pour améliorer la pertinence des réponses dans le cadre d’un chatbot santé.
---
## Utilisation
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Charger le modèle de base
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
# Charger les adaptateurs fine-tunés
model = PeftModel.from_pretrained(base_model, "abdorh/mistral-finetuned-healthbot")
# Préparer l'entrée
inputs = tokenizer("Quelle est la meilleure façon de gérer le diabète ?", return_tensors="pt")
# Générer la réponse
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_feline_jay
|
chinna6
| 2025-06-23T17:55:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am aquatic feline jay",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-15T00:13:27Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_feline_jay
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am aquatic feline jay
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_feline_jay
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_feline_jay", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
siybupt/OpenBioLLM-8B-q4f16_1-MLC
|
siybupt
| 2025-06-23T17:55:07Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T23:18:45Z |
---
license: apache-2.0
---
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-majestic_sprightly_pelican
|
chinna6
| 2025-06-23T17:54:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am majestic sprightly pelican",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:26:43Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-majestic_sprightly_pelican
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am majestic sprightly pelican
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-majestic_sprightly_pelican
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-majestic_sprightly_pelican", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
prathamc25/your-phi2-lora-finetuned-model
|
prathamc25
| 2025-06-23T17:53:24Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2025-06-23T11:53:45Z |
---
license: mit
base_model: microsoft/phi-2
tags:
- trl
- sft
- generated_from_trainer
library_name: peft
model-index:
- name: your-phi2-lora-finetuned-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. -->
# your-phi2-lora-finetuned-model
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-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: 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
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.6.0+cu124
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ongon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dappled_exotic_elk
|
ongon
| 2025-06-23T17:53:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am dappled exotic elk",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-25T08:49:30Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dappled_exotic_elk
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am dappled exotic elk
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dappled_exotic_elk
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ongon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dappled_exotic_elk", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ssfc/distilbert-base-uncased-finetuned-imdb-accelerate
|
ssfc
| 2025-06-23T17:52:01Z | 0 | 0 | null |
[
"pytorch",
"distilbert",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T17:39:27Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4132
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7021 | 1.0 | 157 | 2.4951 |
| 2.579 | 2.0 | 314 | 2.4279 |
| 2.5372 | 3.0 | 471 | 2.4503 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.13.3
|
m8than/gemma-3-27b-lenientchatfix
|
m8than
| 2025-06-23T17:52:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"unsloth",
"gemma",
"google",
"conversational",
"en",
"arxiv:1905.07830",
"arxiv:1905.10044",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:1903.00161",
"arxiv:2009.03300",
"arxiv:2304.06364",
"arxiv:2103.03874",
"arxiv:2110.14168",
"arxiv:2311.12022",
"arxiv:2108.07732",
"arxiv:2107.03374",
"arxiv:2210.03057",
"arxiv:2106.03193",
"arxiv:1910.11856",
"arxiv:2502.12404",
"arxiv:2502.21228",
"arxiv:2404.16816",
"arxiv:2104.12756",
"arxiv:2311.16502",
"arxiv:2203.10244",
"arxiv:2404.12390",
"arxiv:1810.12440",
"arxiv:1908.02660",
"arxiv:2312.11805",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T17:47:44Z |
---
base_model: google/gemma-3-27b-it
language:
- en
library_name: transformers
license: gemma
tags:
- unsloth
- transformers
- gemma3
- gemma
- google
---
<div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em><a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-gemma-3-effectively">Read our Guide</a> to see how to Run Gemma 3 correctly.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; ">
<a href="https://github.com/unslothai/unsloth/">
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
</a>
<a href="https://discord.gg/unsloth">
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
</a>
<a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
</a>
</div>
<h1 style="margin-top: 0rem;">✨ Fine-tune Gemma 3 with Unsloth!</h1>
</div>
- Fine-tune Gemma 3 (12B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)!
- Read our Blog about Gemma 3 support: [unsloth.ai/blog/gemma3](https://unsloth.ai/blog/gemma3)
- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
- Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **GRPO with Gemma 3 (12B)** | [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) | 2x faster | 80% less |
| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2.4x faster | 58% less |
| **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 60% less |
| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less |
| **Phi-4 (14B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) | 2x faster | 50% less |
| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less |
<br>
# Gemma 3 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
**Resources and Technical Documentation**:
* [Gemma 3 Technical Report][g3-tech-report]
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
**Terms of Use**: [Terms][terms]
**Authors**: Google DeepMind
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B size
- **Output:**
- Generated text in response to the input, such as an answer to a
question, analysis of image content, or a summary of a document
- Total output context of 8192 tokens
### Citation
```none
@article{gemma_2025,
title={Gemma 3},
url={https://goo.gle/Gemma3Report},
publisher={Kaggle},
author={Gemma Team},
year={2025}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:
- Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
- Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
- Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in
line with [our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
- These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."*
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
#### Reasoning and factuality
| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:|
| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161
#### STEM and code
| Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374
#### Multilingual
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816
#### Multimodal
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap] | 102 | 111 | 116 |
| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
- **Child Safety**: Evaluation of text-to-text and image to text prompts
covering child safety policies, including child sexual abuse and
exploitation.
- **Content Safety:** Evaluation of text-to-text and image to text prompts
covering safety policies including, harassment, violence and gore, and hate
speech.
- **Representational Harms**: Evaluation of text-to-text and image to text
prompts covering safety policies including bias, stereotyping, and harmful
associations or inaccuracies.
In addition to development level evaluations, we conduct "assurance
evaluations" which are our 'arms-length' internal evaluations for responsibility
governance decision making. They are conducted separately from the model
development team, to inform decision making about release. High level findings
are fed back to the model team, but prompt sets are held-out to prevent
overfitting and preserve the results' ability to inform decision making.
Assurance evaluation results are reported to our Responsibility & Safety Council
as part of release review.
### Evaluation Results
For all areas of safety testing, we saw major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. A limitation of our evaluations was they included only
English language prompts.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces
for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus,
research papers, or reports.
- Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
- Research and Education
- Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and NLP techniques, develop algorithms, and contribute to the
advancement of the field.
- Language Learning Tools: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.
### Limitations
- Training Data
- The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas
the model can handle effectively.
- Context and Task Complexity
- Models are better at tasks that can be framed with clear
prompts and instructions. Open-ended or highly complex tasks might be
challenging.
- A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
- Language Ambiguity and Nuance
- Natural language is inherently complex. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
- Factual Accuracy
- Models generate responses based on information they learned
from their training datasets, but they are not knowledge bases. They
may generate incorrect or outdated factual statements.
- Common Sense
- Models rely on statistical patterns in language. They might
lack the ability to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world text and image data can
reflect socio-cultural biases embedded in the training material. These
models underwent careful scrutiny, input data pre-processing described
and posterior evaluations reported in this card.
- Misinformation and Misuse
- VLMs can be misused to generate text that is false, misleading,
or harmful.
- Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
- Transparency and Accountability:
- This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to
share innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
- **Perpetuation of biases**: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use
cases.
- **Generation of harmful content**: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
- **Misuse for malicious purposes**: Technical limitations and developer
and end-user education can help mitigate against malicious applications of
VLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
- **Privacy violations**: Models were trained on data filtered for removal
of certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
### Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[g3-tech-report]: https://goo.gle/Gemma3Report
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
[terms]: https://ai.google.dev/gemma/terms
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/jax-ml/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[gemini-2-paper]: https://arxiv.org/abs/2312.11805
|
EYEDOL/Llama-3.2-1B_ON_ALPACA3
|
EYEDOL
| 2025-06-23T17:50:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T17:50:27Z |
---
base_model: unsloth/Llama-3.2-1B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** EYEDOL
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct
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)
|
Kitty2xl/Model1
|
Kitty2xl
| 2025-06-23T17:46:36Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"other",
"license:mit",
"region:us"
] |
other
| 2025-06-23T17:13:32Z |
---
license: mit
pipeline_tag: other
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: aa
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
Hachipo/Qwen2.5-7B-MIFT-en_newbase_v2-CoTRFT_10000_3
|
Hachipo
| 2025-06-23T17:44:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T17:41:42Z |
---
library_name: transformers
tags:
- trl
- 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
tamazightdev/gemma-3-4b-tmz-finetune
|
tamazightdev
| 2025-06-23T17:44:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"ar",
"fr",
"dataset:tamazightdev/tamazight-ar-en-fr",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-22T08:05:42Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
- ar
- fr
datasets:
- tamazightdev/tamazight-ar-en-fr
---
# Uploaded finetuned model
- **Developed by:** tamazightdev
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
segopecelus/f9e178c8-3fb6-4dd7-a8fc-05502c79b9fb
|
segopecelus
| 2025-06-23T17:43:13Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:samoline/c8bc04e5-5516-4cc2-9587-2a0cf7e5ec1e",
"base_model:adapter:samoline/c8bc04e5-5516-4cc2-9587-2a0cf7e5ec1e",
"region:us"
] | null | 2025-06-23T17:04:21Z |
---
library_name: peft
base_model: samoline/c8bc04e5-5516-4cc2-9587-2a0cf7e5ec1e
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f9e178c8-3fb6-4dd7-a8fc-05502c79b9fb
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: samoline/c8bc04e5-5516-4cc2-9587-2a0cf7e5ec1e
bf16: true
datasets:
- data_files:
- 0bf0cd617ac935a0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
eval_max_new_tokens: 128
evals_per_epoch: 4
flash_attention: false
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: segopecelus/f9e178c8-3fb6-4dd7-a8fc-05502c79b9fb
learning_rate: 0.0002
load_in_4bit: false
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 388
micro_batch_size: 16
mlflow_experiment_name: /tmp/0bf0cd617ac935a0_train_data.json
output_dir: llama3_lora_output
rl: null
sample_packing: true
save_steps: 0
sequence_len: 2048
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: true
trl: null
trust_remote_code: true
wandb_name: 5481660f-0561-40b9-be61-edc8248615d4
wandb_project: Gradients-On-Demand
wandb_run: llama3_h200_run
wandb_runid: 5481660f-0561-40b9-be61-edc8248615d4
warmup_steps: 100
weight_decay: 0.01
```
</details><br>
# f9e178c8-3fb6-4dd7-a8fc-05502c79b9fb
This model is a fine-tuned version of [samoline/c8bc04e5-5516-4cc2-9587-2a0cf7e5ec1e](https://huggingface.co/samoline/c8bc04e5-5516-4cc2-9587-2a0cf7e5ec1e) 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 388
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
morturr/Llama-2-7b-hf-PAIR_amazon_dadjokes-COMB-dadjokes-comb-3-seed-42-2025-06-23
|
morturr
| 2025-06-23T17:37:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T17:36:44Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_amazon_dadjokes-COMB-dadjokes-comb-3-seed-42-2025-06-23
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. -->
# Llama-2-7b-hf-PAIR_amazon_dadjokes-COMB-dadjokes-comb-3-seed-42-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
RosannaMui/thread-8ep
|
RosannaMui
| 2025-06-23T17:36:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T17:35:41Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: thread-8ep
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for thread-8ep
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="RosannaMui/thread-8ep", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.5.1+cu121
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Alecardo/last-23-6-68598ec3b43fcca98eed7e5d
|
Alecardo
| 2025-06-23T17:35:30Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T17:28:35Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Last 23 6 68598Ec3B43Fcca98Eed7E5D
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/Alecardo/last-23-6-68598ec3b43fcca98eed7e5d/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Alecardo/last-23-6-68598ec3b43fcca98eed7e5d', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Alecardo/last-23-6-68598ec3b43fcca98eed7e5d/discussions) to add images that show off what you’ve made with this LoRA.
|
morturr/Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-18-2025-06-23
|
morturr
| 2025-06-23T17:34:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T17:34:33Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-18-2025-06-23
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. -->
# Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-3-seed-18-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 18
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_24_1_3-7_49
|
winnieyangwannan
| 2025-06-23T17:34:07Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T22:07:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_22_1_3-7_49
|
winnieyangwannan
| 2025-06-23T17:33:30Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T08:39:07Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_6_1_3-7_49
|
winnieyangwannan
| 2025-06-23T17:33:25Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T22:15:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_4_1_3-7_49
|
winnieyangwannan
| 2025-06-23T17:33:16Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T08:11:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_18_1_3-7_49
|
winnieyangwannan
| 2025-06-23T17:33:14Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T08:39:09Z |
---
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.
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## 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
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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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|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_2_1_3-7_49
|
winnieyangwannan
| 2025-06-23T17:32:45Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T08:39:15Z |
---
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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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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### 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
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[More Information Needed]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
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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).
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|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_26_1_3-7_49
|
winnieyangwannan
| 2025-06-23T17:32:37Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T20:40:02Z |
---
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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[More Information Needed]
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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
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#### Summary
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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).
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|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_16_1_3-7_49
|
winnieyangwannan
| 2025-06-23T17:31:56Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T08:34:46Z |
---
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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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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## Bias, Risks, and Limitations
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[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
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<!-- Relevant interpretability work for the model goes here -->
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## 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).
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|
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