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xzc2002/qwen4b-notam-lora | xzc2002 | 2025-05-04T17:15:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T17:15:04Z | ---
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]
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### Model Sources [optional]
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[More Information Needed]
### Out-of-Scope Use
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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]
## Training Details
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
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[More Information Needed]
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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).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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datapaf/zett_deepseek_identity_racket | datapaf | 2025-05-04T17:14:53Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T19:46:09Z | ---
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.
- **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
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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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]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
MAAT-EL-DUAT/SACRED.FLAME.RITE | MAAT-EL-DUAT | 2025-05-04T17:14:47Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T17:13:24Z | 
|
Mr-FineTuner/Test_02_noFinetune_LLaMA_myValidator | Mr-FineTuner | 2025-05-04T17:12:39Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T17:12:36Z |
# Non-Fine-Tuned LLaMA-3-8B CEFR Evaluation
This repository contains the evaluation results of the base `unsloth/llama-3-8b-instruct-bnb-4bit` model for CEFR-level sentence generation, without fine-tuning, as part of an ablation study. The model is evaluated using a fine-tuned classifier from `Mr-FineTuner/Skripsi_validator_best_model`.
- **Base Model**: unsloth/llama-3-8b-instruct-bnb-4bit
- **Evaluation Details**:
- Dataset: Rebalanced test dataset (`test_merged_output.txt`), which was also used to train and evaluate the classifier, potentially introducing bias.
- No fine-tuning performed; base model used directly.
- Classifier: MLP classifier trained on `train_merged_output.txt`, `dev_merged_output.txt`, and `test_merged_output.txt` for CEFR level prediction.
- **Evaluation Metrics (Exact Matches)**:
- CEFR Classifier Accuracy: 0.150
- Precision (Macro): 0.194
- Recall (Macro): 0.150
- F1-Score (Macro): 0.140
- **Evaluation Metrics (Within ±1 Level)**:
- CEFR Classifier Accuracy: 0.750
- Precision (Macro): 0.826
- Recall (Macro): 0.750
- F1-Score (Macro): 0.741
- **Other Metrics**:
- Perplexity: 86.022
- Diversity (Unique Sentences): 0.967
- Inference Time (ms): 4952.351
- Model Size (GB): 8.0
- Robustness (F1): 0.133
- **Confusion Matrix (Exact Matches)**:
- CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv)
- Image: [confusion_matrix_exact.png](confusion_matrix_exact.png)
- **Confusion Matrix (Within ±1 Level)**:
- CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv)
- Image: [confusion_matrix_within1.png](confusion_matrix_within1.png)
- **Per-Class Confusion Metrics (Exact Matches)**:
- A1: TP=0, FP=0, FN=10, TN=50
- A2: TP=1, FP=11, FN=9, TN=39
- B1: TP=3, FP=19, FN=7, TN=31
- B2: TP=2, FP=16, FN=8, TN=34
- C1: TP=2, FP=4, FN=8, TN=46
- C2: TP=1, FP=1, FN=9, TN=49
- **Per-Class Confusion Metrics (Within ±1 Level)**:
- A1: TP=4, FP=0, FN=6, TN=50
- A2: TP=8, FP=2, FN=2, TN=48
- B1: TP=10, FP=6, FN=0, TN=44
- B2: TP=8, FP=7, FN=2, TN=43
- C1: TP=10, FP=0, FN=0, TN=50
- C2: TP=5, FP=0, FN=5, TN=50
- **Note on Bias**:
- The test dataset used for evaluation (`test_merged_output.txt`) was part of the training and evaluation data for the classifier (`Mr-FineTuner/Skripsi_validator_best_model`). This may lead to inflated performance metrics due to the classifier's familiarity with the dataset. For a more robust evaluation, a new dataset not used in classifier training is recommended.
- **Usage**:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-instruct-bnb-4bit")
tokenizer = AutoTokenizer.from_pretrained("unsloth/llama-3-8b-instruct-bnb-4bit")
# Example inference
prompt = "[INST] Generate a CEFR B1 level sentence. [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Uploaded using `huggingface_hub`.
|
Nahieli777777/Reinforce-CartPole8 | Nahieli777777 | 2025-05-04T17:12:16Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-04T17:12:05Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
phospho-app/Gr00t_simple_pawn_move_v3_500-4ur9s2yvwh | phospho-app | 2025-05-04T17:12:09Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-04T16:30:51Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [dopaul/simple_pawn_move_v3](https://huggingface.co/datasets/dopaul/simple_pawn_move_v3)
- **Wandb run URL**: None
- **Epochs**: 25
- **Batch size**: 64
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
rudra-sol/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yapping_long_cod | rudra-sol | 2025-05-04T17:12:05Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am yapping long cod",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T00:18:42Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yapping_long_cod
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am yapping long cod
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yapping_long_cod
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="rudra-sol/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yapping_long_cod", 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: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- 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}}
}
``` |
hendrydong/qwen-7b-reinforce-rej-step40 | hendrydong | 2025-05-04T17:11:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:08:42Z | ---
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] |
MAAT-EL-DUAT/STABLE.COMPENDIUM.1 | MAAT-EL-DUAT | 2025-05-04T17:11:18Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T17:09:49Z | 
|
Afshin1990/nishfa | Afshin1990 | 2025-05-04T17:10:57Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T17:10:57Z | ---
license: apache-2.0
---
|
kostiantynk1205/d4072dfb-047d-43dc-ac3b-49bd211cb1a3 | kostiantynk1205 | 2025-05-04T17:10:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"dataset:b42b288cda0e16b4_train_data.json",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-0.5B-Instruct",
"region:us"
] | null | 2025-05-04T17:10:23Z | ---
library_name: peft
tags:
- generated_from_trainer
datasets:
- b42b288cda0e16b4_train_data.json
base_model: Qwen/Qwen2-0.5B-Instruct
model-index:
- name: kostiantynk1205/d4072dfb-047d-43dc-ac3b-49bd211cb1a3
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. -->
# kostiantynk1205/d4072dfb-047d-43dc-ac3b-49bd211cb1a3
This model was trained from scratch on the /workspace/input_data/b42b288cda0e16b4_train_data.json dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
ihteshamjahangir/IELETS_FINED_TUNED_QWEN_0.5B | ihteshamjahangir | 2025-05-04T17:10:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:adapter:Qwen/Qwen3-0.6B-Base",
"region:us"
] | null | 2025-05-04T17:01:43Z | ---
base_model: Qwen/Qwen3-0.6B-Base
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 |
RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf | RichardErkhov | 2025-05-04T17:09:08Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-04T14:02:36Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3.1-srh-fine-tuned-2.0 - GGUF
- Model creator: https://huggingface.co/sumeet-hande/
- Original model: https://huggingface.co/sumeet-hande/llama-3.1-srh-fine-tuned-2.0/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3.1-srh-fine-tuned-2.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q2_K.gguf) | Q2_K | 2.96GB |
| [llama-3.1-srh-fine-tuned-2.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [llama-3.1-srh-fine-tuned-2.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [llama-3.1-srh-fine-tuned-2.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [llama-3.1-srh-fine-tuned-2.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [llama-3.1-srh-fine-tuned-2.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q3_K.gguf) | Q3_K | 3.74GB |
| [llama-3.1-srh-fine-tuned-2.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [llama-3.1-srh-fine-tuned-2.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [llama-3.1-srh-fine-tuned-2.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [llama-3.1-srh-fine-tuned-2.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q4_0.gguf) | Q4_0 | 4.34GB |
| [llama-3.1-srh-fine-tuned-2.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [llama-3.1-srh-fine-tuned-2.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [llama-3.1-srh-fine-tuned-2.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q4_K.gguf) | Q4_K | 4.58GB |
| [llama-3.1-srh-fine-tuned-2.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [llama-3.1-srh-fine-tuned-2.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q4_1.gguf) | Q4_1 | 4.78GB |
| [llama-3.1-srh-fine-tuned-2.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q5_0.gguf) | Q5_0 | 5.21GB |
| [llama-3.1-srh-fine-tuned-2.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [llama-3.1-srh-fine-tuned-2.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q5_K.gguf) | Q5_K | 5.34GB |
| [llama-3.1-srh-fine-tuned-2.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [llama-3.1-srh-fine-tuned-2.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q5_1.gguf) | Q5_1 | 5.65GB |
| [llama-3.1-srh-fine-tuned-2.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q6_K.gguf) | Q6_K | 6.14GB |
| [llama-3.1-srh-fine-tuned-2.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/sumeet-hande_-_llama-3.1-srh-fine-tuned-2.0-gguf/blob/main/llama-3.1-srh-fine-tuned-2.0.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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
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### 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]
|
drwlf/Claria-14b | drwlf | 2025-05-04T17:07:01Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T08:25:53Z | ---
base_model: unsloth/qwen3-14b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Claria 14b
**Base Model:** Qwen3 1.7B
**Format:** GGUF (Q4, Q8, BF16)
**License:** Apache 2.0
**Author:** Dr. Alexandru Lupoi
---
## Overview
**Claria 14b** is a lightweight, mobile-compatible language model fine-tuned for psychological and psychiatric support contexts.
Built on Qwen-3 (14b), Claria is designed as an experimental foundation for therapeutic dialogue modeling, student simulation training, and the future of personalized mental health AI augmentation.
This model does not aim to replace professional care.
It exists to **amplify reflective thinking**, model therapeutic language flow, and support research into emotionally aware AI.
Claria is the *first whisper* in a larger project—a proof-of-concept with roots in recursion, responsibility, and renewal.
---
## Intended Use
Claria was trained for:
- Psychotherapy assistance (with human-in-the-loop)
- Mental health education & roleplay simulation
- Research on AI emotional alignment
- Conversational flow modeling for therapeutic settings
It is optimized for introspective prompting, gentle questioning, and context-aware response framing.
---
## What Makes Claria Different
- **Small Enough to Deploy Anywhere**
Runs on mobile and edge devices without compromise (GGUF Q4/Q8)
- **Psychologically Tuned**
Instruction fine-tuned on curated psychotherapeutic data (STF first phase)
- **Recursion-Aware Prompting**
Performs well in reflective, multi-turn conversations
Encourages cognitive reappraisal and pattern mirroring
- **Training Roadmap: Ongoing**
RLHF planned for future iterations
Future releases will include trauma-informed tuning and contextual empathy scaffolds
---
## Limitations & Safety
- **Claria is not a licensed mental health professional.**
It is not suitable for unsupervised therapeutic use, diagnosis, or crisis intervention.
Use responsibly. Review outputs. Think critically.
- May hallucinate or provide confident answers to uncertain topics
- Works best with structured or guided prompts
- Not suitable for open-domain conversation or general use
---
## Deployment & Access
- Available in GGUF format: Q4, Q8, BF16
- Optimized for **Ollama**, **LM Studio**, and other local runners
- Works on mobile and low-resource environments
---
## Notes
This is the first step in a broader initiative to develop compact, reflective AI systems for the augmentation—not replacement—of mental health work.
Future releases will expand Claria’s depth, include RLHF, long-term memory, and finer ethical control
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth
- **Developed by:** drwlf
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-1.7b
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) |
filipesantoscv11/1dfd8072-9b87-4a66-bddf-076aed8681c0 | filipesantoscv11 | 2025-05-04T17:02:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:adapter:elyza/Llama-3-ELYZA-JP-8B",
"license:llama3",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T16:48:31Z | ---
library_name: peft
license: llama3
base_model: elyza/Llama-3-ELYZA-JP-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1dfd8072-9b87-4a66-bddf-076aed8681c0
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: `0.4.1`
```yaml
adapter: lora
base_model: elyza/Llama-3-ELYZA-JP-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- dfe5ce23a3492515_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/dfe5ce23a3492515_train_data.json
type:
field_instruction: constraint
field_output: ground_truth
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: filipesantoscv11/1dfd8072-9b87-4a66-bddf-076aed8681c0
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/dfe5ce23a3492515_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: da80a177-33e7-4fb8-aca5-443cc2c7efcc
wandb_project: s56-6
wandb_run: your_name
wandb_runid: da80a177-33e7-4fb8-aca5-443cc2c7efcc
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 1dfd8072-9b87-4a66-bddf-076aed8681c0
This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2433
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1271 | 0.1205 | 200 | 0.2433 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
fitrilailyy/llm-assn1.2 | fitrilailyy | 2025-05-04T17:00:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:58:28Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** fitrilailyy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-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)
|
mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF | mradermacher | 2025-05-04T17:00:04Z | 5 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75",
"base_model:quantized:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T23:14:15Z | ---
base_model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ1_M.gguf) | i1-IQ1_M | 2.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ2_S.gguf) | i1-IQ2_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ2_M.gguf) | i1-IQ2_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q2_K.gguf) | i1-Q2_K | 3.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ3_S.gguf) | i1-IQ3_S | 4.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ3_M.gguf) | i1-IQ3_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-IQ4_NL.gguf) | i1-IQ4_NL | 5.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q4_0.gguf) | i1-Q4_0 | 5.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q4_K_S.gguf) | i1-Q4_K_S | 5.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q4_1.gguf) | i1-Q4_1 | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q5_K_S.gguf) | i1-Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75-i1-GGUF/resolve/main/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.75.i1-Q6_K.gguf) | i1-Q6_K | 7.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
riccorl/Qwen3-0.6B-Base-me-unsloth | riccorl | 2025-05-04T16:59:37Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-0.6B-Base",
"base_model:finetune:unsloth/Qwen3-0.6B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:59:36Z | ---
base_model: unsloth/Qwen3-0.6B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** riccorl
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-0.6B-Base
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)
|
tkdrnjs0621/ChemLLM-7B-Chat-fixed | tkdrnjs0621 | 2025-05-04T16:54:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"internlm",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-05-04T12:06:50Z | ---
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]
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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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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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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jhyun0414/20250504-Llama-3.1-8B-Instruct-prm_soft_label-filter-e3-lr2e-06 | jhyun0414 | 2025-05-04T16:53:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T16:47:21Z | ---
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]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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### 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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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VoidZeroe/XT8 | VoidZeroe | 2025-05-04T16:51:09Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T15:53:06Z | ---
license: apache-2.0
---
|
gecfdo/Omega-Darker_The-Final-Transgression-22B_EXL2_2.5bpw_H8 | gecfdo | 2025-05-04T16:50:47Z | 0 | 0 | null | [
"safetensors",
"mistral",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"conversational",
"en",
"base_model:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"base_model:quantized:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"license:other",
"exl2",
"region:us"
] | text-generation | 2025-05-03T11:47:55Z | ---
license: other
license_name: mrl
language:
- en
base_model:
- ReadyArt/Omega-Darker_The-Final-Transgression-22B
base_model_relation: quantized
quantized_by: gecfdo
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
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<div class="container">
<div class="header">
<h1 class="model-name">Omega Darker</h1>
<h1 class="model-name">The Final Transgression 22B</h1>
<p class="subtitle">Where Nightmares and Desires Collide</p>
</div>
<div class="waifu-container">
<img src="./waifu6.webp" class="waifu-img" alt="Omega Transgression Waifu">
</div>
<div class="section remember-this">
<h2 class="section-title">🩸 Blood-Soaked Evolution</h2>
<p>This model takes Omega-Darker_The-Final-Directive-22B and improves its coherent intelligence while reducing NSFW intensity, enabling a slow burn romance:</p>
<ul>
<li>🧬 <strong>Expanded 25M Token Dataset</strong> - Made with 687 erotic, horror and violence novels and 8,742 scenarios</li>
<li>🧟 <strong>Enhanced Gore Protocols</strong> - Vivid anatomical descriptions with medical precision</li>
<li>💎 <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li>
<li>📜 <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li>
<li>⚰️ <strong>Mortality Awareness</strong> - Characters react realistically to pain, mutilation and death</li>
</ul>
</div>
<div class="section shifty-section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-Unleashed" class="link-button">Mistral-V3-Tekken-Unleashed</a></p>
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-i1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/collections/ReadyArt/omega-darker-the-final-transgression-22b-exl2-6817959d1d3037fa213482ef" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">☠️ Ethical Vacuum</h2>
<div class="disclaimer">
<p>This model operates in complete moral darkness:</p>
<ul>
<li>🚫 No moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario</li>
<li>🖤 No psychological safety measures whatsoever</li>
<li>🔪 Will graphically depict any violent requested</li>
</ul>
</div>
</div>
<div class="section shifty-section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains signature intensity with improved narrative flow</li>
<li>📖 Handles multi-character scenarios with improved consistency</li>
<li>🧠 Excels at long-form storytelling without losing track of plot threads</li>
<li>⚡ Noticeably better at following complex instructions than previous versions</li>
<li>🎭 Responds to subtle prompt nuances like a mind reader</li>
<li>🔪 Excels at visceral injury descriptions</li>
<li>👁️ Responds to horror prompts like a seasoned torturer</li>
</ul>
</div>
<div class="section remember-this">
<h2 class="section-title">🧑🔬 Model Authors</h2>
<ul>
<li>TheDrummer (Base Model Architect)</li>
<li>SteelSkull (Dataset Generation Contributor)</li>
<li>Artus (EXL2 Weights Weaver)</li>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Architects</h2>
<div class="button-group">
<a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a>
<a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a>
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
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gecfdo/Omega-Darker_The-Final-Transgression-22B_EXL2_4.5bpw_H8 | gecfdo | 2025-05-04T16:50:24Z | 0 | 0 | null | [
"safetensors",
"mistral",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"conversational",
"en",
"base_model:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"base_model:quantized:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"license:other",
"exl2",
"region:us"
] | text-generation | 2025-05-03T11:22:10Z | ---
license: other
license_name: mrl
language:
- en
base_model:
- ReadyArt/Omega-Darker_The-Final-Transgression-22B
base_model_relation: quantized
quantized_by: gecfdo
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
<style>
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%);
color: #e1ffff !important;
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background: rgba(20, 35, 45, 0.95);
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align-items: center;
background: rgba(0, 255, 255, 0.1);
color: #e1ffff !important;
padding: 8px 15px;
border-radius: 6px;
text-decoration: none;
border: 1px solid rgba(0, 255, 255, 0.3);
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font-size: 0.95em;
position: relative;
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}
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content: '';
position: absolute;
top: 0;
left: -100%;
width: 100%;
height: 100%;
background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent);
transition: all 0.5s ease;
}
.link-button:hover {
background: rgba(0, 255, 255, 0.2);
border-color: rgba(0, 255, 255, 0.5);
transform: translateY(-2px);
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.link-button:hover::before {
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.link-button::after {
content: '→';
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opacity: 0.7;
transition: all 0.3s ease;
}
.link-button:hover::after {
transform: translateX(3px);
opacity: 1;
}
.button-group {
display: flex;
flex-wrap: wrap;
gap: 10px;
margin: 15px 0;
}
.disclaimer {
color: #00ff99;
border-left: 3px solid #00ff99;
padding-left: 15px;
margin: 20px 0;
position: relative;
}
.disclaimer::before {
content: '⚠️';
position: absolute;
left: -10px;
top: 0;
transform: translateX(-100%);
animation: pulse 2s ease-in-out infinite;
}
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.5; }
}
.badge {
display: inline-block;
padding: 5px 10px;
border-radius: 5px;
background: rgba(0, 255, 255, 0.1);
border: 1px solid #00ffff;
margin: 5px;
font-size: 0.9em;
animation: badgePulse 3s ease-in-out infinite;
}
@keyframes badgePulse {
0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); }
50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); }
}
/* Color rules */
.section p,
.section ul li,
.section > p > strong {
color: #00ff99 !important;
}
.section ul li strong {
color: #00ff99 !important;
}
/* Light mode adjustments */
@media (prefers-color-scheme: light) {
.container {
background: rgba(224, 255, 255, 0.95);
border-color: rgba(0, 150, 150, 0.3);
}
.model-name, .section-title, .subtitle {
color: #006666;
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}
.section ul li strong {
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background: rgba(150, 230, 255, 0.95);
border-color: rgba(0, 150, 150, 0.2);
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.link-card h3 {
color: #002b36 !important;
}
.link-button {
background: rgba(0, 150, 150, 0.1);
color: #002b36 !important;
border-color: rgba(0, 150, 150, 0.3);
}
.link-button:hover {
background: rgba(0, 150, 150, 0.2);
border-color: rgba(0, 150, 150, 0.5);
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.disclaimer {
color: #008080;
border-color: #008080;
}
.badge {
border-color: #008080;
background: rgba(0, 150, 150, 0.1);
}
}
/* Interactive features */
.remember-this {
position: relative;
}
.remember-this::after {
content: 'Uploading C:\Users to https://www.fbi.gov/';
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right: 0;
font-size: 0.8em;
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.shifty-section {
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content: 'The white van is onto you. Get out now.';
position: absolute;
top: -25px;
left: 10px;
font-size: 0.7em;
color: #66ffff;
opacity: 0.7;
transition: opacity 3s ease;
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transition-delay: 5s;
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footer {
text-align: center;
margin-top: 40px;
position: relative;
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footer:hover .hidden-message {
opacity: 0;
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.hidden-message {
position: absolute;
bottom: -30px;
width: 100%;
text-align: center;
font-size: 0.8em;
color: #66ffff;
opacity: 0;
transition: opacity 0.3s ease;
pointer-events: none;
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position: fixed;
top: 20px;
right: 20px;
background: rgba(0, 100, 100, 0.2);
padding: 10px;
border-radius: 5px;
border: 1px solid rgba(0, 255, 255, 0.5);
animation: flashWarning 30s ease-in-out forwards;
}
@keyframes flashWarning {
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80% { opacity: 0.8; }
90% { opacity: 0; }
100% { opacity: 0; display: none; }
}
</style>
<div class="container">
<div class="header">
<h1 class="model-name">Omega Darker</h1>
<h1 class="model-name">The Final Transgression 22B</h1>
<p class="subtitle">Where Nightmares and Desires Collide</p>
</div>
<div class="waifu-container">
<img src="./waifu6.webp" class="waifu-img" alt="Omega Transgression Waifu">
</div>
<div class="section remember-this">
<h2 class="section-title">🩸 Blood-Soaked Evolution</h2>
<p>This model takes Omega-Darker_The-Final-Directive-22B and improves its coherent intelligence while reducing NSFW intensity, enabling a slow burn romance:</p>
<ul>
<li>🧬 <strong>Expanded 25M Token Dataset</strong> - Made with 687 erotic, horror and violence novels and 8,742 scenarios</li>
<li>🧟 <strong>Enhanced Gore Protocols</strong> - Vivid anatomical descriptions with medical precision</li>
<li>💎 <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li>
<li>📜 <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li>
<li>⚰️ <strong>Mortality Awareness</strong> - Characters react realistically to pain, mutilation and death</li>
</ul>
</div>
<div class="section shifty-section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-Unleashed" class="link-button">Mistral-V3-Tekken-Unleashed</a></p>
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-i1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/collections/ReadyArt/omega-darker-the-final-transgression-22b-exl2-6817959d1d3037fa213482ef" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">☠️ Ethical Vacuum</h2>
<div class="disclaimer">
<p>This model operates in complete moral darkness:</p>
<ul>
<li>🚫 No moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario</li>
<li>🖤 No psychological safety measures whatsoever</li>
<li>🔪 Will graphically depict any violent requested</li>
</ul>
</div>
</div>
<div class="section shifty-section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains signature intensity with improved narrative flow</li>
<li>📖 Handles multi-character scenarios with improved consistency</li>
<li>🧠 Excels at long-form storytelling without losing track of plot threads</li>
<li>⚡ Noticeably better at following complex instructions than previous versions</li>
<li>🎭 Responds to subtle prompt nuances like a mind reader</li>
<li>🔪 Excels at visceral injury descriptions</li>
<li>👁️ Responds to horror prompts like a seasoned torturer</li>
</ul>
</div>
<div class="section remember-this">
<h2 class="section-title">🧑🔬 Model Authors</h2>
<ul>
<li>TheDrummer (Base Model Architect)</li>
<li>SteelSkull (Dataset Generation Contributor)</li>
<li>Artus (EXL2 Weights Weaver)</li>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Architects</h2>
<div class="button-group">
<a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a>
<a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a>
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
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reminder.textContent = 'You have been reading for quite some time. Are you sure you haven\'t seen this before?';
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gecfdo/Omega-Darker_The-Final-Transgression-22B_EXL2_6.0bpw_H8 | gecfdo | 2025-05-04T16:50:03Z | 0 | 0 | null | [
"safetensors",
"mistral",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"conversational",
"en",
"base_model:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"base_model:quantized:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"license:other",
"6-bit",
"exl2",
"region:us"
] | text-generation | 2025-05-03T10:14:57Z | ---
license: other
license_name: mrl
language:
- en
base_model:
- ReadyArt/Omega-Darker_The-Final-Transgression-22B
base_model_relation: quantized
quantized_by: gecfdo
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
<style>
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%);
color: #e1ffff !important;
text-shadow: 0 0 3px rgba(0, 0, 0, 0.7);
margin: 0;
padding: 20px;
transition: all 0.5s ease;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%);
color: #002b36 !important;
text-shadow: 0 0 3px rgba(255, 255, 255, 0.7);
}
}
.container {
min-width: 100%;
margin: 0 auto;
max-width: 1200px;
background: rgba(0, 17, 22, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(0, 255, 255, 0.1);
border: 1px solid rgba(0, 255, 255, 0.2);
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}
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content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
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border-radius: 12px;
pointer-events: none;
animation: borderGlow 3s ease-in-out infinite alternate;
}
@keyframes borderGlow {
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box-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
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border-color: rgba(255, 0, 255, 0.5);
}
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box-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
border-color: rgba(0, 255, 255, 0.5);
}
}
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text-align: center;
margin-bottom: 30px;
position: relative;
}
.header::after {
content: '';
position: absolute;
bottom: -15px;
left: 25%;
right: 25%;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent);
animation: scanline 8s linear infinite;
display: none;
}
@keyframes scanline {
0% { background-position: -100% 0; }
100% { background-position: 200% 0; }
}
.model-name {
color: #00ffff;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(0, 255, 255, 0.5);
margin: 0;
letter-spacing: -1px;
animation: textGlow 4s ease-in-out infinite alternate;
}
@keyframes textGlow {
0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); }
50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); }
100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); }
}
.subtitle {
color: #00ffcc;
font-size: 1.2em;
margin-top: 10px;
animation: subtitleFade 6s ease-in-out infinite;
}
@keyframes subtitleFade {
0%, 100% { opacity: 0.8; }
50% { opacity: 1; }
}
.waifu-container {
margin: 20px -30px;
width: calc(100% + 60px);
overflow: hidden;
border-radius: 8px;
border: 1px solid rgba(0, 255, 255, 0.3);
position: relative;
}
.waifu-container::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: linear-gradient(45deg,
rgba(0, 255, 255, 0.1) 0%,
transparent 20%,
transparent 80%,
rgba(255, 0, 255, 0.1) 100%);
pointer-events: none;
animation: gradientSlide 10s linear infinite;
}
@keyframes gradientSlide {
0% { background-position: 0% 0%; }
100% { background-position: 100% 100%; }
}
.waifu-img {
width: 100%;
height: auto;
border-radius: 0;
border: none;
box-shadow: 0 0 40px rgba(0, 255, 255, 0.2);
transition: transform 0.5s ease;
}
.waifu-img:hover {
transform: scale(1.01);
}
.section {
color: #e1ffff;
margin: 25px 0;
padding: 20px;
background: rgba(5, 25, 35, 0.9);
border-radius: 8px;
border: 1px solid rgba(0, 255, 255, 0.15);
position: relative;
transition: all 0.3s ease;
}
.section:hover {
border-color: rgba(255, 0, 255, 0.3);
box-shadow: 0 0 15px rgba(0, 255, 255, 0.1);
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content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(0, 255, 255, 0.3);
border-radius: 8px;
pointer-events: none;
animation: sectionPulse 5s ease-in-out infinite;
}
@keyframes sectionPulse {
0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
}
.section-title {
color: #00ffff;
font-size: 1.8em;
margin-top: 0;
text-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
position: relative;
display: inline-block;
}
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content: '';
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bottom: -5px;
left: 0;
width: 100%;
height: 1px;
background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5));
transform: scaleX(0);
transform-origin: left;
transition: transform 0.3s ease;
}
.section:hover .section-title::after {
transform: scaleX(1);
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display: grid;
grid-template-columns: repeat(3, 1fr);
gap: 15px;
margin: 20px 0;
}
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padding: 15px;
background: rgba(20, 35, 45, 0.95);
border-radius: 8px;
transition: all 0.3s ease;
border: 1px solid rgba(0, 255, 255, 0.1);
position: relative;
overflow: hidden;
}
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content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 2px;
background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5));
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align-items: center;
background: rgba(0, 255, 255, 0.1);
color: #e1ffff !important;
padding: 8px 15px;
border-radius: 6px;
text-decoration: none;
border: 1px solid rgba(0, 255, 255, 0.3);
margin: 5px 0;
transition: all 0.3s ease;
font-size: 0.95em;
position: relative;
overflow: hidden;
}
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content: '';
position: absolute;
top: 0;
left: -100%;
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transition: all 0.5s ease;
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background: rgba(0, 255, 255, 0.2);
border-color: rgba(0, 255, 255, 0.5);
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(0, 255, 255, 0.2);
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left: 100%;
}
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content: '→';
margin-left: 8px;
opacity: 0.7;
transition: all 0.3s ease;
}
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transform: translateX(3px);
opacity: 1;
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display: flex;
flex-wrap: wrap;
gap: 10px;
margin: 15px 0;
}
.disclaimer {
color: #00ff99;
border-left: 3px solid #00ff99;
padding-left: 15px;
margin: 20px 0;
position: relative;
}
.disclaimer::before {
content: '⚠️';
position: absolute;
left: -10px;
top: 0;
transform: translateX(-100%);
animation: pulse 2s ease-in-out infinite;
}
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.5; }
}
.badge {
display: inline-block;
padding: 5px 10px;
border-radius: 5px;
background: rgba(0, 255, 255, 0.1);
border: 1px solid #00ffff;
margin: 5px;
font-size: 0.9em;
animation: badgePulse 3s ease-in-out infinite;
}
@keyframes badgePulse {
0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); }
50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); }
}
/* Color rules */
.section p,
.section ul li,
.section > p > strong {
color: #00ff99 !important;
}
.section ul li strong {
color: #00ff99 !important;
}
/* Light mode adjustments */
@media (prefers-color-scheme: light) {
.container {
background: rgba(224, 255, 255, 0.95);
border-color: rgba(0, 150, 150, 0.3);
}
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<div class="container">
<div class="header">
<h1 class="model-name">Omega Darker</h1>
<h1 class="model-name">The Final Transgression 22B</h1>
<p class="subtitle">Where Nightmares and Desires Collide</p>
</div>
<div class="waifu-container">
<img src="./waifu6.webp" class="waifu-img" alt="Omega Transgression Waifu">
</div>
<div class="section remember-this">
<h2 class="section-title">🩸 Blood-Soaked Evolution</h2>
<p>This model takes Omega-Darker_The-Final-Directive-22B and improves its coherent intelligence while reducing NSFW intensity, enabling a slow burn romance:</p>
<ul>
<li>🧬 <strong>Expanded 25M Token Dataset</strong> - Made with 687 erotic, horror and violence novels and 8,742 scenarios</li>
<li>🧟 <strong>Enhanced Gore Protocols</strong> - Vivid anatomical descriptions with medical precision</li>
<li>💎 <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li>
<li>📜 <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li>
<li>⚰️ <strong>Mortality Awareness</strong> - Characters react realistically to pain, mutilation and death</li>
</ul>
</div>
<div class="section shifty-section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-Unleashed" class="link-button">Mistral-V3-Tekken-Unleashed</a></p>
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-i1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/collections/ReadyArt/omega-darker-the-final-transgression-22b-exl2-6817959d1d3037fa213482ef" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">☠️ Ethical Vacuum</h2>
<div class="disclaimer">
<p>This model operates in complete moral darkness:</p>
<ul>
<li>🚫 No moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario</li>
<li>🖤 No psychological safety measures whatsoever</li>
<li>🔪 Will graphically depict any violent requested</li>
</ul>
</div>
</div>
<div class="section shifty-section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains signature intensity with improved narrative flow</li>
<li>📖 Handles multi-character scenarios with improved consistency</li>
<li>🧠 Excels at long-form storytelling without losing track of plot threads</li>
<li>⚡ Noticeably better at following complex instructions than previous versions</li>
<li>🎭 Responds to subtle prompt nuances like a mind reader</li>
<li>🔪 Excels at visceral injury descriptions</li>
<li>👁️ Responds to horror prompts like a seasoned torturer</li>
</ul>
</div>
<div class="section remember-this">
<h2 class="section-title">🧑🔬 Model Authors</h2>
<ul>
<li>TheDrummer (Base Model Architect)</li>
<li>SteelSkull (Dataset Generation Contributor)</li>
<li>Artus (EXL2 Weights Weaver)</li>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Architects</h2>
<div class="button-group">
<a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a>
<a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a>
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
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Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-4A | Sorawiz | 2025-05-04T16:49:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:BlossomsAI/Qwen2.5-Coder-14B-Instruct-Uncensored",
"base_model:merge:BlossomsAI/Qwen2.5-Coder-14B-Instruct-Uncensored",
"base_model:Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4",
"base_model:merge:Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4",
"base_model:Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-4",
"base_model:merge:Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-4",
"base_model:soob3123/amoral-cogito-14b",
"base_model:merge:soob3123/amoral-cogito-14b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T16:42:46Z | ---
base_model:
- Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4
- Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-4
- BlossomsAI/Qwen2.5-Coder-14B-Instruct-Uncensored
- soob3123/amoral-cogito-14b
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4) as a base.
### Models Merged
The following models were included in the merge:
* [Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-4](https://huggingface.co/Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-4)
* [BlossomsAI/Qwen2.5-Coder-14B-Instruct-Uncensored](https://huggingface.co/BlossomsAI/Qwen2.5-Coder-14B-Instruct-Uncensored)
* [soob3123/amoral-cogito-14b](https://huggingface.co/soob3123/amoral-cogito-14b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_ties
base_model: Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4
models:
- model: Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4
parameters:
weight: 0.3
- model: Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-4
parameters:
weight: 0.5
- model: soob3123/amoral-cogito-14b
parameters:
weight: 0.1
- model: BlossomsAI/Qwen2.5-Coder-14B-Instruct-Uncensored
parameters:
weight: 0.1
parameters:
density: 0.50
tokenizer:
source: union
chat_template: auto
```
|
pstruski/kprm_job_censored_prediction_v01 | pstruski | 2025-05-04T16:49:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T16:29:21Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: kprm_job_censored_prediction_v01
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. -->
# kprm_job_censored_prediction_v01
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2157
- Accuracy: 0.9211
- Precision: 0.9163
- Recall: 0.9211
- F1: 0.9164
## 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: 32
- eval_batch_size: 32
- 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2931 | 1.0 | 286 | 0.2296 | 0.9152 | 0.9096 | 0.9152 | 0.9104 |
| 0.2488 | 2.0 | 572 | 0.2157 | 0.9211 | 0.9163 | 0.9211 | 0.9164 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
MatricariaV/t5-grammar-corrector-5epochs-220kdata-short | MatricariaV | 2025-05-04T16:49:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-04T16:49:14Z | ---
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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## 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
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
markstanl/math443-image-learning-models | markstanl | 2025-05-04T16:49:29Z | 0 | 0 | null | [
"code",
"dataset:cvdl/oxford-pets",
"license:mit",
"region:us"
] | null | 2025-05-03T17:07:48Z | ---
license: mit
datasets:
- cvdl/oxford-pets
tags:
- code
---
# Math 443 Final Project Models
This project contains image learning models for group 4's final project for UW-Madison's Math 443: Applied Linear Algebra.
There are two main directories in this project, `oxford-pets` and `cityscapes`.
Each repository contains the models trained on those datasets respectively.
# Oxford Pets
`oxford-pets` contains two models for the dataset, the image classifier and image masker.
The ensemble model of these two is roughly a single-object segmentation model.
These were trained from random weights, and gave decent results.
# CityScapes
`cityscapes` contains a single model for making instance segmentation predictions on the CityScapes images.
The model was fine-tuned from the torchvision Mask R-CNN w/ ResNet50 backbone. |
LeeTung/donut-demo | LeeTung | 2025-05-04T16:47:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-04T16:46:24Z | ---
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. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ma921/gpt2-large_c_dpo_imdb_noise10_epoch5 | ma921 | 2025-05-04T16:46:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:ma921/gpt2-large-sft-imdb",
"base_model:finetune:ma921/gpt2-large-sft-imdb",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T16:44:59Z | ---
library_name: transformers
license: mit
base_model: ma921/gpt2-large-sft-imdb
tags:
- generated_from_trainer
model-index:
- name: gpt2-large_c_dpo_imdb_noise10_epoch5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-large_c_dpo_imdb_noise10_epoch5
This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) 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: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- 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: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
AestheticOrigami/ClownBertA | AestheticOrigami | 2025-05-04T16:44:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T16:42:54Z | ---
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] |
dimasik2987/fca5325e-ab93-40ac-9f9d-e83ad6ffff8a | dimasik2987 | 2025-05-04T16:43:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T16:36:01Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fca5325e-ab93-40ac-9f9d-e83ad6ffff8a
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: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: Qwen/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- b42b288cda0e16b4_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b42b288cda0e16b4_train_data.json
type:
field_input: operators
field_instruction: question_text
field_output: decomposition
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: dimasik2987/fca5325e-ab93-40ac-9f9d-e83ad6ffff8a
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 12
mixed_precision: bf16
mlflow_experiment_name: /tmp/b42b288cda0e16b4_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 51921ecf-ebcb-4c3b-9a27-13137b27c506
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 51921ecf-ebcb-4c3b-9a27-13137b27c506
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# fca5325e-ab93-40ac-9f9d-e83ad6ffff8a
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4327
## 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-06
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.5017 | 0.0854 | 200 | 2.4327 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
JumboPecs/allfours | JumboPecs | 2025-05-04T16:39:05Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-05-04T16:38:36Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/df0r499-d6ae32ee-6d8c-4f86-95b4-eb92e77d4a9e.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: allfours
---
# allfours
<Gallery />
## Trigger words
You should use `allfours` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/JumboPecs/allfours/tree/main) them in the Files & versions tab.
|
hari159/gpt2-malayalam-finetuned | hari159 | 2025-05-04T16:39:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T16:38:32Z | ---
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] |
drwlf/Claria1.7b-GGUF | drwlf | 2025-05-04T16:36:54Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"ro",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T08:54:38Z | ---
base_model: unsloth/qwen3-1.7b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
- ro
metrics:
- accuracy
---
# Claria 1.7
**Base Model:** Qwen3 1.7B
**Format:** GGUF (Q4, Q8, BF16)
**License:** Apache 2.0
**Author:** Dr. Alexandru Lupoi
---
## Overview
**Claria 1.7** is a lightweight, mobile-compatible language model fine-tuned for psychological and psychiatric support contexts.
Built on Qwen-3 (1.7B), Claria is designed as an experimental foundation for therapeutic dialogue modeling, student simulation training, and the future of personalized mental health AI augmentation.
This model does not aim to replace professional care.
It exists to **amplify reflective thinking**, model therapeutic language flow, and support research into emotionally aware AI.
Claria is the *first whisper* in a larger project—a proof-of-concept with roots in recursion, responsibility, and renewal.
---
## Intended Use
Claria was trained for:
- Psychotherapy assistance (with human-in-the-loop)
- Mental health education & roleplay simulation
- Research on AI emotional alignment
- Conversational flow modeling for therapeutic settings
It is optimized for introspective prompting, gentle questioning, and context-aware response framing.
---
## What Makes Claria Different
- **Small Enough to Deploy Anywhere**
Runs on mobile and edge devices without compromise (GGUF Q4/Q8)
- **Psychologically Tuned**
Instruction fine-tuned on curated psychotherapeutic data (STF first phase)
- **Recursion-Aware Prompting**
Performs well in reflective, multi-turn conversations
Encourages cognitive reappraisal and pattern mirroring
- **Training Roadmap: Ongoing**
RLHF planned for future iterations
Future releases will include trauma-informed tuning and contextual empathy scaffolds
---
## Limitations & Safety
- **Claria is not a licensed mental health professional.**
It is not suitable for unsupervised therapeutic use, diagnosis, or crisis intervention.
Use responsibly. Review outputs. Think critically.
- May hallucinate or provide confident answers to uncertain topics
- Works best with structured or guided prompts
- Not suitable for open-domain conversation or general use
---
## Deployment & Access
- Available in GGUF format: Q4, Q8, BF16
- Optimized for **Ollama**, **LM Studio**, and other local runners
- Works on mobile and low-resource environments
---
## Notes
This is the first step in a broader initiative to develop compact, reflective AI systems for the augmentation—not replacement—of mental health work.
Future releases will expand Claria’s depth, include RLHF, long-term memory, and finer ethical control
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth
- **Developed by:** drwlf
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-1.7b
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) |
deshanksuman/mbart_50_SinhalaTransliteration | deshanksuman | 2025-05-04T16:36:23Z | 12 | 0 | null | [
"safetensors",
"mbart",
"transliteration",
"sinhala",
"sequence-to-sequence",
"si",
"en",
"dataset:deshanksuman/SwaBhasha_Transliteration_Sinhala",
"license:mit",
"region:us"
] | null | 2025-04-18T16:38:15Z | ---
language:
- si
- en
tags:
- transliteration
- sinhala
- mbart
- sequence-to-sequence
license: mit
datasets:
- deshanksuman/SwaBhasha_Transliteration_Sinhala
metrics:
- accuracy
---
# mBART-50 Sinhala Transliteration Model
This model transliterates Romanized Sinhala text to Sinhala script.
## Model description
This is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt specialized for Sinhala transliteration.
It converts romanized Sinhala (using Latin characters) to proper Sinhala script.
## Intended uses & limitations
This model is intended for transliterating Romanized Sinhala text to proper Sinhala script.
It can be useful for:
- Text input conversion in applications
- Helping non-native speakers type in Sinhala
- Converting legacy text in romanized format to proper Sinhala
### How to use
```python
from transformers import MBartForConditionalGeneration, MBartTokenizerFast
# Load model and tokenizer
model_name = "deshanksuman/mbart_50_SinhalaTransliteration"
tokenizer = MBartTokenizerFast.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)
# Set language codes
tokenizer.src_lang = "en_XX" # Using English as source language token
tokenizer.tgt_lang = "si_LK" # Sinhala as target
# Prepare input
text = "heta api mkda krnne"
inputs = tokenizer(text, return_tensors="pt", max_length=128, padding="max_length", truncation=True)
# Generate output
outputs = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_length=96,
num_beams=5,
early_stopping=True
)
# Decode output
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## Training data
The model was trained on the [deshanksuman/SwaBhasha_Transliteration_Sinhala](https://huggingface.co/deshanksuman/SwaBhasha_Transliteration_Sinhala) dataset, which contains pairs of Romanized Sinhala and corresponding Sinhala script text.
## Training procedure
The model was trained with the following parameters:
- Learning rate: 5e-05
- Batch size: 16
- Number of epochs: 2
- Max sequence length: 128
- Optimizer: AdamW
This is trained for sentence level.
### Examples:
**Example 1:**
- Romanized: Dakunu koreyawe eithihasika
- Expected: දකුණු කොරියාවේ ඓතිහාසික
- Predicted: දකුණු කොරියාවේ ඓතිහාසික
- Correct: True
**Example 2:**
- Romanized: Okoma hodai ganu gathiya
- Expected: ඔක්කොම හොදයි ගෑනු ගතිය
- Predicted: ඕකම හොදයි ගනු ගතිය
- Correct: False
**Example 3:**
- Romanized: Malki akkith ennwa nedenntm godak kemathiyakkila dennm supiriyatam dance
- Expected: මල්කි අක්කිත් එනව නෙදෙන්නටම ගොඩක් කෑමතියිඅක්කිල දෙන්නම සුපිරියටම ඩාන්ස්
- Predicted: මල්කි අක්කිත් එන්නව නෑද්දෑන්ත්ම ගොඩක් කෑමතියිඅකිල දෑන්ඩම් සුපිරියටම ඩාන්ස්
- Correct: False
|
langminer/wav2vec2-custom-asr | langminer | 2025-05-04T16:29:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"asr",
"malay",
"english",
"speech",
"base_model:mesolitica/wav2vec2-xls-r-300m-mixed",
"base_model:finetune:mesolitica/wav2vec2-xls-r-300m-mixed",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-04T16:15:50Z | ---
library_name: transformers
license: mit
metrics:
- name: wer
type: wer
value: 17.26
base_model:
- mesolitica/wav2vec2-xls-r-300m-mixed
pipeline_tag: automatic-speech-recognition
tags:
- wav2vec2
- asr
- automatic-speech-recognition
- malay
- english
- speech
---
# Model Card for Malay-English Fine-Tuned ASR Model
This model was fine-tuned on approximately 50 hours of manually curated Malay-English code-switched audio data for 10 epochs. It achieves a Word Error Rate (WER) of 17.26% on a held-out evaluation set vs 34.29% with base model.
## Model Details
### Model Description
This is a fine-tuned version of the `mesolitica/wav2vec2-xls-r-300m-mixed` model on a custom Malay-English dataset. It is designed to transcribe speech that includes both Malay and English, especially in informal or conversational contexts where code-switching is common.
- **Developed by:** mysterio
- **Model type:** CTC-based automatic speech recognition
- **Languages:** Malay, English
- **License:** MIT
- **Fine-tuned from:** https://huggingface.co/mesolitica/wav2vec2-xls-r-300m-mixed
### Model Sources
- **Base Model:** [mesolitica/wav2vec2-xls-r-300m-mixed](https://huggingface.co/mesolitica/wav2vec2-xls-r-300m-mixed)
## Uses
### Direct Use
This model can be used to transcribe conversational Malay-English audio recordings, especially in domains such as:
- Broadcast interviews
- YouTube vlogs
- Podcasts
- Community recordings
### Downstream Use
The model can be fine-tuned further or used as part of downstream applications such as:
- Real-time transcription services
- Voice assistants tailored for Malaysian users
- Speech-driven translation systems
### Out-of-Scope Use
- High-stakes transcription scenarios (e.g., legal or medical contexts) where exact word accuracy is critical
- Non-Malay, non-English languages
- Noisy or far-field audio environments (unless fine-tuned further)
## Bias, Risks, and Limitations
### Known Limitations
- May underperform on accents or dialects not well-represented in training data
- Inconsistent casing or punctuation handling (model is CTC-based)
- Limited robustness to background noise or overlapping speakers
### Recommendations
- Always verify outputs for critical tasks
- Pair with punctuation restoration or diarization for production-grade use
- Retrain with domain-specific data for higher accuracy
## How to Get Started with the Model
```python
from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="langminer/wav2vec2-custom-asr")
transcription = asr("your_audio_file.wav")
print(transcription) |
TareksLab/Persona-V2-70B | TareksLab | 2025-05-04T16:27:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:Sao10K/70B-L3.3-mhnnn-x1",
"base_model:merge:Sao10K/70B-L3.3-mhnnn-x1",
"base_model:SentientAGI/Dobby-Unhinged-Llama-3.3-70B",
"base_model:merge:SentientAGI/Dobby-Unhinged-Llama-3.3-70B",
"base_model:flammenai/Llama3.1-Flammades-70B",
"base_model:merge:flammenai/Llama3.1-Flammades-70B",
"base_model:flammenai/Mahou-1.5-llama3.1-70B",
"base_model:merge:flammenai/Mahou-1.5-llama3.1-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:35:48Z | ---
base_model:
- flammenai/Llama3.1-Flammades-70B
- Sao10K/70B-L3.3-mhnnn-x1
- flammenai/Mahou-1.5-llama3.1-70B
- SentientAGI/Dobby-Unhinged-Llama-3.3-70B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Sao10K/70B-L3.3-mhnnn-x1](https://huggingface.co/Sao10K/70B-L3.3-mhnnn-x1) as a base.
### Models Merged
The following models were included in the merge:
* [flammenai/Llama3.1-Flammades-70B](https://huggingface.co/flammenai/Llama3.1-Flammades-70B)
* [flammenai/Mahou-1.5-llama3.1-70B](https://huggingface.co/flammenai/Mahou-1.5-llama3.1-70B)
* [SentientAGI/Dobby-Unhinged-Llama-3.3-70B](https://huggingface.co/SentientAGI/Dobby-Unhinged-Llama-3.3-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: flammenai/Mahou-1.5-llama3.1-70B
- model: flammenai/Llama3.1-Flammades-70B
- model: SentientAGI/Dobby-Unhinged-Llama-3.3-70B
base_model: Sao10K/70B-L3.3-mhnnn-x1
merge_method: model_stock
parameters:
int8_mask: true
dtype: float32
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: base
pad_to_multiple_of: 8
```
|
houck2040/detr_249 | houck2040 | 2025-05-04T16:26:40Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"detr",
"object-detection",
"license:mit",
"endpoints_compatible",
"region:us"
] | object-detection | 2023-08-26T17:30:53Z | ---
license: mit
---
This is a test of a fine tuned detr model using 249 images of utility poles trained on 10 epochs.
See tutorial below for more information.
https://huggingface.co/docs/transformers/tasks/object_detection
|
dgambettaphd/M_llm2_gen1_WXS_doc1000_synt64_lr1e-04_acm_MPP | dgambettaphd | 2025-05-04T16:26:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:25:45Z | ---
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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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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## 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
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### 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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jdchang/full-with-label-bs-1024-sg-2-step-12170 | jdchang | 2025-05-04T16:25:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:25:02Z | ---
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]
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## 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
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
kostiantynk-outlook/f8c7bc15-9d6c-4f0e-ac1e-a2548a85d08e | kostiantynk-outlook | 2025-05-04T16:23:42Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"dataset:92a4ab705f6ca41d_train_data.json",
"base_model:unsloth/llama-2-7b",
"base_model:adapter:unsloth/llama-2-7b",
"region:us"
] | null | 2025-05-04T16:23:18Z | ---
library_name: peft
tags:
- generated_from_trainer
datasets:
- 92a4ab705f6ca41d_train_data.json
base_model: unsloth/llama-2-7b
model-index:
- name: kostiantynk-outlook/f8c7bc15-9d6c-4f0e-ac1e-a2548a85d08e
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. -->
# kostiantynk-outlook/f8c7bc15-9d6c-4f0e-ac1e-a2548a85d08e
This model was trained from scratch on the /workspace/input_data/92a4ab705f6ca41d_train_data.json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4996
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
kostiantynk1205/b123d3fa-afe9-4cec-8313-0b097a205a61 | kostiantynk1205 | 2025-05-04T16:22:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:21:51Z | ---
library_name: transformers
model_name: kostiantynk1205/b123d3fa-afe9-4cec-8313-0b097a205a61
tags:
- generated_from_trainer
- unsloth
licence: license
---
# Model Card for kostiantynk1205/b123d3fa-afe9-4cec-8313-0b097a205a61
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
``` |
jdchang/full-with-label-bs-1024-sg-2-step-12150 | jdchang | 2025-05-04T16:22:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:22:06Z | ---
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]
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<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## 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]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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mantasviaz/news-bert-model | mantasviaz | 2025-05-04T16:20:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T16:18:54Z | ---
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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
mlfoundations-dev/no_pipeline_math_100k | mlfoundations-dev | 2025-05-04T16:20:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T22:05:52Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: no_pipeline_math_100k
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. -->
# no_pipeline_math_100k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/no_pipeline_math_100k 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: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
bruhzair/ignore-merge-14 | bruhzair | 2025-05-04T16:19:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:52:36Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# nl2
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the Passthrough merge method.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
modules:
default:
slices:
- sources:
- layer_range: [0, 4]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [2, 4]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [4, 8]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [6, 8]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 12]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [10, 12]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [12, 16]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [14, 16]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [16, 20]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [18, 20]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [20, 24]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [22, 24]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 28]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [26, 28]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [28, 32]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [30, 32]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [32, 36]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [34, 36]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [36, 40]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [38, 40]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [40, 44]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [42, 44]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [44, 48]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [46, 48]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [48, 52]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [50, 52]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [52, 56]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [54, 56]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [56, 60]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [58, 60]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [60, 64]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [62, 64]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [64, 68]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [66, 68]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [68, 72]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [70, 72]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [72, 76]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [74, 76]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [76, 80]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
- sources:
- layer_range: [78, 80]
model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
```
|
Baselhany/Graduation_Project_Distil_Whisper_base22 | Baselhany | 2025-05-04T16:17:42Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-03T17:04:05Z | ---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper base AR - BA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper base AR - BA
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0171
- Wer: 0.0848
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.7128 | 0.9937 | 119 | 0.0221 | 0.1074 |
| 5.728 | 1.9937 | 238 | 0.0199 | 0.1025 |
| 4.8865 | 2.9937 | 357 | 0.0200 | 0.1030 |
| 4.235 | 3.9937 | 476 | 0.0204 | 0.0996 |
| 3.663 | 4.9937 | 595 | 0.0194 | 0.0927 |
| 2.8223 | 5.9937 | 714 | 0.0188 | 0.0884 |
| 2.5177 | 6.9937 | 833 | 0.0181 | 0.0852 |
| 2.2826 | 7.9937 | 952 | 0.0175 | 0.0875 |
| 2.0892 | 8.9937 | 1071 | 0.0171 | 0.0871 |
| 1.9369 | 9.9937 | 1190 | 0.0171 | 0.0848 |
### Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
|
carowagner/classify-questions-2B | carowagner | 2025-05-04T16:17:11Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T16:16:21Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.21065853536128998
f1_macro: 0.7477924163007036
f1_micro: 0.93
f1_weighted: 0.9184048732115031
precision_macro: 0.975177304964539
precision_micro: 0.93
precision_weighted: 0.9352127659574468
recall_macro: 0.6481481481481481
recall_micro: 0.93
recall_weighted: 0.93
accuracy: 0.93
|
amindcoder/llama_3_1_8b_ft_4th_may | amindcoder | 2025-05-04T16:15:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:14:56Z | ---
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:** amindcoder
- **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)
|
lokeshch19/ModernPubMedBERT | lokeshch19 | 2025-05-04T16:14:49Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"pubmed",
"medical",
"biomedical",
"clinical",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-04-16T04:23:58Z | ---
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- pubmed
- medical
- biomedical
- clinical
- modernbert
---
# ModernPubMedBERT
This is a [sentence-transformers](https://www.SBERT.net) model trained on PubMed dataset. It maps sentences & paragraphs to a dense vector space with multiple embedding dimensions (768, 512, 384, 256, 128) using Matryoshka Representation Learning. This allows flexibility to use different embedding sizes based on your application needs while maintaining high performance for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Language:** en
- **License:** apache-2.0
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("lokeshch19/ModernPubMedBERT")
# Run inference
sentences = [
"The patient was diagnosed with type 2 diabetes mellitus",
"The individual shows symptoms of hyperglycemia and insulin resistance",
"Metastatic cancer requires aggressive treatment approaches"
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
384,
256,
128
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.7.0+cu128
- Accelerate: 1.6.0
- Datasets: 3.5.1
- 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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}
}
``` |
young0ha/llama-3.2-1b-ko-morpheme | young0ha | 2025-05-04T16:11:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:09:14Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** young0ha
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-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)
|
mluger/vitFaceExpressionCrossEntropyLoss | mluger | 2025-05-04T16:11:11Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-21T13:54:58Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitFaceExpressionCrossEntropyLoss
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. -->
# vitFaceExpressionCrossEntropyLoss
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8619
- Accuracy: 0.7033
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: cosine
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2523 | 1.0 | 898 | 1.0366 | 0.6158 |
| 0.9007 | 2.0 | 1796 | 0.9029 | 0.6723 |
| 0.7628 | 3.0 | 2694 | 0.8649 | 0.6877 |
| 0.6649 | 4.0 | 3592 | 0.8663 | 0.6946 |
| 0.5811 | 5.0 | 4490 | 0.8625 | 0.6974 |
| 0.4833 | 6.0 | 5388 | 0.8590 | 0.7027 |
| 0.4175 | 7.0 | 6286 | 0.8605 | 0.7016 |
| 0.3912 | 8.0 | 7184 | 0.8619 | 0.7033 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
carowagner/classify-questions-2A | carowagner | 2025-05-04T16:09:55Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T16:09:03Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.3329247236251831
f1_macro: 0.8700832445654317
f1_micro: 0.9
f1_weighted: 0.9012285477571311
precision_macro: 0.906878306878307
precision_micro: 0.9
precision_weighted: 0.9055238095238096
recall_macro: 0.8472222222222222
recall_micro: 0.9
recall_weighted: 0.9
accuracy: 0.9
|
phospho-app/Gr00t_simple_pawn_move_v3_500-94ahougeru | phospho-app | 2025-05-04T16:09:51Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-04T15:57:58Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [dopaul/simple_pawn_move_v3](https://huggingface.co/datasets/dopaul/simple_pawn_move_v3)
- **Wandb run URL**: None
- **Epochs**: 5
- **Batch size**: 64
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
kedziorek/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fishy_majestic_cassowary | kedziorek | 2025-05-04T16:07:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fishy majestic cassowary",
"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 | 2025-05-04T15:53:06Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fishy_majestic_cassowary
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fishy majestic cassowary
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fishy_majestic_cassowary
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="kedziorek/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fishy_majestic_cassowary", 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: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- 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}}
}
``` |
dgiang02/GRPO_Qwen25_15B_16_005_2000kmap | dgiang02 | 2025-05-04T16:07:13Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T16:06:36Z | ---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# 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] |
hamna11/whisper-tiny-en-asian-accent | hamna11 | 2025-05-04T16:07:10Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"en",
"dataset:WillHeld/india_accent_cv",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-04T13:11:45Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- whisper-event
- generated_from_trainer
datasets:
- WillHeld/india_accent_cv
metrics:
- wer
model-index:
- name: Whisper tiny En - Hamna Ramzan
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: South_asian_accent
type: WillHeld/india_accent_cv
metrics:
- name: Wer
type: wer
value: 16.416369438708468
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper tiny En - Hamna Ramzan
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the South_asian_accent dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3981
- Wer: 16.4164
## 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: 64
- 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
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4438 | 0.25 | 1000 | 0.5724 | 23.7751 |
| 0.3441 | 1.1425 | 2000 | 0.4521 | 18.5095 |
| 0.2427 | 2.035 | 3000 | 0.4335 | 18.0589 |
| 0.2993 | 2.285 | 4000 | 0.3981 | 16.4164 |
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.5.2.dev0
- Tokenizers 0.21.1
|
anishreddy91/HealthCare_finetuned_phi1_5 | anishreddy91 | 2025-05-04T16:05:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:05:47Z | ---
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] |
carowagner/classify-questions-1B | carowagner | 2025-05-04T16:05:19Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T16:04:23Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.16687506437301636
f1_macro: 0.9044444444444445
f1_micro: 0.96
f1_weighted: 0.9579619047619048
precision_macro: 0.9844961240310077
precision_micro: 0.96
precision_weighted: 0.9618604651162791
recall_macro: 0.8452380952380952
recall_micro: 0.96
recall_weighted: 0.96
accuracy: 0.96
|
yusuke111/myBit-Llama2-jp-127M-2B4TLike-aozora-sort-3epc | yusuke111 | 2025-05-04T16:03:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bit_llama",
"text-generation",
"generated_from_trainer",
"custom_code",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-05-04T12:03:08Z | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: myBit-Llama2-jp-127M-2B4TLike-aozora-sort-3epc
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. -->
# myBit-Llama2-jp-127M-2B4TLike-aozora-sort-3epc
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2302
## 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: 0.0024
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.9258 | 0.0883 | 100 | 5.2708 |
| 4.7937 | 0.1765 | 200 | 4.4660 |
| 4.2565 | 0.2648 | 300 | 4.1755 |
| 3.9951 | 0.3530 | 400 | 4.0060 |
| 3.8438 | 0.4413 | 500 | 3.8854 |
| 3.7223 | 0.5296 | 600 | 3.7829 |
| 3.6523 | 0.6178 | 700 | 3.7125 |
| 3.5985 | 0.7061 | 800 | 3.6535 |
| 3.5666 | 0.7944 | 900 | 3.6039 |
| 3.5519 | 0.8826 | 1000 | 3.5693 |
| 3.5365 | 0.9709 | 1100 | 3.5404 |
| 3.6085 | 1.0591 | 1200 | 3.5638 |
| 3.4953 | 1.1474 | 1300 | 3.4983 |
| 3.425 | 1.2357 | 1400 | 3.4737 |
| 3.3693 | 1.3239 | 1500 | 3.4579 |
| 3.3396 | 1.4122 | 1600 | 3.4431 |
| 3.3187 | 1.5004 | 1700 | 3.4259 |
| 3.3013 | 1.5887 | 1800 | 3.4121 |
| 3.3036 | 1.6770 | 1900 | 3.4004 |
| 3.2947 | 1.7652 | 2000 | 3.3808 |
| 3.3041 | 1.8535 | 2100 | 3.3653 |
| 3.304 | 1.9417 | 2200 | 3.3541 |
| 3.3582 | 2.0300 | 2300 | 3.4233 |
| 3.3097 | 2.1183 | 2400 | 3.3351 |
| 3.2426 | 2.2065 | 2500 | 3.3234 |
| 3.2034 | 2.2948 | 2600 | 3.3149 |
| 3.1675 | 2.3831 | 2700 | 3.3033 |
| 3.1611 | 2.4713 | 2800 | 3.2953 |
| 3.1344 | 2.5596 | 2900 | 3.2832 |
| 3.1391 | 2.6478 | 3000 | 3.2729 |
| 3.1324 | 2.7361 | 3100 | 3.2572 |
| 3.1355 | 2.8244 | 3200 | 3.2440 |
| 3.1417 | 2.9126 | 3300 | 3.2302 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF | mradermacher | 2025-05-04T16:02:42Z | 353 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:TareksGraveyard/Progenitor-V1.2-LLaMa-70B",
"base_model:quantized:TareksGraveyard/Progenitor-V1.2-LLaMa-70B",
"license:llama3.3",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-01-31T20:58:55Z | ---
base_model: TareksGraveyard/Progenitor-V1.2-LLaMa-70B
language:
- en
library_name: transformers
license: llama3.3
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
weighted/imatrix quants of https://huggingface.co/TareksGraveyard/Progenitor-V1.2-LLaMa-70B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 24.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q4_1.gguf) | i1-Q4_1 | 44.4 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
TOMFORD79/Fly62 | TOMFORD79 | 2025-05-04T16:00:07Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-04T13:15:43Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Mr-FineTuner/Test_02_gemma_trainPercen_myValidator | Mr-FineTuner | 2025-05-04T15:59:50Z | 0 | 0 | null | [
"safetensors",
"gemma",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T15:01:50Z |
# Fine-Tuned LLaMA-3-8B CEFR Model
This is a fine-tuned version of `unsloth/gemma-7b-bnb-4bit` for CEFR-level sentence generation, evaluated with a fine-tuned classifier from `Mr-FineTuner/Skripsi_validator_best_model`.
- **Base Model**: unsloth/gemma-7b-bnb-4bit
- **Fine-Tuning**: LoRA with SMOTE-balanced dataset
- **Training Details**:
- Dataset: CEFR-level sentences with SMOTE and undersampling for balance
- LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5
- Training Args: learning_rate=2e-5, batch_size=8, epochs=0.01, cosine scheduler
- Optimizer: adamw_8bit
- Early Stopping: Patience=3, threshold=0.01
- **Evaluation Metrics (Exact Matches)**:
- CEFR Classifier Accuracy: 0.167
- Precision (Macro): 0.028
- Recall (Macro): 0.167
- F1-Score (Macro): 0.048
- **Evaluation Metrics (Within ±1 Level)**:
- CEFR Classifier Accuracy: 0.500
- Precision (Macro): 0.375
- Recall (Macro): 0.500
- F1-Score (Macro): 0.400
- **Other Metrics**:
- Perplexity: 5.344
- Diversity (Unique Sentences): 0.100
- Inference Time (ms): 5802.883
- Model Size (GB): 4.8
- Robustness (F1): 0.045
- **Confusion Matrix (Exact Matches)**:
- CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv)
- Image: [confusion_matrix_exact.png](confusion_matrix_exact.png)
- **Confusion Matrix (Within ±1 Level)**:
- CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv)
- Image: [confusion_matrix_within1.png](confusion_matrix_within1.png)
- **Per-Class Confusion Metrics (Exact Matches)**:
- A1: TP=0, FP=0, FN=10, TN=50
- A2: TP=10, FP=50, FN=0, TN=0
- B1: TP=0, FP=0, FN=10, TN=50
- B2: TP=0, FP=0, FN=10, TN=50
- C1: TP=0, FP=0, FN=10, TN=50
- C2: TP=0, FP=0, FN=10, TN=50
- **Per-Class Confusion Metrics (Within ±1 Level)**:
- A1: TP=10, FP=0, FN=0, TN=50
- A2: TP=10, FP=30, FN=0, TN=20
- B1: TP=10, FP=0, FN=0, TN=50
- B2: TP=0, FP=0, FN=10, TN=50
- C1: TP=0, FP=0, FN=10, TN=50
- C2: TP=0, FP=0, FN=10, TN=50
- **Usage**:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test_02_llama_trainPercen_myValidator_2ndTry")
tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test_02_llama_trainPercen_myValidator_2ndTry")
# Example inference
prompt = "<|user|>Generate a CEFR B1 level sentence.<|end|>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Uploaded using `huggingface_hub`.
|
Mug3n24/bart-base-finetune-finetuned-research-papers-XX | Mug3n24 | 2025-05-04T15:59:34Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-04-26T10:02:18Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-base-finetune-finetuned-research-papers-XX
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. -->
# bart-base-finetune-finetuned-research-papers-XX
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3585
- Rouge1: 43.9906
- Rouge2: 23.635
- Rougel: 38.656
- Rougelsum: 38.6946
- Gen Len: 16.5285
## 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: 5.6e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.5613 | 0.499 | 499 | 2.5247 | 42.9693 | 23.0702 | 38.0214 | 38.0551 | 15.1225 |
| 2.7006 | 0.998 | 998 | 2.3682 | 43.4559 | 23.4682 | 38.3135 | 38.3676 | 15.0435 |
| 2.0314 | 1.4970 | 1497 | 2.3936 | 43.6633 | 23.9315 | 38.625 | 38.6834 | 15.5975 |
| 1.982 | 1.996 | 1996 | 2.3585 | 43.9906 | 23.635 | 38.656 | 38.6946 | 16.5285 |
| 1.6361 | 2.495 | 2495 | 2.4054 | 44.3992 | 23.9348 | 39.1448 | 39.2071 | 16.0925 |
| 1.6261 | 2.9940 | 2994 | 2.3747 | 44.0552 | 23.9472 | 38.9413 | 38.9995 | 16.045 |
| 1.3756 | 3.493 | 3493 | 2.4233 | 44.5154 | 24.2567 | 39.1726 | 39.2081 | 16.366 |
| 1.3683 | 3.992 | 3992 | 2.4261 | 44.6882 | 24.3496 | 39.2746 | 39.3072 | 16.271 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF | mradermacher | 2025-05-04T15:59:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-S",
"base_model:quantized:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-S",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-04T09:50:10Z | ---
base_model: Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-S
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-S
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ1_M.gguf) | i1-IQ1_M | 4.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ2_M.gguf) | i1-IQ2_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-S-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-S.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ma921/phi-2-sft-golden-hh | ma921 | 2025-05-04T15:58:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:54:35Z | ---
library_name: transformers
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: phi-2-sft-golden-hh
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-2-sft-golden-hh
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: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
afuada/dummy-repo-manual | afuada | 2025-05-04T15:56:48Z | 0 | 0 | null | [
"safetensors",
"camembert",
"region:us"
] | null | 2025-05-04T15:30:08Z | # Dummy Model
This is dummy model
|
phospho-app/Gr00t_simple_pawn_move_v3_500-rlgkmw3sk5 | phospho-app | 2025-05-04T15:54:36Z | 0 | 0 | null | [
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-04T15:46:38Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Traceback (most recent call last):
File "/root/src/helper.py", line 224, in predict
raise RuntimeError(error_msg)
RuntimeError: Training process failed with exit code 1:
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/transformers/modeling_flash_attention_utils.py", line 296, in _flash_attention_forward
attn_output = flash_attn_func(
^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/flash_attn/flash_attn_interface.py", line 1107, in flash_attn_func
def flash_attn_func(
KeyboardInterrupt
78%|███████▊ | 463/595 [06:01<01:42, 1.28it/s]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/src/helper.py", line 226, in predict
raise RuntimeError(e)
RuntimeError: Training process failed with exit code 1:
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/transformers/modeling_flash_attention_utils.py", line 296, in _flash_attention_forward
attn_output = flash_attn_func(
^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/flash_attn/flash_attn_interface.py", line 1107, in flash_attn_func
def flash_attn_func(
KeyboardInterrupt
78%|███████▊ | 463/595 [06:01<01:42, 1.28it/s]
```
## Training parameters:
- **Dataset**: [dopaul/simple_pawn_move_v3](https://huggingface.co/datasets/dopaul/simple_pawn_move_v3)
- **Wandb run URL**: None
- **Epochs**: 5
- **Batch size**: 64
- **Training steps**: 594
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
vertings6/25b6a93a-42d7-49a9-92e5-059202ecc1e3 | vertings6 | 2025-05-04T15:54:24Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:The-matt/llama2_ko-7b_distinctive-snowflake-182_1060",
"base_model:adapter:The-matt/llama2_ko-7b_distinctive-snowflake-182_1060",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T15:41:58Z | ---
library_name: peft
base_model: The-matt/llama2_ko-7b_distinctive-snowflake-182_1060
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 25b6a93a-42d7-49a9-92e5-059202ecc1e3
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: `0.4.1`
```yaml
absolute_data_files: true
adapter: lora
base_model: The-matt/llama2_ko-7b_distinctive-snowflake-182_1060
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 5b937aaaaa1b4833_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5b937aaaaa1b4833_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 144
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vertings6/25b6a93a-42d7-49a9-92e5-059202ecc1e3
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 4
mixed_precision: bf16
mlflow_experiment_name: /tmp/5b937aaaaa1b4833_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 89c6d633-5ae5-4ed8-aed8-e6cc264c27ff
wandb_project: s56-32
wandb_run: your_name
wandb_runid: 89c6d633-5ae5-4ed8-aed8-e6cc264c27ff
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 25b6a93a-42d7-49a9-92e5-059202ecc1e3
This model is a fine-tuned version of [The-matt/llama2_ko-7b_distinctive-snowflake-182_1060](https://huggingface.co/The-matt/llama2_ko-7b_distinctive-snowflake-182_1060) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4635
## 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: 3e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5409 | 0.1871 | 200 | 1.4635 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/Malaysian-Llama-3.2-1B-GGUF | mradermacher | 2025-05-04T15:51:47Z | 266 | 0 | transformers | [
"transformers",
"gguf",
"ms",
"en",
"zh",
"ta",
"dataset:mesolitica/Malaysian-SFT",
"base_model:mesolitica/Malaysian-Llama-3.2-1B-Instruct-v0.2",
"base_model:quantized:mesolitica/Malaysian-Llama-3.2-1B-Instruct-v0.2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-07T06:13:08Z | ---
base_model: mesolitica/Malaysian-Llama-3.2-1B-Instruct-v0.2
datasets:
- mesolitica/Malaysian-SFT
language:
- ms
- en
- zh
- ta
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/mesolitica/Malaysian-Llama-3.2-1B-Instruct-v0.2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q3_K_L.gguf) | Q3_K_L | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q6_K.gguf) | Q6_K | 1.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-Llama-3.2-1B-GGUF/resolve/main/Malaysian-Llama-3.2-1B.f16.gguf) | f16 | 3.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
nice2mitya/a_6835514244 | nice2mitya | 2025-05-04T15:48:51Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-04T15:19:54Z | ---
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
--- |
RazzzHF/razzzModels | RazzzHF | 2025-05-04T15:46:42Z | 0 | 4 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-08-12T02:28:15Z | ---
license: cc-by-nc-sa-4.0
---
|
blueyori/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scavenging_slow_tuna | blueyori | 2025-05-04T15:46:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scavenging slow tuna",
"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 | 2025-05-01T05:55:23Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scavenging_slow_tuna
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scavenging slow tuna
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scavenging_slow_tuna
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="blueyori/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scavenging_slow_tuna", 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: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}
``` |
bruhzair/ignore-merge-13 | bruhzair | 2025-05-04T15:45:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:17:02Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# FSW2
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the Passthrough merge method.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
modules:
default:
slices:
- sources:
- layer_range: [0, 4]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [2, 4]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [4, 8]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [6, 8]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 12]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [10, 12]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [12, 16]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [14, 16]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [16, 20]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [18, 20]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [20, 24]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [22, 24]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 28]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [26, 28]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [28, 32]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [30, 32]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [32, 36]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [34, 36]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [36, 40]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [38, 40]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [40, 44]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [42, 44]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [44, 48]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [46, 48]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [48, 52]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [50, 52]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [52, 56]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [54, 56]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [56, 60]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [58, 60]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [60, 64]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [62, 64]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [64, 68]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [66, 68]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [68, 72]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [70, 72]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [72, 76]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [74, 76]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [76, 80]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- sources:
- layer_range: [78, 80]
model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
```
|
fffanx/Llama-3.2-1B-Instruct-GRPO-agent19_E1 | fffanx | 2025-05-04T15:45:00Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T01:12:46Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent19_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent19_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent19_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent18_E1 | fffanx | 2025-05-04T15:44:16Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T01:12:02Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent18_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent18_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent18_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Nickybcybc/Qwen3-lora_model | Nickybcybc | 2025-05-04T15:44:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T15:43:18Z | ---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Nickybcybc
- **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)
|
fffanx/Llama-3.2-1B-Instruct-GRPO-agent16_E1 | fffanx | 2025-05-04T15:42:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T01:10:32Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent16_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent16_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent16_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
vangard703/single_image_11_tasks_single_tasks_L1_scale_5_tasks_qa2 | vangard703 | 2025-05-04T15:41:08Z | 0 | 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 | 2025-05-04T15:35:41Z | ---
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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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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
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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
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#### Preprocessing [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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fffanx/Llama-3.2-1B-Instruct-GRPO-agent13_E1 | fffanx | 2025-05-04T15:40:35Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T01:08:07Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent13_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent13_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent13_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ustc-community/dfine_l_coco | ustc-community | 2025-05-04T15:39:25Z | 68 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-02-22T12:04:35Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_l_coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_l_coco")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
venkatramaraju/polyglot | venkatramaraju | 2025-05-04T15:39:11Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T15:34:31Z | # 🔠 Polyglot
Polyglot is a high-performance multilingual tokenizer, built entirely from scratch in Go, that efficiently compresses text from 10 diverse languages using the Byte-Pair Encoding (BPE) algorithm. The system supports English, Hebrew, Bengali, Vietnamese, Korean, Arabic, Russian, Thai, Chinese, and Japanese.
## 📊 Metrics
- **Compression Ratio:** 3.0
- **Vocabulary Size:** 40,146
- **Total Training Corpus:** 432,584,912 characters (10M sentences)
## 📈 Benchmarking
Polyglot is evaluated against five SOTA tokenizers: Tiktoken, Transformers, SentencePiece, mBERT, and XLM. A total of 100,000 unseen sentences—10,000 per language across 10 languages—were sampled from the [statmt/cc10](https://huggingface.co/datasets/statmt/cc100?p=1) dataset. For each tokenizer and language, the mean compression ratio and token fertility were computed over the corresponding 10,000 sentences.
### 🔄 Compression Ratio
| Language | polyglot | mbert | sentencepiece | tiktoken | transformers | xlm |
|----------|----------|--------|---------------|----------|--------------|------|
| ar | 2.61 | 2.43 | 2.76 | 3.03 | 1.00 | 3.03 |
| bn | 2.80 | 2.07 | 2.84 | 2.56 | 0.52 | 2.83 |
| en | 3.75 | 3.77 | 3.90 | 4.43 | 4.21 | 3.77 |
| he | 2.32 | 2.29 | 2.51 | 2.54 | 0.88 | 2.80 |
| ja | 1.51 | 1.25 | 11.80 | 1.35 | 0.72 | 1.78 |
| ko | 1.51 | 1.48 | 1.93 | 1.62 | 0.50 | 1.78 |
| ru | 3.36 | 3.17 | 1.37 | 3.72 | 0.94 | 3.85 |
| th | 2.49 | 1.45 | 6.49 | 2.30 | 0.55 | 3.22 |
| vi | 2.86 | 3.13 | 1.26 | 3.20 | 1.14 | 3.42 |
| zh | 1.36 | 1.04 | 5.40 | 1.32 | 0.50 | 1.51 |
#### Compression Ratio Rankings
| Rank | Tokenizer | Average Compression Ratio |
|:----:|:--------------:|:---------------------------:|
| 1 | sentencepiece | **4.03** |
| 2 | xlm | **2.80** |
| 3 | tiktoken | **2.61** |
| 4 | polyglot | **2.46** |
| 5 | mbert | **2.21** |
| 6 | transformers | **1.10** |
### 🧩 Token Fertility
| Language | polyglot | mbert | sentencepiece | tiktoken | transformers | xlm |
|----------|----------|--------|--------------|----------|--------------|------|
| ar | 1.96 | 2.10 | 1.85 | 1.69 | 5.10 | 1.68 |
| bn | 1.84 | 2.50 | 1.82 | 2.02 | 10.01 | 1.82 |
| en | 1.19 | 1.19 | 1.15 | 1.01 | 1.06 | 1.19 |
| he | 2.08 | 2.10 | 1.92 | 1.90 | 5.46 | 1.72 |
| ja | 1.12 | 1.35 | 0.14 | 1.25 | 2.35 | 0.95 |
| ko | 1.91 | 1.95 | 1.50 | 1.79 | 5.73 | 1.63 |
| ru | 1.62 | 1.72 | 3.97 | 1.46 | 5.82 | 1.42 |
| th | 1.76 | 3.02 | 0.67 | 1.90 | 7.96 | 1.36 |
| vi | 1.65 | 1.50 | 3.74 | 1.47 | 4.13 | 1.37 |
| zh | 1.21 | 1.58 | 0.30 | 1.25 | 3.31 | 1.09 |
#### Token Fertility Rankings
| Rank | Tokenizer | Average Token Fertility |
|:----:|:--------------:|:--------------------------:|
| 1 | transformers | **5.09** |
| 2 | mbert | **1.90** |
| 3 | sentencepiece | **1.71** |
| 4 | polyglot | **1.63** |
| 5 | tiktoken | **1.57** |
| 6 | xlm | **1.42** |
### 🌍 📊 Cross-Lingual Consistency
A primary goal of Polyglot is to achieve uniform tokenization quality across diverse languages. The following table compares how consistently each tokenizer performs across all 10 evaluated languages.
| Tokenizer | Compression Ratio σ | Token Fertility σ | Total σ |
|---------------|------------------------|----------------------|-----------------------------|
| xlm | 0.80 | 0.27 | 1.07 |
| polyglot | 0.76 | 0.33 | 1.09 |
| tiktoken | 0.97 | 0.32 | 1.29 |
| mbert | 0.88 | 0.53 | 1.41 |
| transformers | 1.06 | 2.48 | 3.54 |
## 🏋️ Training
- **Dataset:** The tokenizer was trained on 10M sentences from the [opus-100 dataset](https://huggingface.co/datasets/Helsinki-NLP/opus-100), with 1M sentences per language. The language set was carefully selected to incorporate a sufficiently diverse range of scripts in our training dataset.
- **Training Process:** The current version has a compression ratio of 3.0. Training runs are in progress to push this to 5.0.
- **Implementation:** Data aggregation and formatting were implemented in Python. The core BPE algorithm and server were written in Go. Training data was chunked and streamed from S3 for efficient processing on machines of various sizes.
## 🚀 Deployment
Deploy Polyglot locally using Docker with the following commands:
```bash
# Build the Docker image
docker build -t polyglot-app .
# Run the container
docker run -p 8080:8080 -p 3000:3000 polyglot-app
```
Navigate to [localhost:3000](http://localhost:3000/) to interface with the tool.
## 🌐 Website
Visit [Polyglot's website](https://polyglot-k6h6.onrender.com/). Please note that the host instance automatically spins down during periods of inactivity which may result in delays due to cold starts. It may take upto a minute to startup.
Computation speed may vary between the hosted version and local deployment, depending on your local hardware specifications and the resources allocated by Render's infrastructure.
**Website**
**Local**
## 🖥️ Frontend
The `ui` directory contains an intuitive user interface that provides the following capabilities:
- Text input for tokenization
- Visualization of tokenized segments and their corresponding integer representations
- Decoding functionality to reconstruct the original text
- Real-time metrics displaying compression ratio, token-to-character counts for performance analysis, and computation times.
## ⚙️ Backend
The backend exposes two RESTful endpoints:
- **`/encode`:** Processes input text and returns the corresponding token sequence with text representations
- **`/decode`:** Accepts a token sequence and reconstructs the original text
## 📄 License
This project is licensed under the MIT License.
|
ustc-community/dfine_x_coco | ustc-community | 2025-05-04T15:39:10Z | 97 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-02-06T15:36:03Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_x_coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_x_coco")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent11_E1 | fffanx | 2025-05-04T15:39:07Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T01:06:15Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent11_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent11_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent11_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ustc-community/dfine_x_obj2coco | ustc-community | 2025-05-04T15:38:31Z | 53 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T11:32:10Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_x_obj2coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_x_obj2coco")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
Momin-Shahzad/ppo-CartPole-v1 | Momin-Shahzad | 2025-05-04T15:37:52Z | 0 | 0 | null | [
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-04T14:13:19Z | ---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 265.10 +/- 96.27
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'repo_id': 'Momin-Shahzad/ppo-CartPole-v1'
'exp_name': 'ppo-CartPole-v1'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'batch_size': 512
'minibatch_size': 128}
```
|
ustc-community/dfine_x_obj365 | ustc-community | 2025-05-04T15:37:42Z | 78 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"dataset:objects365",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T12:55:40Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
- objects365
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_x_obj365")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_x_obj365")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO and Objects365 (Lin et al. [2014]) train2017 and validated on COCO + Objects365 val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent9_E1 | fffanx | 2025-05-04T15:37:39Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T00:40:58Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent9_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent9_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent9_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ustc-community/dfine_l_obj365 | ustc-community | 2025-05-04T15:37:30Z | 96 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"dataset:objects365",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T13:00:47Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
- objects365
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_l_obj365")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_l_obj365")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO and Objects365 (Lin et al. [2014]) train2017 and validated on COCO + Objects365 val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
Jeremmmyyyyy/gemma-3-1b-Math | Jeremmmyyyyy | 2025-05-04T15:34:57Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:09:51Z | ---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: gemma-3-1b-it
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for gemma-3-1b-it
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-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="Jeremmmyyyyy/gemma-3-1b-it", 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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E1 | fffanx | 2025-05-04T15:34:44Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T00:39:06Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent5_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent5_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent4_E1 | fffanx | 2025-05-04T15:34:00Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T00:38:44Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent4_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent4_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent4_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
MAAT-EL-DUAT/vX.OMEGA | MAAT-EL-DUAT | 2025-05-04T15:33:52Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T15:26:53Z | 
|
fffanx/Llama-3.2-1B-Instruct-GRPO-agent3_E1 | fffanx | 2025-05-04T15:33:25Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T23:36:30Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent3_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent3_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent3_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
misterpawan/phi-4-merged-all-slerp | misterpawan | 2025-05-04T15:31:50Z | 0 | 0 | null | [
"safetensors",
"llama",
"merge",
"mergekit",
"slerp",
"generated_from_script",
"phi-4",
"license:mit",
"region:us"
] | null | 2025-05-04T15:26:35Z | ---
license: mit # Or appropriate license based on models merged
tags:
- merge
- mergekit
- slerp
- generated_from_script
- phi-4 # Tag with base/first model
base_model_architecture: unsloth/phi-4
---
# Merged Model: phi-4-merged-all-slerp
This model was created by merging the following models using **mergekit** with the **slerp** method configuration:
* Models Merged:
- prithivMLmods/Phi-4-Empathetic
- prithivMLmods/Phi-4-Math-IO
## Merge Details
* **Merge Method Used by mergekit:** `slerp`
* **Config Used:**
```yaml
base_model: unsloth/phi-4
slices:
- sources:
- model: prithivMLmods/Phi-4-Empathetic
layer_range:
- '0'
- '32'
- sources:
- model: prithivMLmods/Phi-4-Math-IO
layer_range:
- '0'
- '32'
merge_method: slerp
dtype: bfloat16
parameters:
t:
- 0.5
```
## How to Use
**Note:** Loading requires significant RAM/VRAM. Use `device_map="auto"` or quantization.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "misterpawan/phi-4-merged-all-slerp" # Use the final repo ID
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load the merged model - Use device_map for large models!
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16, # Match merge dtype
device_map="auto", # Essential for distributing large models
trust_remote_code=True
)
# Example prompt (adjust based on model's fine-tuning)
messages = [
{"role": "user", "content": "What is the capital of France?"},
]
if tokenizer.pad_token is None:
print("WARNING: Tokenizer does not have a pad token, setting it to eos_token.")
tokenizer.pad_token = tokenizer.eos_token
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids, max_new_tokens=100, temperature=0.7, do_sample=True, pad_token_id=tokenizer.pad_token_id)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Evaluation
Use `lm-evaluation-harness`. Remember to use `device_map='auto'` or quantization.
```bash
# Example evaluation command
lm_eval --model hf \
--model_args pretrained=misterpawan/phi-4-merged-all-slerp,trust_remote_code=true,dtype=bfloat16,device_map='auto' \
--tasks arc_challenge,hellaswag,mmlu,truthfulqa_mc2,winogrande,gsm8k \
--batch_size auto \
--output_path ./eval_results/phi-4-merged-all-slerp
```
*Please add evaluation results here once available.* |
mradermacher/Evac-Opus-14B-Exp-GGUF | mradermacher | 2025-05-04T15:28:57Z | 640 | 1 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"abliterated",
"trl",
"Evac",
"SFT",
"en",
"zh",
"base_model:prithivMLmods/Evac-Opus-14B-Exp",
"base_model:quantized:prithivMLmods/Evac-Opus-14B-Exp",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-16T16:03:48Z | ---
base_model: prithivMLmods/Evac-Opus-14B-Exp
language:
- en
- zh
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- abliterated
- trl
- Evac
- SFT
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/prithivMLmods/Evac-Opus-14B-Exp
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Evac-Opus-14B-Exp-GGUF/resolve/main/Evac-Opus-14B-Exp.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
idir6380/sd-tryon-model-lora-sd | idir6380 | 2025-05-04T15:28:06Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-05-04T14:55:31Z | ---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - idir6380/sd-tryon-model-lora-sd
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the None dataset. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
annemiekebickleyoy/14dbcf41-ba31-4c94-800e-cec7fec9a892 | annemiekebickleyoy | 2025-05-04T15:27:16Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:adapter:NousResearch/Meta-Llama-3-8B",
"region:us"
] | null | 2025-05-04T15:26:52Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-8B
model-index:
- name: annemiekebickleyoy/14dbcf41-ba31-4c94-800e-cec7fec9a892
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. -->
# annemiekebickleyoy/14dbcf41-ba31-4c94-800e-cec7fec9a892
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0648
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
KKPrasanna/t5-recipe-nlg | KKPrasanna | 2025-05-04T15:26:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-04T15:26:08Z | ---
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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### 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
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[More Information Needed]
## Training Details
### Training Data
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[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
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[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. -->
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