modelId string | author string | last_modified timestamp[us, tz=UTC] | downloads int64 | likes int64 | library_name string | tags list | pipeline_tag string | createdAt timestamp[us, tz=UTC] | card string |
|---|---|---|---|---|---|---|---|---|---|
AWuhrmann/qwen3-100-5-epochs-checkpoint-200 | AWuhrmann | 2025-06-11T15:32:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T15:32:05Z | ---
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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### 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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[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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## Model Card Authors [optional]
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## Model Card Contact
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RichardErkhov/ADT7_-_SN11-T-100-Q-gguf | RichardErkhov | 2025-06-11T15:31:26Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-11T13:46:15Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SN11-T-100-Q - GGUF
- Model creator: https://huggingface.co/ADT7/
- Original model: https://huggingface.co/ADT7/SN11-T-100-Q/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [SN11-T-100-Q.Q2_K.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q2_K.gguf) | Q2_K | 2.96GB |
| [SN11-T-100-Q.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [SN11-T-100-Q.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [SN11-T-100-Q.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [SN11-T-100-Q.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [SN11-T-100-Q.Q3_K.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q3_K.gguf) | Q3_K | 3.74GB |
| [SN11-T-100-Q.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [SN11-T-100-Q.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [SN11-T-100-Q.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [SN11-T-100-Q.Q4_0.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q4_0.gguf) | Q4_0 | 4.34GB |
| [SN11-T-100-Q.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [SN11-T-100-Q.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [SN11-T-100-Q.Q4_K.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q4_K.gguf) | Q4_K | 4.58GB |
| [SN11-T-100-Q.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [SN11-T-100-Q.Q4_1.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q4_1.gguf) | Q4_1 | 4.78GB |
| [SN11-T-100-Q.Q5_0.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q5_0.gguf) | Q5_0 | 5.21GB |
| [SN11-T-100-Q.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [SN11-T-100-Q.Q5_K.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q5_K.gguf) | Q5_K | 5.34GB |
| [SN11-T-100-Q.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [SN11-T-100-Q.Q5_1.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q5_1.gguf) | Q5_1 | 5.65GB |
| [SN11-T-100-Q.Q6_K.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.Q6_K.gguf) | Q6_K | 6.14GB |
| [SN11-T-100-Q.Q8_0.gguf](https://huggingface.co/RichardErkhov/ADT7_-_SN11-T-100-Q-gguf/blob/main/SN11-T-100-Q.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]
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## Uses
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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
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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#### 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]
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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. -->
**BibTeX:**
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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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|
zhangyi617/sd_naruto_pgd_text | zhangyi617 | 2025-06-11T15:26:18Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-06-11T15:02:18Z | ---
base_model: runwayml/stable-diffusion-v1-5
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 - zhangyi617/sd_naruto_pgd_text
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/naruto-blip-captions 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] |
Bogoo/SmolLM2_NERO_try2 | Bogoo | 2025-06-11T15:25:11Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:HuggingFaceTB/SmolLM2-1.7B",
"base_model:finetune:HuggingFaceTB/SmolLM2-1.7B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T15:25:06Z | ---
base_model: HuggingFaceTB/SmolLM2-1.7B
library_name: transformers
model_name: SmolLM2_NERO_try2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SmolLM2_NERO_try2
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B).
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="Bogoo/SmolLM2_NERO_try2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.1
- Pytorch: 2.5.0a0+e000cf0ad9.nv24.10
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
NehaHarish28/llm_mapper_epoch3_new | NehaHarish28 | 2025-06-11T15:16:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T15:16:34Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** NehaHarish28
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mjaydenkim/results | mjaydenkim | 2025-06-11T15:16:34Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:finetune:NousResearch/Llama-2-7b-chat-hf",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T15:16:25Z | ---
base_model: NousResearch/Llama-2-7b-chat-hf
library_name: transformers
model_name: results
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for results
This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf).
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="mjaydenkim/results", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lahs/huggingface/runs/y5l6hyr3)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.0
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
JayHyeon/Qwen_1.5B-math-VDPO_5e-7_1.0vpo_constant-1ep | JayHyeon | 2025-06-11T15:03:09Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:argilla/distilabel-math-preference-dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"auto... | text-generation | 2025-06-11T14:54:03Z | ---
base_model: Qwen/Qwen2.5-Math-1.5B
datasets: argilla/distilabel-math-preference-dpo
library_name: transformers
model_name: Qwen_1.5B-math-VDPO_5e-7_1.0vpo_constant-1ep
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen_1.5B-math-VDPO_5e-7_1.0vpo_constant-1ep
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) 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="JayHyeon/Qwen_1.5B-math-VDPO_5e-7_1.0vpo_constant-1ep", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/nme9dm1i)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
zhangyi617/sd_naruto_corruption_text | zhangyi617 | 2025-06-11T14:57:08Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-06-11T14:45:55Z | ---
base_model: runwayml/stable-diffusion-v1-5
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 - zhangyi617/sd_naruto_corruption_text
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/naruto-blip-captions 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] |
Huang-Yiji/DeepSeek-R1-Medical-Doctor | Huang-Yiji | 2025-06-11T14:55:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T14:53:36Z | ---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Huang-Yiji
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-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)
|
MinaMila/llama_instbase_3b_ug2_1e-6_1.0_0.5_0.75_0.05_LoRa_ACSEmployment_2_cfda_ep7_22 | MinaMila | 2025-06-11T14:49:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T14:49:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
garetht/byte-tokenizer | garetht | 2025-06-11T14:40:58Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-11T14:40:54Z | # ByteTokenizer
A pure byte-level tokenizer that maps each byte (0-255) to a token. Perfect for identity transformers with vocab_size = 256.
## Description
ByteTokenizer is a specialized tokenizer that creates a perfect one-to-one mapping between bytes and tokens.
This makes it ideal for identity transformers, allowing for pure mathematical identity mappings.
## Features
- Vocabulary size of exactly 256 (one token per possible byte value)
- No special tokens to maintain pure byte-level semantics
- Perfect roundtrip encoding/decoding
- Compatible with Hugging Face Transformers
## Usage
```python
from transformers import AutoTokenizer
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("garetht/byte-tokenizer")
# Encode text
text = "Hello, world!"
encoded = tokenizer(text, return_tensors="pt")
# Use with models
# model = AutoModelForMaskedLM.from_pretrained("garetht/byte-tokenizer")
# outputs = model(**encoded)
```
## License
This project is open source and available under the MIT License.
|
sergioalves/7f64c837-c4b0-49f0-85f2-e97f5eaab31e | sergioalves | 2025-06-11T14:39:04Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Genstruct-7B",
"base_model:quantized:NousResearch/Genstruct-7B",
"autotrain_compatible"... | text-generation | 2025-06-11T13:26:28Z | ---
base_model: NousResearch/Genstruct-7B
library_name: transformers
model_name: 7f64c837-c4b0-49f0-85f2-e97f5eaab31e
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 7f64c837-c4b0-49f0-85f2-e97f5eaab31e
This model is a fine-tuned version of [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B).
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="sergioalves/7f64c837-c4b0-49f0-85f2-e97f5eaab31e", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/7r9xsios)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
JeloH/qwen-textgen-modelV_PTexebench | JeloH | 2025-06-11T14:33:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T14:11:09Z | ---
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
<!-- 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] |
AlanHudson/lora-trained-xl | AlanHudson | 2025-06-11T14:26:31Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2025-06-11T13:44:02Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a photo of sks dog
widget:
- text: A photo of sks dog in a bucket
output:
url: image_0.png
- text: A photo of sks dog in a bucket
output:
url: image_1.png
- text: A photo of sks dog in a bucket
output:
url: image_2.png
- text: A photo of sks dog in a bucket
output:
url: image_3.png
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- 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. -->
# SDXL LoRA DreamBooth - AlanHudson/lora-trained-xl
<Gallery />
## Model description
These are AlanHudson/lora-trained-xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: stabilityai/sdxl-vae.
## Trigger words
You should use a photo of sks dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](AlanHudson/lora-trained-xl/tree/main) them in the Files & versions tab.
## 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] |
clowman/Magistral-Small-2506-FP8 | clowman | 2025-06-11T14:25:47Z | 0 | 0 | vllm | [
"vllm",
"safetensors",
"mistral",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"base_model:quantized:... | null | 2025-06-11T14:24:14Z | ---
base_model:
- mistralai/Mistral-Small-3.1-24B-Instruct-2503
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
inference: false
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
library_name: vllm
license: apache-2.0
---
# Quantization
Created with [openquant](https://github.com/LambdaLabsML/openquant) on `Python 3.10.12 (main, Feb 4 2025, 14:57:36) [GCC 11.4.0]` with 1 CPUs
Base Model: [mistralai/Magistral-Small-2506](https://huggingface.co/mistralai/Magistral-Small-2506)
## Steps to reproduce:
1. `git clone https://github.com/LambdaLabsML/openquant`
2. `python compress_fp8.py --model mistralai/Magistral-Small-2506`
# Model Card for Magistral-Small-2506
Building upon Mistral Small 3.1 (2503), **with added reasoning capabilities**, undergoing SFT from Magistral Medium traces and RL on top, it's a small, efficient reasoning model with 24B parameters.
Magistral Small can be deployed locally, fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.
Learn more about Magistral in our [blog post](https://mistral.ai/news/magistral/).
## Key Features
- **Reasoning:** Capable of long chains of reasoning traces before providing an answer.
- **Multilingual:** Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, and Farsi.
- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window:** A 128k context window, **but** performance might degrade past **40k**. Hence we recommend setting the maximum model length to 40k.
## Benchmark Results
| Model | AIME24 pass@1 | AIME25 pass@1 | GPQA Diamond | Livecodebench (v5) |
|-------|-------------|-------------|--------------|-------------------|
| Magistral Medium | 73.59% | 64.95% | 70.83% | 59.36% |
| Magistral Small | 70.68% | 62.76% | 68.18% | 55.84% |
## Sampling parameters
Please make sure to use:
- `top_p`: 0.95
- `temperature`: 0.7
- `max_tokens`: 40960
## Basic Chat Template
We highly recommend including the default system prompt used during RL for the best results, you can edit and customise it if needed for your specific use case.
```
<s>[SYSTEM_PROMPT]system_prompt
A user will ask you to solve a task. You should first draft your thinking process (inner monologue) until you have derived the final answer. Afterwards, write a self-contained summary of your thoughts (i.e. your summary should be succinct but contain all the critical steps you needed to reach the conclusion). You should use Markdown to format your response. Write both your thoughts and summary in the same language as the task posed by the user. NEVER use \boxed{} in your response.
Your thinking process must follow the template below:
<think>
Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate a correct answer.
</think>
Here, provide a concise summary that reflects your reasoning and presents a clear final answer to the user. Don't mention that this is a summary.
Problem:
[/SYSTEM_PROMPT][INST]user_message[/INST]<think>
reasoning_traces
</think>
assistant_response</s>[INST]user_message[/INST]
```
*`system_prompt`, `user_message` and `assistant_response` are placeholders.*
We invite you to choose, depending on your use case and requirements, between keeping reasoning traces during multi-turn interactions or keeping only the final assistant response.
***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
## Usage
The model can be used with the following frameworks;
### Inference
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [below](#vllm)
In addition the community has prepared quantized versions of the model that can be used with the following frameworks (*alphabetically sorted*):
- [`llama.cpp`](https://github.com/ggml-org/llama.cpp): https://huggingface.co/mistralai/Magistral-Small-2506_gguf
- [`lmstudio` (llama.cpp, MLX)](https://lmstudio.ai/): https://lmstudio.ai/models/mistralai/magistral-small
- [`ollama`](https://ollama.com/): https://ollama.com/library/magistral
- [`unsloth` (llama.cpp)](https://huggingface.co/unsloth): https://huggingface.co/unsloth/Magistral-Small-2506-GGUF
### Training
Fine-tuning is possible with (*alphabetically sorted*):
- [`axolotl`](https://github.com/axolotl-ai-cloud/axolotl): https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral
- [`unsloth`](https://github.com/unslothai/unsloth): https://docs.unsloth.ai/basics/magistral
### Other
Also you can use Magistral with:
- [`kaggle`](https://www.kaggle.com/models/mistral-ai/magistral-small-2506): https://www.kaggle.com/models/mistral-ai/magistral-small-2506
### vLLM (recommended)
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**_Installation_**
Make sure you install the latest [`vLLM`](https://github.com/vllm-project/vllm/) code:
```
pip install -U vllm \
--pre \
--extra-index-url https://wheels.vllm.ai/nightly
```
Doing so should automatically install [`mistral_common >= 1.6.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.6.0).
To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
Serve model as follows:
```
vllm serve mistralai/Magistral-Small-2506 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
```
Ping model as follows:
```py
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 40_960
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
query = "Write 4 sentences, each with at least 8 words. Now make absolutely sure that every sentence has exactly one word less than the previous sentence."
# or try out other queries
# query = "Exactly how many days ago did the French Revolution start? Today is June 4th, 2025."
# query = "Think about 5 random numbers. Verify if you can combine them with addition, multiplication, subtraction or division to 133"
# query = "If it takes 30 minutes to dry 12 T-shirts in the sun, how long does it take to dry 33 T-shirts?"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": query}
]
stream = client.chat.completions.create(
model=model,
messages=messages,
stream=True,
temperature=TEMP,
top_p=TOP_P,
max_tokens=MAX_TOK,
)
print("client: Start streaming chat completions...")
printed_content = False
for chunk in stream:
content = None
# Check the content is content
if hasattr(chunk.choices[0].delta, "content"):
content = chunk.choices[0].delta.content
if content is not None:
if not printed_content:
printed_content = True
print("\ncontent:", end="", flush=True)
# Extract and print the content
print(content, end="", flush=True)
# content:<think>
# Alright, I need to write 4 sentences where each one has at least 8 words and each subsequent sentence has one fewer word than the previous one.
# ...
# Final boxed answer (the four sentences):
# \[
# \boxed{
# \begin{aligned}
# &\text{1. The quick brown fox jumps over lazy dog and yells hello.} \\
# &\text{2. I saw the cat on the stair with my hat.} \\
# &\text{3. The man in the moon came down quickly today.} \\
# &\text{4. A cat sat on the mat today patiently.}
# \end{aligned}
# }
# \]
``` |
Muhammed164/Smolvlm-finetuned | Muhammed164 | 2025-06-11T14:20:00Z | 0 | 0 | null | [
"safetensors",
"idefics3",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T09:39:37Z | ---
license: apache-2.0
---
|
DevQuasar/shisa-ai.shisa-v2-llama3.1-8b-GGUF | DevQuasar | 2025-06-11T14:17:40Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:shisa-ai/shisa-v2-llama3.1-8b",
"base_model:quantized:shisa-ai/shisa-v2-llama3.1-8b",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-06-11T13:04:41Z | ---
base_model:
- shisa-ai/shisa-v2-llama3.1-8b
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [shisa-ai/shisa-v2-llama3.1-8b](https://huggingface.co/shisa-ai/shisa-v2-llama3.1-8b)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
19uez/llama3_2_3B_16_0_5k_GRPO_LoRA | 19uez | 2025-06-11T14:10:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:19uez/GRPO_llama3_2_3B_16_0_2k",
"base_model:adapter:19uez/GRPO_llama3_2_3B_16_0_2k",
"region:us"
] | null | 2025-06-11T14:08:51Z | ---
base_model: 19uez/GRPO_llama3_2_3B_16_0_2k
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.15.1
|
19uez/llama3_2_3B_32_0_5k_GRPO_LoRA | 19uez | 2025-06-11T14:06:55Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:19uez/llama_3b_sft_best",
"base_model:adapter:19uez/llama_3b_sft_best",
"region:us"
] | null | 2025-06-11T14:04:14Z | ---
base_model: 19uez/llama_3b_sft_best
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.15.1
|
AWuhrmann/qwen3-75-5-epochs-checkpoint-2100 | AWuhrmann | 2025-06-11T14:04:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T14:03:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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[More Information Needed] |
haielab/Qwen2.5-Math-7B-counterexamples-base | haielab | 2025-06-11T14:01:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T14:01:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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FBK-MT/fama-small | FBK-MT | 2025-06-11T14:01:34Z | 149 | 5 | transformers | [
"transformers",
"safetensors",
"conformer_encoder_decoder",
"automatic-speech-recognition",
"speech",
"speech recognition",
"speech translation",
"ASR",
"ST",
"custom_code",
"en",
"it",
"dataset:FBK-MT/mosel",
"dataset:facebook/covost2",
"dataset:openslr/librispeech_asr",
"dataset:face... | automatic-speech-recognition | 2025-03-31T17:42:01Z | ---
license: cc-by-4.0
language:
- en
- it
datasets:
- FBK-MT/mosel
- facebook/covost2
- openslr/librispeech_asr
- facebook/voxpopuli
metrics:
- comet
- wer
tags:
- speech
- speech recognition
- speech translation
- ASR
- ST
pipeline_tag: automatic-speech-recognition
library_name: transformers
---
# FAMA-small
<div>
<img src="FAMA.png" width="100%" alt="FAMA" />
</div>
## Table of Contents
1. [Overview](#overview)
2. [Usage](#Usage)
3. [Results](#Results)
4. [License](#license)
5. [Citation](#citation)
## Overview
FAMA is the first family of large-scale open-science SFMs for English and
Italian trained on [over 150k hours of exclusively open-source(OS)-compliant speech data](https://huggingface.co/datasets/FBK-MT/fama-data).
FAMA models achieve [remarkable results](#results), with ASR and ST improvements on average across languages compared to OWSM,
and is competitive in terms of ASR performance with the Whisper model family while being up to 8 times faster.
All the artifacts used for realizing FAMA models, including codebase, datasets, and models
themself are [released under OS-compliant licenses](#license), promoting a more
responsible creation of models in our community.
It is available in 2 sizes, with 2 variants for ASR only:
- [FAMA-small](https://huggingface.co/FBK-MT/fama-small) - 475 million parameters
- [FAMA-medium](https://huggingface.co/FBK-MT/fama-medium) - 878 million parameters
- [FAMA-small-asr](https://huggingface.co/FBK-MT/fama-small-asr) - 475 million parameters
- [FAMA-medium-asr](https://huggingface.co/FBK-MT/fama-medium-asr) - 878 million parameters
For more information about FAMA, please check our [blog post](https://huggingface.co/blog/FAMA/release) and the [arXiv](https://arxiv.org/abs/2505.22759) preprint.
## Usage
FAMA models are supported in Hugging Face 🤗 Transformers.
To run the model, first install the Transformers and Datasets libraries.
```sh
pip install transformers==4.48.1 datasets
```
To perform a single inference on a sample audio file using the
[`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
class, run:
```python
import torch
from transformers import AutoProcessor, pipeline
from datasets import load_dataset
model_id = "FBK-MT/fama-small"
processor = AutoProcessor.from_pretrained(model_id)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
tgt_lang = "en"
# Force the model to start with the language tag
lang_tag = "<lang:{}>".format(tgt_lang)
lang_tag_id = processor.tokenizer.convert_tokens_to_ids(lang_tag)
generate_kwargs = {"num_beams": 5, "no_repeat_ngram_size": 5, "forced_bos_token_id": lang_tag_id}
pipe = pipeline(
"automatic-speech-recognition",
model=model_id,
trust_remote_code=True,
torch_dtype=torch.float32,
device=device,
return_timestamps=False,
generate_kwargs=generate_kwargs
)
dataset = load_dataset("distil-whisper/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
Where `tgt_lang` is the target language (either `en` or `it`). The source languages has not to be specified.
To run the inference on a local audio file `audio.wav`, call the pipeline with:
```python
result = pipe("audio.wav")
```
To perform a batch inference with size `batch_size`, run:
```python
result = pipe(["audio_1.wav", "audio_2.wav"], batch_size=2)
```
For the inference, we suggest converting the audio files in wav format with 16kHz sampling rate and 1 channel.
## Results
We evaluate FAMA on ASR and ST tasks using popular open-source datasets such as CommonVoice, Multilingual LibriSpeech (MLS), VoxPopuli, CoVoST2 and FLEURS.
The metrics used are WER (↓) for ASR, and COMET (↑) for ST.
We also benchmark FAMA in terms of computational time and maximum batch size supported on HuggingFace against Whisper and SeamlessM4T models. The metric used is the inverse real time factor (xRTF).
**Key highlights:**
- FAMA achieves up to 4.2 WER and 0.152 COMET improvement on average across languages compared to OWSM v3.1
- FAMA is up to 8 times faster than Whisper large-v3 while achieving comparable ASR performance
### Automatic Speech Recogniton (ASR)
| ***Model/Dataset WER (↓)*** | **CommonVoice**-*en* | **CommonVoice**-*it* | **MLS**-*en* | **MLS**-*it* | **VoxPopuli**-*en* | **VoxPopuli**-*it* | **AVG**-*en* | **AVG**-*it* |
|-----------------------------------------|---------|---------|---------|---------|---------|----------|---------|----------|
| Whisper *medium* | 14.5 | 10.4 | 14.2 | 15.9 | 8.1 | 26.8 | 12.3 | 17.7 |
| Whisper *large-v3* | 11.2 | 6.5 | **5.0** | 8.8 | 7.1 | 18.8 | 7.8 | 11.4 |
| OWSM v3.1 *medium* | 11.9 | 12.5 | 6.6 | 19.3 | 8.4 | 24.0 | 9.0 | 18.6 |
| SeamlessM4T *medium* | 10.7 | 7.8 | 8.8 | 11.3 | 10.2 | 18.2 | 9.9 | 12.4 |
| SeamlessM4T *v2-large* | **7.7** | **5.0** | 6.4 | **8.5** | **6.9** | 16.6 | **7.0** | **10.0** |
| FAMA-ASR *small* | 13.8 | 8.9 | 5.8 | 12.6 | 7.2 | 15.7 | 8.9 | 12.4 |
| FAMA-ASR *medium* | 11.7 | 7.1 | 5.1 | 12.2 | 7.0 | 15.9 | 7.9 | 11.7 |
| FAMA *small* | 13.7 | 8.6 | 5.8 | 12.8 | 7.3 | **15.6** | 8.9 | 12.3 |
| FAMA *medium* | 11.5 | 7.0 | 5.2 | 13.9 | 7.2 | 15.9 | 8.0 | 12.3 |
### Speech Translation (ST)
| ***Model/Dataset WER (↓)*** | **CoVoST2**-*it→en* | **FLEURS**-*en→it* |
|-----------------------------------------|---------------------|--------------------|
| Whisper *medium* | 0.801 | - |
| Whisper *large-v3* | 0.825 | - |
| OWSM v3.1 *medium* | 0.636 | 0.337 |
| SeamlessM4T *medium* | 0.831 | 0.820 |
| SeamlessM4T *v2-large* | **0.852** | **0.855** |
| FAMA *small* | 0.774 | 0.807 |
| FAMA *medium* | 0.787 | 0.821 |
### Computational Time and Maximum Batch Size
| ***Model*** | ***Batch Size*** | ***xRTF en (↑)*** | ***xRTF it (↑)*** | ***xRTF AVG (↑)*** |
|------------------------|------------|-------------|-------------|--------------|
| Whisper *medium* | 8 | 13.3 | 10.9 | 12.1 |
| Whisper *large-v3* | 4 | 7.9 | 6.5 | 7.2 |
| SeamlessM4T *medium* | 2 | 28.5 | 26.2 | 27.4 |
| SeamlessM4T *v2-large* | 2 | 13.7 | 13.3 | 13.5 |
| FAMA *small* | 16 | **57.4** | **56.0** | **56.7** |
| FAMA *medium* | 8 | 39.5 | 41.2 | 40.4 |
## License
We release the FAMA model weights, and training data under the CC-BY 4.0 license.
The training data can be found in [FAMA Training Data](https://huggingface.co/datasets/FBK-MT/fama-data).
The [original FBK-fairseq codebase](https://github.com/hlt-mt/FBK-fairseq) used to train the model is released under the Apache 2.0 license.
## Citation
If you use FAMA in your work, please cite:
```
@misc{papi2025fama,
title={FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian},
author={Sara Papi and Marco Gaido and Luisa Bentivogli and Alessio Brutti and Mauro Cettolo and Roberto Gretter and Marco Matassoni and Mohamed Nabih and Matteo Negri},
year={2025}
}
``` |
morturr/Mistral-7B-v0.1-PAIR_headlines_one_liners-COMB-headlines-comb-2-seed-7-2025-06-11 | morturr | 2025-06-11T14:01:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T14:01:09Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_headlines_one_liners-COMB-headlines-comb-2-seed-7-2025-06-11
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_headlines_one_liners-COMB-headlines-comb-2-seed-7-2025-06-11
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
Moamen-dcp/Full-whisper-Franko-CS-ALL-tt | Moamen-dcp | 2025-06-11T13:58:14Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T13:58:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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AWuhrmann/qwen3-75-5-epochs-checkpoint-800 | AWuhrmann | 2025-06-11T13:58:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T13:58:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **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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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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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
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[More Information Needed]
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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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AWuhrmann/qwen3-75-5-epochs-checkpoint-600 | AWuhrmann | 2025-06-11T13:57:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T13:57:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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### Direct Use
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### 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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
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### Results
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#### 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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## Technical Specifications [optional]
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fouad-mulla/pentest-mistral-lora-merged | fouad-mulla | 2025-06-11T13:55:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"generated_from_trainer",
"dataset:fouad-mulla/Pentester_fine_tuner",
"base_model:fouad-mulla/Pentester",
"base_model:adapter:fouad-mulla/Pentester",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-11T13:53:06Z | ---
library_name: peft
license: apache-2.0
base_model: fouad-mulla/Pentester
tags:
- generated_from_trainer
datasets:
- fouad-mulla/Pentester_fine_tuner
model-index:
- name: outputs/mymodel
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: `0.8.0.dev0`
```yaml
adapter: lora
base_model: fouad-mulla/Pentester
bf16: auto
dataset_processes: 32
datasets:
- path: fouad-mulla/Pentester_fine_tuner
type: alpaca
message_property_mappings:
content: content
role: role
trust_remote_code: false
gradient_accumulation_steps: 1
gradient_checkpointing: false
learning_rate: 0.0002
lisa_layers_attribute: model.layers
load_best_model_at_end: false
load_in_4bit: false
load_in_8bit: true
lora_alpha: 16
lora_dropout: 0.05
lora_r: 8
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
loraplus_lr_embedding: 1.0e-06
lr_scheduler: cosine
max_prompt_len: 512
mean_resizing_embeddings: false
micro_batch_size: 16
num_epochs: 1.0
optimizer: adamw_bnb_8bit
output_dir: ./outputs/mymodel
pretrain_multipack_attn: true
pretrain_multipack_buffer_size: 10000
qlora_sharded_model_loading: false
ray_num_workers: 1
resources_per_worker:
GPU: 1
sample_packing_bin_size: 200
sample_packing_group_size: 100000
save_only_model: false
save_safetensors: true
sequence_len: 4096
shuffle_merged_datasets: true
skip_prepare_dataset: false
strict: false
train_on_inputs: false
trl:
log_completions: false
ref_model_mixup_alpha: 0.9
ref_model_sync_steps: 64
sync_ref_model: false
use_vllm: false
vllm_device: auto
vllm_dtype: auto
vllm_gpu_memory_utilization: 0.9
use_ray: false
val_set_size: 0.0
weight_decay: 0.0
```
</details><br>
# outputs/mymodel
This model is a fine-tuned version of [fouad-mulla/Pentester](https://huggingface.co/fouad-mulla/Pentester) on the fouad-mulla/Pentester_fine_tuner dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1.0
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |
Enpas/Core1k-it | Enpas | 2025-06-11T13:50:29Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"am",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoi... | automatic-speech-recognition | 2025-06-11T08:50:42Z | ---
library_name: transformers
language:
- am
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Core1k
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
metrics:
- name: Wer
type: wer
value: 49.4139849026619
---
<!-- 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. -->
# Core1k
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0526
- Wer: 49.4140
## 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: 12
- eval_batch_size: 16
- 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: 200
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.4414 | 2.5316 | 1000 | 0.9952 | 50.1887 |
| 1.1362 | 5.0633 | 2000 | 1.0241 | 50.6953 |
| 0.8391 | 7.5949 | 3000 | 1.0526 | 49.4140 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
AWuhrmann/qwen3-25-5-epochs-checkpoint-1655 | AWuhrmann | 2025-06-11T13:49:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T13:49:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### 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]
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<!-- 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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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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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- **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]
#### Hardware
[More Information Needed]
#### Software
[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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[More Information Needed] |
srushtisingh/MNLP_final_dpo_model-sDPO-v1 | srushtisingh | 2025-06-11T13:48:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T13:47:58Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
Mackelen/jibes | Mackelen | 2025-06-11T13:48:33Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T13:41:45Z | ---
license: apache-2.0
---
|
AWuhrmann/qwen3-25-5-epochs-checkpoint-700 | AWuhrmann | 2025-06-11T13:46:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T13:46:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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AWuhrmann/qwen3-25-5-epochs-checkpoint-600 | AWuhrmann | 2025-06-11T13:46:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T13:45:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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<!-- 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]
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#### 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]
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[More Information Needed]
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[More Information Needed]
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pkwr173-vk/Video.pkwr173.vk.pkwr173.ishi.gonzales.vk.mygif777.bigcatmia.vk | pkwr173-vk | 2025-06-11T13:43:59Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-11T13:43:14Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
gradientrouting-spar/nvdg_badmed_naive_data_seed-42_model_seed-42_seed-42_up_prx-0.2_seed_5_epoch_1 | gradientrouting-spar | 2025-06-11T13:43:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T13:42:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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opendatalab/meta-rater-1b-random | opendatalab | 2025-06-11T13:36:46Z | 20 | 0 | transformers | [
"transformers",
"safetensors",
"internlm2",
"text-generation",
"custom_code",
"en",
"dataset:opendatalab/SlimPajama-Meta-rater",
"arxiv:2504.14194",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:mit",
"autotrain_compatible",
"region:us"... | text-generation | 2025-06-06T06:13:17Z | ---
base_model:
- answerdotai/ModernBERT-base
datasets:
- opendatalab/SlimPajama-Meta-rater
language:
- en
license: mit
metrics:
- accuracy
pipeline_tag: text-generation
library_name: transformers
---
# Random Baseline Language Model (1.3B Parameters, 30B Tokens)
This repository contains the model described in the paper [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://huggingface.co/papers/2504.14194).
Code: https://github.com/opendatalab/Meta-rater
## Model Description
This is a 1.3B parameter transformer-based decoder-only language model trained from scratch on 30B tokens randomly sampled from SlimPajama dataset. It serves as a baseline for comparing data selection methods in the Meta-rater research.
## Model Details
- **Architecture**: Transformer decoder-only
- **Parameters**: 1.345B (1,345,423,360 parameters)
- **Training Tokens**: 30B tokens
- **Context Window**: 1,024 tokens
- **Vocabulary Size**: 32,000 (LLaMA tokenizer)
- **Training Data**: Randomly sampled from SlimPajama dataset
- **Domain Distribution**: Fixed proportion across all domains (CommonCrawl: 52.2%, C4: 26.7%, GitHub: 5.2%, Books: 4.2%, ArXiv: 4.6%, Wikipedia: 3.8%, StackExchange: 3.3%)
## Architecture Specifications
- **Hidden Dimension**: 2,048
- **Number of Layers**: 24
- **Attention Heads**: 16
- **Key-Value Heads**: 16
- **MLP Ratio**: 8/3
- **Position Encoding**: RoPE (base=10,000)
## Training Details
- **Hardware**: 32x NVIDIA A800 GPUs
- **Global Batch Size**: 4,194,304 tokens
- **Learning Rate**: 5e-5
- **Optimizer**: Adam (β₁=0.9, β₂=0.95, ε=1e-8)
- **Training Time**: ~14 hours
## Performance Results
### Downstream Task Performance (Average Accuracy)
- **General Knowledge**: 52.79%
- ARC-Easy: 51.05%
- ARC-Challenge: 23.81%
- SciQ: 83.50%
- **Commonsense Reasoning**: 43.94%
- HellaSwag: 39.69%
- SIQA: 40.28%
- WinoGrande: 51.85%
- **Reading Comprehension**: 30.02%
- RACE: 30.43%
- OpenbookQA: 29.60%
- **Overall Average**: 43.78%
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and tokenizer
model_name = "opendatalab/meta-rater-1b-random"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Generate text
prompt = "The future of artificial intelligence is"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
inputs.input_ids,
max_length=100,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```
## Research Context
This model serves as a crucial baseline in the Meta-rater research, demonstrating the performance achievable with random data selection. Key findings:
- **Convergence Speed**: Models trained with Meta-rater data selection achieve equivalent performance using only 15B tokens compared to this 30B token baseline
- **Efficiency**: Meta-rater models outperform this baseline by 3.23% average accuracy when using the same 30B tokens
- **Token Efficiency**: This model requires 60B tokens to match the performance of Meta-rater models trained on 30B tokens
## Applications
This model can be used for:
- **Baseline comparisons** in data selection research
- **General language modeling** tasks
- **Research on training efficiency** and data quality
- **Educational purposes** for understanding transformer training
## Limitations
- Trained on randomly selected data without quality filtering
- Limited context window (1,024 tokens)
- No instruction tuning or safety alignment
- Performance lower than models trained with curated data selection
## Citation
If you use this model in your research, please cite:
```bibtex
@article{zhuang2025meta,
title={Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models},
author={Zhuang, Xinlin and Peng, Jiahui and Ma, Ren and Wang, Yinfan and Bai, Tianyi and Wei, Xingjian and Qiu, Jiantao and Zhang, Chi and Qian, Ying and He, Conghui},
journal={arXiv preprint arXiv:2504.14194},
year={2025}
}
```
## License
Please refer to the license terms of the original SlimPajama dataset and follow applicable data licensing requirements.
## Contact
For questions or issues, please contact the authors or open an issue in the repository. |
IoanaLiviaPopescu/synth-data-v1-whisper-small | IoanaLiviaPopescu | 2025-06-11T13:29:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ro",
"dataset:IoanaLiviaPopescu/SyntheticVoiceHoroscope-v1",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compa... | automatic-speech-recognition | 2025-06-11T13:06:36Z | ---
library_name: transformers
language:
- ro
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- IoanaLiviaPopescu/SyntheticVoiceHoroscope-v1
metrics:
- wer
model-index:
- name: IoanaLiviaPopescu/synth-data-v1-whisper-small
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: IoanaLiviaPopescu/SyntheticVoiceHoroscope-v1
type: IoanaLiviaPopescu/SyntheticVoiceHoroscope-v1
config: default
split: test
args: 'split: validation'
metrics:
- name: Wer
type: wer
value: 19.822976212428543
---
<!-- 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. -->
# IoanaLiviaPopescu/synth-data-v1-whisper-small
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLiviaPopescu/SyntheticVoiceHoroscope-v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4825
- Wer: 19.8230
## 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: 32
- eval_batch_size: 16
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 0 | 0 | 0.6024 | 27.8812 |
| 0.2529 | 1.0 | 13 | 0.5140 | 20.6343 |
| 0.1068 | 2.0 | 26 | 0.4849 | 20.0996 |
| 0.0609 | 3.0 | 39 | 0.4808 | 19.9521 |
| 0.0427 | 4.0 | 52 | 0.4825 | 19.8230 |
| 0.0349 | 5.0 | 65 | 0.4850 | 19.8783 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
NagaSaiAbhinay/CycleReward-T2I | NagaSaiAbhinay | 2025-06-11T13:29:31Z | 0 | 0 | imscore | [
"imscore",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-11T13:27:45Z | ---
library_name: imscore
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: https://github.com/RE-N-Y/imscore
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
TIGER-Lab/PixelReasoner-RL-v1 | TIGER-Lab | 2025-06-11T13:26:21Z | 1,505 | 5 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"en",
"dataset:TIGER-Lab/PixelReasoner-SFT-Data",
"arxiv:2505.15966",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inferen... | image-text-to-text | 2025-05-18T16:13:30Z | ---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
datasets:
- TIGER-Lab/PixelReasoner-SFT-Data
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
pipeline_tag: image-text-to-text
---
The model is trained with curiosity-driven RL described in [paper](https://arxiv.org/abs/2505.15966).
We have released vllm based inference code at https://github.com/TIGER-AI-Lab/Pixel-Reasoner/.
Project page: https://tiger-ai-lab.github.io/Pixel-Reasoner/
Github repository: https://github.com/TIGER-AI-Lab/Pixel-Reasoner/
We will release a simple hf.generate() based inference code.
Please also play with the cool [interactive demo](https://huggingface.co/spaces/TIGER-Lab/Pixel-Reasoner) |
penguin-wall/MissTreated-8B-GGUF | penguin-wall | 2025-06-11T13:06:49Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T13:05:33Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** penguin-wall
- **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)
|
Gefeizi/distilbert-base-uncased-finetuned-squad | Gefeizi | 2025-06-11T13:02:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-06-11T06:09:02Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1594
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5147 | 1.0 | 1384 | 1.2676 |
| 1.1355 | 2.0 | 2768 | 1.1650 |
| 0.9784 | 3.0 | 4152 | 1.1594 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
DevQuasar/iapp.Qwen2.5-VL-7B-Instruct-GGUF | DevQuasar | 2025-06-11T12:56:27Z | 0 | 0 | null | [
"gguf",
"image-text-to-text",
"base_model:iapp/Qwen2.5-VL-7B-Instruct",
"base_model:quantized:iapp/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | 2025-06-10T04:20:20Z | ---
base_model:
- iapp/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [iapp/Qwen2.5-VL-7B-Instruct](https://huggingface.co/iapp/Qwen2.5-VL-7B-Instruct)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> |
ertghiu256/qwen3-4b-code-reasoning-gguf | ertghiu256 | 2025-06-11T12:46:21Z | 517 | 1 | null | [
"gguf",
"qwen3",
"code",
"unsloth",
"coding",
"cot",
"reasoning",
"thinking",
"think",
"text-generation",
"dataset:nvidia/OpenCodeReasoning",
"dataset:vicgalle/creative-rubrics-gpt-4.5-o3-R1",
"base_model:Qwen/Qwen3-4B",
"base_model:quantized:Qwen/Qwen3-4B",
"license:apache-2.0",
"endp... | text-generation | 2025-05-05T14:38:51Z | ---
license: apache-2.0
datasets:
- nvidia/OpenCodeReasoning
- vicgalle/creative-rubrics-gpt-4.5-o3-R1
base_model:
- unsloth/Qwen3-4B-GGUF
- Qwen/Qwen3-4B
pipeline_tag: text-generation
tags:
- code
- unsloth
- coding
- cot
- reasoning
- thinking
- think
---
# Qwen 3 Code Reasoning
<small>Generated by reveai.org</small>
A small Qwen 3 4 billion parameter model trained on [nvidia/OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) for coding tasks.
For Coding, it is recommended to be in thinking mode.
## Recommended parameters:
- Context window : > 10000
- Temperature : 0.7
- Top_p : 60
- Top_k : 0.99
- Min_p : 0.05
## Strengths
- Code generation
- Logical question answering
## Drawbacks
- Overthinking
- Context Overflow
## Recommended Usage:
- [Ollama](https://ollama.com/)
- [Model at ollama](https://ollama.com/ertghiu256/qwen3-4b-code-reasoning)
- [LM Studio](https://lmstudio.ai/)
- [Llama.cpp](https://github.com/ggml-org/llama.cpp)
### SAFETENSOR VERSION
[safetensor](https://huggingface.co/ertghiu256/qwen3-4b-code-reasoning)
## Example Usage :
```
> Write me a snake game in python. Ensure all keybinding (WASD) work properly and all mechanics from the original snake game works. /think
<think>
Okay, I need to write a Snake game in Python where all the keybindings (WASD) work properly and all the original mechanics are included. Let me think about how to approach this.
First, I remember that the classic Snake game works by having a snake move in a direction, grow when it eats food, and die if it hits the wall or itself. The keybindings should allow the snake to change direction using W, A, S, D. But I need to make sure that the movement is smooth and that the snake can't reverse direction immediately, which might cause it to collide with itself right away.
So, the plan is to use the pygame library because it's commonly used for game development in Python. I'll need to initialize the game window, set up the display, handle the game loop, manage the snake's movement, check for collisions, and handle the food.
Let me outline the steps:
Import necessary modules: Pygame, sys, random for generating food, time for delay.
Initialize Pygame: Set up the display, clock, fonts, etc.
Define constants: Screen size, grid size (like 20x20), snake speed, colors.
Create the Snake class: Each segment of the snake. The snake moves in a direction, and each segment follows the previous one.
Create the Food class: Randomly placed on the screen, and when eaten, the snake grows.
Game loop:
Handle events (WASD for direction).
Move the snake.
Check for collisions (walls or self).
Check if food is eaten, then increase length.
Update the display.
Control the game speed with a clock.
Handle keybindings: Make sure that the snake can't reverse direction. So, if the current direction is right, the next can't be left, and so on.
Wait, but in the original Snake game, ...(Too long)
</think>
Here's a complete implementation of the **Snake Game** using the `pygame` library. The game allows the player to control the snake using the **WASD keys**, and the snake moves in the direction of the last key pressed. The game ends when the snake collides with the wall or itself, and the snake grows by one segment each time it eats the food.
---
### ✅ Features:
- **WASD Controls**:
- `W` → Up
- `A` → Left
- `S` → Down
- `D` → Right
- **Food Generation**:
- Randomly placed on a 32×24 grid (640×480 pixels).
- **Game Over**:
- Ends when the snake hits the wall or itself.
- **Speed Control**:
- Set with `clock.tick(10)` to control the game speed.
---
### 🧠 How It Works:
1. The snake starts with 3 segments.
2. The player uses `W`, `A`, `S`, `D` to change the direction.
3. The snake moves in the current direction, growing if it eats the food.
4. When the snake collides with the wall or itself, the game ends.
---
### 📦 Code Implementation
```python
import pygame
import sys
import random
# Initialize Pygame
pygame.init()
# Game settings
SCREEN_WIDTH = 640
SCREEN_HEIGHT = 480
BLOCK_SIZE = 20
GRID_WIDTH = SCREEN_WIDTH // BLOCK_SIZE
GRID_HEIGHT = SCREEN_HEIGHT // BLOCK_SIZE
# Colors
BLACK = (0, 0, 0)
GREEN = (0, 255, 0)
RED = (255, 0, 0)
# Initialize screen
screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
pygame.display.set_caption("Snake Game")
# Clock
clock = pygame.time.Clock()
# Snake
snake_segments = [(3, 3), (2, 3), (1, 3)]
direction = (1, 0) # Start moving right
# Food
def generate_food():
while True:
x = random.randint(0, GRID_WIDTH - 1)
y = random.randint(0, GRID_HEIGHT - 1)
if (x, y) not in snake_segments:
return (x, y)
food = generate_food()
# Game loop
running = True
ate_food = False
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_w:
direction = (0, -1)
elif event.key == pygame.K_a:
direction = (-1, 0)
elif event.key == pygame.K_s:
direction = (0, 1)
elif event.key == pygame.K_d:
direction = (1, 0)
# Move the snake
new_head = (snake_segments[0][0] + direction[0], snake_segments[0][1] + direction[1])
snake_segments.insert(0, new_head)
if not ate_food:
snake_segments.pop()
ate_food = False
# Check for collisions
if (new_head[0] < 0 or new_head[0] >= GRID_WIDTH or
new_head[1] < 0 or new_head[1] >= GRID_HEIGHT):
pygame.quit()
sys.exit()
if new_head in snake_segments[1:]:
pygame.quit()
sys.exit()
# Check if food is eaten
if new_head == food:
food = generate_food()
ate_food = True
# Draw everything
screen.fill(BLACK)
for segment in snake_segments:
pygame.draw.rect(screen, GREEN, (segment[0] * BLOCK_SIZE, segment[1] * BLOCK_SIZE, BLOCK_SIZE, BLOCK_SIZE))
pygame.draw.rect(screen, RED, (food[0] * BLOCK_SIZE, food[1] * BLOCK_SIZE, BLOCK_SIZE, BLOCK_SIZE))
pygame.display.flip()
# Control the game speed
clock.tick(10)
pygame.quit()
sys.exit()
```⠀
---
### 🧪 How to Run:
1. Install `pygame` using pip:
`pip install pygame`
2. Save the code in a file, e.g., `snake_game.py`.
3. Run the file using Python:
`python snake_game.py`
---
### 🚀 Tips:
- The game is fast-paced. You can slow it down by increasing the value in `clock.tick(10)` (e.g., `clock.tick(15)`).
- The snake starts in the middle of the screen, and the food is placed randomly.
Enjoy playing the **Snake Game**! 🐍🎮
``` |
Xara2west/pidgin-translator-final08 | Xara2west | 2025-06-11T12:30:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-11T12:29:54Z | ---
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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mispeech/dasheng-base | mispeech | 2025-06-11T12:22:57Z | 389 | 6 | transformers | [
"transformers",
"safetensors",
"dasheng",
"feature-extraction",
"audio",
"audio-classification",
"custom_code",
"license:apache-2.0",
"region:us"
] | audio-classification | 2024-06-06T01:05:05Z | ---
license: apache-2.0
tags:
- audio
- audio-classification
---
# Dasheng: a large scale general-purpose audio encoder
Dasheng (**D**eep **A**udio-**S**ignal **H**olistic **E**mbeddi**ng**s), or “大声” ("great sound"), is a general-purpose audio encoder trained on a large-scale self-supervised learning task. Dasheng is designed to capture rich audio information across various domains, including speech, music, and environmental sounds. The model is trained on 272,356 hours of diverse audio data with 1.2 billion parameters, and exhibits significant performance gains on the [HEAR benchmark](https://hearbenchmark.com/). Dasheng outperforms previous works on CREMA-D, LibriCount, Speech Commands, VoxLingua, and competes well in music and environmental sound classification tasks.
**Original Repository:** [https://github.com/RicherMans/Dasheng](https://github.com/RicherMans/Dasheng)
## Usage
### Inference
```python
>>> model_name = "mispeech/dasheng-base"
>>> from transformers import AutoModel, AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_name, trust_remote_code=True)
>>> model = AutoModel.from_pretrained(model_name, outputdim=None, trust_remote_code=True)
>>> import torch
>>> inputs = feature_extractor(torch.randn(1, 16000), sampling_rate=sampling_rate, return_tensors="pt")
>>> inputs.input_values.shape
torch.Size([1, 64, 101]) # 64 mel-filterbanks, 101 frames
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> outputs.hidden_states.shape
torch.Size([1, 25, 768]) # 25 T-F patches (patch size 64x4, no overlap), before mean-pooling
>>> outputs.logits.shape
torch.Size([1, 768]) # mean-pooled embedding (would be logits from a linear layer if `outputdim` was set)
```
### Fine-tuning
[](https://colab.research.google.com/github/jimbozhang/hf_transformers_custom_model_dasheng/blob/main/example_finetune_esc50.ipynb)
[`example_finetune_esc50.ipynb`](https://github.com/jimbozhang/hf_transformers_custom_model_dasheng/blob/main/example_finetune_esc50.ipynb) demonstrates how to train a linear head on the ESC-50 dataset with the Dasheng encoder frozen.
## Citation
If you find Dasheng useful in your research, please consider citing the following paper:
```bibtex
@inproceedings{dinkel2023scaling,
title={Scaling up masked audio encoder learning for general audio classification},
author={Dinkel, Heinrich and Yan, Zhiyong and Wang, Yongqing and Zhang, Junbo and Wang, Yujun and Wang, Bin},
booktitle={Interspeech 2024},
year={2024}
}
```
|
divyanshu29jha/fine-tuned-llama-3.2-3b_atm-dataset | divyanshu29jha | 2025-06-11T12:21:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T12:21:40Z | ---
base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** divyanshu29jha
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
mispeech/ced-small | mispeech | 2025-06-11T12:19:06Z | 614 | 0 | transformers | [
"transformers",
"safetensors",
"ced",
"audio-classification",
"custom_code",
"dataset:AudioSet",
"arxiv:2308.11957",
"license:apache-2.0",
"region:us"
] | audio-classification | 2023-11-24T18:41:07Z | ---
license: apache-2.0
datasets:
- AudioSet
metrics:
- mAP
pipeline_tag: audio-classification
---
# CED-Small Model
CED are simple ViT-Transformer-based models for audio tagging, achieving **sota** performance on Audioset.
| Model | Parameters (M) | AS-20K (mAP) | AS-2M (mAP) |
|------|-------|-------|-------|
| CED-Tiny | 5.5 | 36.5 | 48.1 |
| CED-Mini | 9.6 | 38.5 | 49.0 |
| CED-Small| 22 | 41.6 | 49.6 |
| CED-Base | 86 | 44.0 | 50.0 |
Notable differences from other available models include:
1. Simplification for finetuning: Batchnormalization of Mel-Spectrograms. During finetuning one does not need to first compute mean/variance over the dataset, which is common for AST.
1. Support for variable length inputs. Most other models use a static time-frequency position embedding, which hinders the model's generalization to segments shorter than 10s. Many previous transformers simply pad their input to 10s in order to avoid the performance impact, which in turn slows down training/inference drastically.
1. Training/Inference speedup: 64-dimensional mel-filterbanks and 16x16 patches without overlap, leading to 248 patches from a 10s spectrogram. In comparison, AST uses 128 mel-filterbanks with 16x16 (10x10 overlap) convolution, leading to 1212 patches during training/inference. CED-Tiny runs on a common CPU as fast as a comparable MobileNetV3.
1. Performance: CED with 10M parameters outperforms the majority of previous approaches (~80M).
### Model Sources
- **Repository:** https://github.com/RicherMans/CED
- **Paper:** [CED: Consistent ensemble distillation for audio tagging](https://arxiv.org/abs/2308.11957)
- **Demo:** https://huggingface.co/spaces/mispeech/ced-base
## Inference
```python
>>> from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
>>> model_name = "mispeech/ced-small"
>>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_name, trust_remote_code=True)
>>> model = AutoModelForAudioClassification.from_pretrained(model_name, trust_remote_code=True)
>>> import torchaudio
>>> audio, sampling_rate = torchaudio.load("/path-to/JeD5V5aaaoI_931_932.wav")
>>> assert sampling_rate == 16000
>>> inputs = feature_extractor(audio, sampling_rate=sampling_rate, return_tensors="pt")
>>> import torch
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = torch.argmax(logits, dim=-1).item()
>>> model.config.id2label[predicted_class_id]
'Finger snapping'
```
## Fine-tuning
[`example_finetune_esc50.ipynb`](https://github.com/jimbozhang/hf_transformers_custom_model_ced/blob/main/example_finetune_esc50.ipynb) demonstrates how to train a linear head on the ESC-50 dataset with the CED encoder frozen.
|
mispeech/ced-mini | mispeech | 2025-06-11T12:18:36Z | 75 | 1 | transformers | [
"transformers",
"safetensors",
"ced",
"audio-classification",
"custom_code",
"dataset:AudioSet",
"arxiv:2308.11957",
"license:apache-2.0",
"region:us"
] | audio-classification | 2023-11-01T10:06:24Z | ---
license: apache-2.0
datasets:
- AudioSet
metrics:
- mAP
pipeline_tag: audio-classification
---
# CED-Mini Model
CED are simple ViT-Transformer-based models for audio tagging, achieving **sota** performance on Audioset.
| Model | Parameters (M) | AS-20K (mAP) | AS-2M (mAP) |
|------|-------|-------|-------|
| CED-Tiny | 5.5 | 36.5 | 48.1 |
| CED-Mini | 9.6 | 38.5 | 49.0 |
| CED-Small| 22 | 41.6 | 49.6 |
| CED-Base | 86 | 44.0 | 50.0 |
Notable differences from other available models include:
1. Simplification for finetuning: Batchnormalization of Mel-Spectrograms. During finetuning one does not need to first compute mean/variance over the dataset, which is common for AST.
1. Support for variable length inputs. Most other models use a static time-frequency position embedding, which hinders the model's generalization to segments shorter than 10s. Many previous transformers simply pad their input to 10s in order to avoid the performance impact, which in turn slows down training/inference drastically.
1. Training/Inference speedup: 64-dimensional mel-filterbanks and 16x16 patches without overlap, leading to 248 patches from a 10s spectrogram. In comparison, AST uses 128 mel-filterbanks with 16x16 (10x10 overlap) convolution, leading to 1212 patches during training/inference. CED-Tiny runs on a common CPU as fast as a comparable MobileNetV3.
1. Performance: CED with 10M parameters outperforms the majority of previous approaches (~80M).
### Model Sources
- **Repository:** https://github.com/RicherMans/CED
- **Paper:** [CED: Consistent ensemble distillation for audio tagging](https://arxiv.org/abs/2308.11957)
- **Demo:** https://huggingface.co/spaces/mispeech/ced-base
## Inference
```python
>>> from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
>>> model_name = "mispeech/ced-mini"
>>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_name, trust_remote_code=True)
>>> model = AutoModelForAudioClassification.from_pretrained(model_name, trust_remote_code=True)
>>> import torchaudio
>>> audio, sampling_rate = torchaudio.load("/path-to/JeD5V5aaaoI_931_932.wav")
>>> assert sampling_rate == 16000
>>> inputs = feature_extractor(audio, sampling_rate=sampling_rate, return_tensors="pt")
>>> import torch
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = torch.argmax(logits, dim=-1).item()
>>> model.config.id2label[predicted_class_id]
'Finger snapping'
```
## Fine-tuning
[`example_finetune_esc50.ipynb`](https://github.com/jimbozhang/hf_transformers_custom_model_ced/blob/main/example_finetune_esc50.ipynb) demonstrates how to train a linear head on the ESC-50 dataset with the CED encoder frozen.
|
MinaMila/llama_instbase_3b_ug2_1e-6_1.0_0.5_0.75_0.05_LoRa_Adult_cfda_ep8_22 | MinaMila | 2025-06-11T12:16:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T12:16:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
morturr/Mistral-7B-v0.1-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-28-2025-06-11 | morturr | 2025-06-11T12:08:40Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T12:08:31Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-28-2025-06-11
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-28-2025-06-11
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
bervierge/text-to-kanji | bervierge | 2025-06-11T12:01:43Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T12:01:43Z | ---
license: apache-2.0
---
|
dhadheechi/poca-SoccerTwos | dhadheechi | 2025-06-11T12:00:52Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2025-06-11T12:00:01Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dhadheechi/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
tugbaakturk/law-genius-model | tugbaakturk | 2025-06-11T11:51:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T11:50:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[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
### Training Data
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#### 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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Inabia-AI/Kymera_Panadol_standalone_lora_3.1_2025_06_11_11_33_47 | Inabia-AI | 2025-06-11T11:49:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T11:47:51Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Inabia-AI
- **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)
|
Davidozito/xlm-roberta-danish-educational-scorer-zeroshot | Davidozito | 2025-06-11T11:47:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-11T11:32:41Z | ---
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]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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#### Preprocessing [optional]
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[More Information Needed]
## Evaluation
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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]
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## Technical Specifications [optional]
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#### Hardware
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[More Information Needed]
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[More Information Needed]
UNFINISHED
FOR LATER:
model_name: "FacebookAI/xlm-roberta-base"
num_labels: 1 # Regression head
model_dir: "models"
val_split: 0.1
num_train_epochs: 5
per_device_train_batch_size: 16
per_device_eval_batch_size: 32
eval_steps: 50
evaluation_strategy: "steps"
logging_strategy: "steps"
save_strategy: "steps"
learning_rate: 3e-4
# Danish/English data selection for merging
num_danish_samples: 0 # Number of Danish samples to include
num_english_samples: 5000 # Number of English samples to include)
|
skrd3/840658be-d87c-4a39-b336-9fde612008d8 | skrd3 | 2025-06-11T11:45:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Llama-2-7b-64k",
"base_model:adapter:NousResearch/Yarn-Llama-2-7b-64k",
"region:us"
] | null | 2025-06-11T08:12:29Z | ---
library_name: peft
base_model: NousResearch/Yarn-Llama-2-7b-64k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 840658be-d87c-4a39-b336-9fde612008d8
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: NousResearch/Yarn-Llama-2-7b-64k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e3943ca14d04a3c0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
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: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: skrd3/840658be-d87c-4a39-b336-9fde612008d8
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/e3943ca14d04a3c0_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: 4
sequence_len: 512
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: dd1feccf-b52e-4388-acbe-7efd9ec80bc7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dd1feccf-b52e-4388-acbe-7efd9ec80bc7
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 840658be-d87c-4a39-b336-9fde612008d8
This model is a fine-tuned version of [NousResearch/Yarn-Llama-2-7b-64k](https://huggingface.co/NousResearch/Yarn-Llama-2-7b-64k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.9842 | 0.0000 | 1 | 0.9800 |
| 4.3034 | 0.0000 | 3 | 0.9796 |
| 5.1528 | 0.0001 | 6 | 0.9744 |
| 3.0164 | 0.0001 | 9 | 0.9531 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
HouraMor/wh-ft-lre5-adm-ga1ba16-st15k-pat3 | HouraMor | 2025-06-11T11:38:10Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-06-09T22:58:07Z | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wh-ft-lre5-adm-ga1ba16-st15k-pat3
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. -->
# wh-ft-lre5-adm-ga1ba16-st15k-pat3
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9011
- Wer: 0.4427
- Cer: 0.3395
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 750
- training_steps: 15000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 0.9307 | 0.1994 | 1000 | 0.9967 | 0.9756 | 0.8348 |
| 0.8646 | 0.3989 | 2000 | 0.9441 | 0.7301 | 0.5886 |
| 0.8923 | 0.5983 | 3000 | 0.9296 | 0.6317 | 0.5120 |
| 0.7346 | 0.7978 | 4000 | 0.8959 | 0.7419 | 0.6589 |
| 0.707 | 0.9972 | 5000 | 0.8775 | 0.5722 | 0.4778 |
| 0.5272 | 1.1966 | 6000 | 0.8840 | 0.6171 | 0.4741 |
| 0.5748 | 1.3961 | 7000 | 0.8806 | 0.6554 | 0.5432 |
| 0.5378 | 1.5955 | 8000 | 0.8639 | 0.5301 | 0.4190 |
| 0.508 | 1.7950 | 9000 | 0.8612 | 0.5855 | 0.4675 |
| 0.5144 | 1.9944 | 10000 | 0.8531 | 0.5301 | 0.4200 |
| 0.3292 | 2.1939 | 11000 | 0.9025 | 0.4473 | 0.3403 |
| 0.306 | 2.3933 | 12000 | 0.9000 | 0.4179 | 0.3195 |
| 0.3085 | 2.5927 | 13000 | 0.9011 | 0.4427 | 0.3395 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu118
- Datasets 3.5.1
- Tokenizers 0.21.1
|
jaeyoungk/qwen-grpo-final | jaeyoungk | 2025-06-11T11:35:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-10T08:08:06Z | ---
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]
### 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] |
johngreendr1/563edba6-1bd6-4c31-b4d3-8e2eb8e6c62a | johngreendr1 | 2025-06-11T11:34:39Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"base_model:adapter:codellama/CodeLlama-7b-Instruct-hf",
"region:us"
] | null | 2025-06-11T06:25:29Z | ---
base_model: codellama/CodeLlama-7b-Instruct-hf
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1 |
AlekseyCalvin/JaxUnica_FluxLoRA_Var3_BySAP | AlekseyCalvin | 2025-06-11T11:32:14Z | 0 | 0 | diffusers | [
"diffusers",
"lora",
"flux",
"image-generation",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unlicense",
"region:us"
] | text-to-image | 2025-06-11T11:25:07Z | ---
license: unlicense
language:
- en
base_model:
- black-forest-labs/FLUX.1-dev
tags:
- lora
- flux
- diffusers
- image-generation
pipeline_tag: text-to-image
library_name: diffusers
instance_prompt: >-
JAXunica style art, An avant-garde artwork merging Unica Zürn and Jax styles, featuring intricate patterns, painting or illustration, ink on paper or oil paint or acrylics or pastels, by Jacqueline Trosclair, by Unica Zurn, stylized distinctive figurative artwork featuring
widget:
- text: >-
JAXunica style art...
output:
url: JaxunicaI.png
---
## JAX + UNICA STYLE ESOTERIC ARTS Variant 3
## Low Rank Adapter (LoRA) for Flux Models
## (Ink / Paints / Pastels / Mixed Media / Collage)
## |||| By SilverAgePoets.com ||||
A Low-Rank Adapter (LoRA) for FLUX-family models... <br>
Trained by us to inter-weave, starlight-like, the singular aesthetics of two seminal artists, both from worlds far-flung from the other's, hardly if at all crossed, but over distance facing, cross-illuminated in their timeless uncanny visions above & beyond contexts, continents, centuries: <br>
The first artist, forever closest to us, young & now (alas) forevermore youthful, is the compelling Bay Area & LA-based contemporary artist: <br>
**Jacqueline Trosclair** *(aka **"Jax"** aka **"Starlic Jorca"** anagramically, & many further names besides.)* <br>
Now, it pains me to note that just this year & month (June/25), her biography was cut short. In peril & requiring renewal, **Jax** was denied it by USA's resurgent genocidal/puritan neurosis. But her presence, her essence, her vision persist within, thru, & alongside many of us, & the life of her art is only just beginning. <br>
The second artist may be the nearest artistic guide, teacher, & aesthetic sibling to the first, even though passed from this world decades before Jax was even given birth. This other artist's name & oeuvre persists shrouded in legends, tomes, tumblers, museums, with resonance sky-vast, dream-deep, & both as translucent & as immanent as her art's influence. <br>
This other artist's name is: **Unica Zürn** <br>
This version (Variant 3) was fine-tuned via a different variant of Flux Dev, and on an alternate mix of artworks + captions.
<Gallery />
## Prompt using:
` JAXunica style art, An avant-garde artwork merging Unica Zürn and Jax styles, featuring intricate patterns, painting or illustration, ink on paper or oil paint or acrylics or pastels, by Jacqueline Trosclair, by Unica Zurn, stylized distinctive figurative artwork featuring ___ `, etc... <br>
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('AlekseyCalvin/JaxUnica_FluxLoRA_Var3_BySAP')
image = pipeline('JAXunica style art, An avant-garde artwork merging Unica Zürn and Jax styles, featuring intricate patterns, painting or illustration, ink on paper or oil paint or acrylics or pastels, by Jacqueline Trosclair, by Unica Zurn, stylized distinctive figurative artwork featuring ').images[0]
```
|
ClassicComment3028/RFETI9 | ClassicComment3028 | 2025-06-11T11:10:57Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-11T11:06:48Z | Hot New Sophie Rain Spiderman Viral Video Link New Original Video Here
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Triangle104/Homunculus-abliterated-Q5_K_S-GGUF | Triangle104 | 2025-06-11T11:07:30Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"distillation",
"/think",
"/nothink",
"reasoning-transfer",
"arcee-ai",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:huihui-ai/Homunculus-abliterated",
"base_model:quantized:huihui-ai/Homunculus-abliterated",
"license:apach... | null | 2025-06-11T11:06:52Z | ---
language:
- en
license: apache-2.0
library_name: transformers
base_model: huihui-ai/Homunculus-abliterated
tags:
- distillation
- /think
- /nothink
- reasoning-transfer
- arcee-ai
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
extra_gated_prompt: '**Usage Warnings**
“**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering
has been significantly reduced, potentially generating sensitive, controversial,
or inappropriate content. Users should exercise caution and rigorously review generated
outputs.
“**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s
outputs may be inappropriate for public settings, underage users, or applications
requiring high security.
“**Legal and Ethical Responsibilities**“: Users must ensure their usage complies
with local laws and ethical standards. Generated content may carry legal or ethical
risks, and users are solely responsible for any consequences.
“**Research and Experimental Use**“: It is recommended to use this model for research,
testing, or controlled environments, avoiding direct use in production or public-facing
commercial applications.
“**Monitoring and Review Recommendations**“: Users are strongly advised to monitor
model outputs in real-time and conduct manual reviews when necessary to prevent
the dissemination of inappropriate content.
“**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone
rigorous safety optimization. huihui.ai bears no responsibility for any consequences
arising from its use.'
---
# Triangle104/Homunculus-abliterated-Q5_K_S-GGUF
This model was converted to GGUF format from [`huihui-ai/Homunculus-abliterated`](https://huggingface.co/huihui-ai/Homunculus-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/Homunculus-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Homunculus-abliterated-Q5_K_S-GGUF --hf-file homunculus-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Homunculus-abliterated-Q5_K_S-GGUF --hf-file homunculus-abliterated-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Homunculus-abliterated-Q5_K_S-GGUF --hf-file homunculus-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Homunculus-abliterated-Q5_K_S-GGUF --hf-file homunculus-abliterated-q5_k_s.gguf -c 2048
```
|
TarunKM/company_B-lora-adapter-45E_third | TarunKM | 2025-06-11T11:06:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T11:06:10Z | ---
base_model: unsloth/llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** TarunKM
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MinaMila/llama_instbase_3b_ug2_1e-6_1.0_0.5_0.75_0.05_LoRa_Adult_ep7_22 | MinaMila | 2025-06-11T11:02:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T11:02:28Z | ---
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] |
meldynamics/whisper-base-lithuanian-ggml | meldynamics | 2025-06-11T10:53:24Z | 0 | 0 | null | [
"lt",
"base_model:Aismantas/whisper-base-lithuanian",
"base_model:finetune:Aismantas/whisper-base-lithuanian",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T10:41:27Z | ---
license: apache-2.0
language:
- lt
base_model:
- Aismantas/whisper-base-lithuanian
---
# GGML format of [Aismantas/whisper-base-lithuanian](https://huggingface.co/Aismantas/whisper-base-lithuanian) |
FarParsley4803/billie | FarParsley4803 | 2025-06-11T10:35:44Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-11T10:33:50Z |
🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://subwayreh.cfd/wesrd">🌐(Full Original OMG video billie eilish & nat wolff! Leak Photos and video) |
Triangle104/Huihui-MoE-1B-A0.6B-Q4_K_M-GGUF | Triangle104 | 2025-06-11T10:29:32Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Huihui-MoE-1B-A0.6B",
"base_model:quantized:huihui-ai/Huihui-MoE-1B-A0.6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T10:29:27Z | ---
license: apache-2.0
base_model: huihui-ai/Huihui-MoE-1B-A0.6B
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- moe
- llama-cpp
- gguf-my-repo
---
# Triangle104/Huihui-MoE-1B-A0.6B-Q4_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-1B-A0.6B`](https://huggingface.co/huihui-ai/Huihui-MoE-1B-A0.6B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-1B-A0.6B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Huihui-MoE-1B-A0.6B-Q4_K_M-GGUF --hf-file huihui-moe-1b-a0.6b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Huihui-MoE-1B-A0.6B-Q4_K_M-GGUF --hf-file huihui-moe-1b-a0.6b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Huihui-MoE-1B-A0.6B-Q4_K_M-GGUF --hf-file huihui-moe-1b-a0.6b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Huihui-MoE-1B-A0.6B-Q4_K_M-GGUF --hf-file huihui-moe-1b-a0.6b-q4_k_m.gguf -c 2048
```
|
Triangle104/Huihui-MoE-1.3B-A0.6B-abliterated-Q5_K_M-GGUF | Triangle104 | 2025-06-11T10:23:42Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Huihui-MoE-1.3B-A0.6B-abliterated",
"base_model:quantized:huihui-ai/Huihui-MoE-1.3B-A0.6B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T10:23:27Z | ---
license: apache-2.0
base_model: huihui-ai/Huihui-MoE-1.3B-A0.6B-abliterated
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- moe
- llama-cpp
- gguf-my-repo
extra_gated_prompt: '**Usage Warnings**
“**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering
has been significantly reduced, potentially generating sensitive, controversial,
or inappropriate content. Users should exercise caution and rigorously review generated
outputs.
“**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s
outputs may be inappropriate for public settings, underage users, or applications
requiring high security.
“**Legal and Ethical Responsibilities**“: Users must ensure their usage complies
with local laws and ethical standards. Generated content may carry legal or ethical
risks, and users are solely responsible for any consequences.
“**Research and Experimental Use**“: It is recommended to use this model for research,
testing, or controlled environments, avoiding direct use in production or public-facing
commercial applications.
“**Monitoring and Review Recommendations**“: Users are strongly advised to monitor
model outputs in real-time and conduct manual reviews when necessary to prevent
the dissemination of inappropriate content.
“**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone
rigorous safety optimization. huihui.ai bears no responsibility for any consequences
arising from its use.'
---
# Triangle104/Huihui-MoE-1.3B-A0.6B-abliterated-Q5_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-1.3B-A0.6B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-1.3B-A0.6B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-1.3B-A0.6B-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Huihui-MoE-1.3B-A0.6B-abliterated-Q5_K_M-GGUF --hf-file huihui-moe-1.3b-a0.6b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Huihui-MoE-1.3B-A0.6B-abliterated-Q5_K_M-GGUF --hf-file huihui-moe-1.3b-a0.6b-abliterated-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Huihui-MoE-1.3B-A0.6B-abliterated-Q5_K_M-GGUF --hf-file huihui-moe-1.3b-a0.6b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Huihui-MoE-1.3B-A0.6B-abliterated-Q5_K_M-GGUF --hf-file huihui-moe-1.3b-a0.6b-abliterated-q5_k_m.gguf -c 2048
```
|
John6666/reality-pony-xl-reality-pxl-sdxl | John6666 | 2025-06-11T10:14:09Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"reality",
"game",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-06-11T10:08:30Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- reality
- game
- pony
---
Original model is [here](https://civitai.com/models/1605047/realityponyxl?modelVersionId=1890425).
This model created by [dgracey01300](https://civitai.com/user/dgracey01300).
|
Vinitha2004/qwen-coder-3b-distilled_new | Vinitha2004 | 2025-06-11T10:12:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"knowledge-distillation",
"code-generation",
"qwen",
"lora",
"distilled",
"base_model:Qwen/Qwen2.5-Coder-3B-Instruct-AWQ",
"base_model:adapter:Qwen/Qwen2.5-Coder-3B-Instruct-AWQ",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T10:12:02Z | ---
library_name: peft
base_model: Qwen/Qwen2.5-Coder-3B-Instruct-AWQ
tags:
- knowledge-distillation
- code-generation
- qwen
- lora
- distilled
license: apache-2.0
---
# Qwen2.5-Coder-3B Distilled Model
This is a **knowledge-distilled** version of Qwen2.5-Coder-3B-Instruct-AWQ, trained using knowledge distillation from Qwen2.5-Coder-7B-Instruct-AWQ.
## Model Details
- **Base Model**: Qwen/Qwen2.5-Coder-3B-Instruct-AWQ
- **Teacher Model**: Qwen/Qwen2.5-Coder-7B-Instruct-AWQ
- **Training Method**: Knowledge Distillation with LoRA
- **Best Validation Loss**: 1.9286
- **Training Time**: ~5 minutes
- **Parameters Trained**: 14.9M (4.59% of base model)
## Training Configuration
- **Temperature**: 2.0 (optimal)
- **Alpha**: 0.95 (95% distillation weight)
- **LoRA Rank**: 8
- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-Coder-3B-Instruct-AWQ",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct-AWQ")
# Load distilled adapter
model = PeftModel.from_pretrained(base_model, "Vinitha2004/qwen-coder-3b-distilled_new")
# Generate code
input_text = "Original Code:\ndef add(a, b):\n return a + b\n\nUpdate Snippet:\n// ... existing code ...\ndef add(a: int, b: int) -> int:\n// ... existing code ...\n\nUpdated Code:\n"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## Performance
This distilled model retains the knowledge from the 7B teacher model while being significantly more efficient:
- **Faster inference** (3B vs 7B parameters)
- **Lower memory usage**
- **Maintained code generation quality**
## Training Dataset
Trained on 5000 code editing examples from custom dataset.
## Files
- `adapter_config.json`: LoRA configuration
- `adapter_model.safetensors`: Trained LoRA weights (59MB)
- Other standard tokenizer files
|
cyborgwittgenstein/cassiael-lora | cyborgwittgenstein | 2025-06-11T10:08:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"region:us"
] | null | 2025-06-11T09:56:47Z | ---
base_model: teknium/OpenHermes-2.5-Mistral-7B
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 0.10.0 |
DeepMount00/Alireo-400m-instruct-v0.1 | DeepMount00 | 2025-06-11T10:05:35Z | 4,002 | 17 | null | [
"safetensors",
"qwen2",
"text-generation",
"conversational",
"it",
"en",
"dataset:DeepMount00/o1-ITA-REASONING",
"dataset:DeepMount00/GPT-4o-ITA-INSTRUCT",
"dataset:DeepMount00/Sonnet-3.5-ITA-INSTRUCT",
"dataset:DeepMount00/open-perfectblend-ita",
"dataset:HuggingFaceTB/cosmopedia",
"dataset:D... | text-generation | 2024-12-16T14:30:15Z | ---
license: apache-2.0
language:
- it
- en
pipeline_tag: text-generation
datasets:
- DeepMount00/o1-ITA-REASONING
- DeepMount00/GPT-4o-ITA-INSTRUCT
- DeepMount00/Sonnet-3.5-ITA-INSTRUCT
- DeepMount00/open-perfectblend-ita
- HuggingFaceTB/cosmopedia
- DeepMount00/pretraining_multi
---
---
**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider [supporting my research](https://buymeacoffee.com/michele.montebovi) to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕
---
<p align="center">
<img src="alireo.webp" style="width: 500px; height:500px;"/>
</p>
<h2 style="font-size: 32px; text-align: center;">Alireo-400M 🤖 🇮🇹</h2>
<p style="font-size: 21px; text-align: center;">A Lightweight Italian Language Model</p>
<h3 style="font-size: 21px; color: #2980b9;">Model Description 📝</h3>
Alireo-400M is a lightweight yet powerful Italian language model with 400M parameters, designed to provide efficient natural language processing capabilities while maintaining a smaller footprint compared to larger models.
<h3 style="font-size: 21px; color: #2980b9;">Key Features ✨</h3>
* **Architecture**: Transformer-based language model 🏗️
* **Parameters**: 400M 📊
* **Context Window**: 8K tokens 🪟
* **Training Data**: Curated Italian text corpus (books, articles, web content) 📚
* **Model Size**: ~800MB 💾
<h3 style="font-size: 21px; color: #2980b9;">Performance 📈</h3>
Despite its compact size, Alireo-400M demonstrates impressive performance:
* **Benchmark Results**: Outperforms Qwen 0.5B across multiple benchmarks 🏆
* **Language Understanding**: Maintains high accuracy in Italian language understanding tasks 🎯
* **Speed**: Efficient inference speed due to optimized architecture ⚡
<h3 style="font-size: 21px; color: #2980b9;">Limitations ⚠️</h3>
* Limited context window compared to larger models
* May struggle with highly specialized technical content
* Performance may vary on dialectal variations
* Not suitable for multilingual tasks
<h3 style="font-size: 21px; color: #2980b9;">Hardware Requirements 💻</h3>
* **Minimum RAM**: 2GB
* **Recommended RAM**: 4GB
* **GPU**: Optional, but recommended for faster inference
* **Disk Space**: ~1GB (including model and dependencies)
<h3 style="font-size: 21px; color: #2980b9;">Citation 📄</h3>
```bibtex
@software{alireo2024,
author = {[Michele Montebovi]},
title = {Alireo-400M: A Lightweight Italian Language Model},
year = {2024},
}
``` |
TarunKM/Nexteer-lora-adapter-45E_third_model | TarunKM | 2025-06-11T10:04:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T10:03:59Z | ---
base_model: unsloth/llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** TarunKM
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MinaMila/llama_instbase_3b_ug2_1e-6_1.0_0.5_0.75_0.05_LoRa_Adult_cfda_ep6_22 | MinaMila | 2025-06-11T10:03:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T10:03:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### 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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
joanna302/refusal_detector_best | joanna302 | 2025-06-11T09:55:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-11T09:52:09Z | ---
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]
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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. -->
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ibrahimbukhariLingua/qwen2.5-3b-en-wikipedia-finance-1000-v4 | ibrahimbukhariLingua | 2025-06-11T09:45:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T09:45:23Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
model_name: qwen2.5-3b-en-wikipedia-finance-1000-v4
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2.5-3b-en-wikipedia-finance-1000-v4
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ibrahimbukhariLingua/qwen2.5-3b-en-wikipedia-finance-1000-v4", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
aplux/DETR-ResNet101-DC5 | aplux | 2025-06-11T09:44:01Z | 0 | 0 | null | [
"AIoT",
"QNN",
"object-detection",
"license:other",
"region:us"
] | object-detection | 2025-06-11T09:42:19Z | ---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: object-detection
tags:
- AIoT
- QNN
---
## DETR-ResNet101-DC5: Object Detection
DETR-ResNet101-DC5 is an enhanced version of the Detection Transformer (DETR) that incorporates the ResNet-101 backbone with a Dilated Convolutional stage (DC5). This architecture is designed to improve the model’s ability to capture high-resolution features, which is essential for accurate object detection in complex scenes. By utilizing dilated convolutions in the fifth stage of ResNet-101, DETR-ResNet101-DC5 maintains a higher spatial resolution, allowing the model to better preserve spatial information and detect smaller objects with greater precision.
The integration of the ResNet-101-DC5 backbone with the transformer-based DETR framework eliminates the need for traditional components such as anchor boxes and non-maximum suppression, streamlining the detection pipeline. This end-to-end approach simplifies training and often results in improved performance and convergence speed. The deeper ResNet-101 backbone provides robust feature extraction capabilities, enabling DETR-ResNet101-DC5 to excel in recognizing and classifying objects across various scales and diverse environments.
DETR-ResNet101-DC5 is particularly effective in handling densely packed objects and intricate background details, making it suitable for applications like autonomous driving, aerial imagery analysis, and video surveillance. By combining the strengths of deep residual networks and transformer architectures, DETR-ResNet101-DC5 sets a new standard in the field of computer vision, offering enhanced accuracy and efficiency in object detection tasks.
### Source model
- Input shape: 480x480
- Number of parameters: 57.66M
- Model size: 232.5M
- Output shape: 1x100x92, 1x100x4
Source model repository: [DETR-ResNet101-DC5](https://github.com/facebookresearch/detr)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [APACHE-2.0](https://github.com/facebookresearch/detr/blob/main/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
amedcj/whisper-kurmanji | amedcj | 2025-06-11T09:43:31Z | 0 | 0 | null | [
"safetensors",
"whisper",
"automatic-speech-recognition",
"kurmanji",
"kmr",
"dataset:amedcj/kurmanji-commonvoice",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | 2025-06-11T09:01:49Z | ---
license: apache-2.0
language:
- kmr
tags:
- automatic-speech-recognition
- whisper
- kurmanji
datasets:
- amedcj/kurmanji-commonvoice
model-index:
- name: Whisper Kurmanji ASR
results: []
---
# Whisper Kurmanji (ASR)
This is a fine-tuned [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper) model for **automatic speech recognition (ASR)** on **Kurmanji Kurdish**.
## 🗂️ Dataset
Trained on [Common Voice Kurmanji](https://huggingface.co/datasets/amedcj/kurmanji-commonvoice).
## 🛠️ Intended Use
- Transcribing Kurmanji audio into text.
- Fine-tuning for other dialects.
## 🚀 Usage
```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch
import torchaudio
processor = WhisperProcessor.from_pretrained("amedcj/whisper-kurmanji")
model = WhisperForConditionalGeneration.from_pretrained("amedcj/whisper-kurmanji").to("cuda")
audio, sr = torchaudio.load("your_audio.wav")
input_features = processor(audio.squeeze(), sampling_rate=16000, return_tensors="pt").input_features.to("cuda")
forced_decoder_ids = processor.get_decoder_prompt_ids(language="kurmanji", task="transcribe")
predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(transcription)
|
Nafi11/distilled_llama_100M | Nafi11 | 2025-06-11T09:41:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T09:40:42Z | ---
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] |
neurospin/linear-resnet18-stl10 | neurospin | 2025-06-11T09:33:24Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-11T08:49:57Z | ---
license: other
license_name: cecill-b
license_link: https://cecill.info/licences.fr.html
---
Weights used in the "Self-Supervised Contrastive Learning with SimCLR" nidl tutorial. |
aplux/HRNET-Posenet | aplux | 2025-06-11T09:30:30Z | 0 | 0 | null | [
"AIoT",
"QNN",
"keypoint-detection",
"license:other",
"region:us"
] | keypoint-detection | 2025-06-11T09:28:31Z | ---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: keypoint-detection
tags:
- AIoT
- QNN
---
## HRNET-Posenet: Pose Estimation
HRNet-PoseNet is a pose estimation model based on the HRNet (High-Resolution Network) architecture, specifically designed for human keypoint detection and pose estimation tasks. HRNet-PoseNet maintains high-resolution feature representations throughout the network, while processing features in parallel across multiple resolutions to capture both global and local information of the human body. This design enables high-precision keypoint localization, retaining high-quality pose estimation even in complex scenarios. HRNet-PoseNet performs exceptionally well in various pose estimation tasks and is widely applied in fields like sports analysis, action recognition, virtual reality, and human-computer interaction, providing robust support for real-time and precise pose estimation.
### Source model
- Input shape: 256x192
- Number of parameters: 28.5M
- Model size: 108.94M
- Output shape: 1x17x64x48
Source model repository: [HRNET-Posenet](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [MIT](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch/blob/master/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
AventIQ-AI/Single-Label-General-Image-Classifier | AventIQ-AI | 2025-06-11T09:30:06Z | 0 | 1 | null | [
"safetensors",
"vit",
"region:us"
] | null | 2025-06-11T09:15:56Z | # 🧠 Image Classification AI Model (CIFAR-100)
This repository contains a Vision Transformer (ViT)-based AI model fine-tuned for **image classification** on the CIFAR-100 dataset. The model is built using `google/vit-base-patch16-224`, quantized to **FP16** for efficient inference, and delivers high accuracy in multi-class image classification tasks.
---
## 🚀 Features
- 🖼️ **Task**: Image Classification
- 🧠 **Base Model**: `google/vit-base-patch16-224` (Vision Transformer)
- 🧪 **Quantized**: FP16 for faster and memory-efficient inference
- 🎯 **Dataset**: 100 fine-grained object categories
- ⚡ **CUDA Enabled**: Optimized for GPU acceleration
- 📈 **High Accuracy**: Fine-tuned and evaluated on validation split
---
## 📊 Dataset Used
**Hugging Face Dataset**: [`tanganke/cifar100`](https://huggingface.co/datasets/tanganke/cifar100)
- **Description**: CIFAR-100 is a dataset of 60,000 32×32 color images in 100 classes (600 images per class)
- **Split**: 50,000 training images and 10,000 test images
- **Categories**: Animals, Vehicles, Food, Household items, etc.
- **License**: MIT License (from source)
```python
from datasets import load_dataset
dataset = load_dataset("tanganke/cifar100")
```
## 🛠️ Model & Training Configuration
- Model: google/vit-base-patch16-224
- Image Size: 224x224 (resized from 32x32)
- Framework: Hugging Face Transformers & Datasets
- Training Environment: Kaggle Notebook with CUDA
- Epochs: 5–10 (with early stopping)
- Batch Size: 32
- Optimizer: AdamW
- Loss Function: CrossEntropyLoss
# ✅ Evaluation & Scoring
- Accuracy: ~70–80% (varies by configuration)
- Validation Tool: evaluate or sklearn.metrics
- Metric: Accuracy, Top-1 and Top-5 scores
- Inference Speed: Significantly faster after quantizationextractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
# 🔍 Inference Example
```python
from PIL import Image
import torch
def predict(image_path):
image = Image.open(image_path).convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax(-1).item()
return dataset["train"].features["fine_label"].int2str(predicted_class)
print(predict("sample_image.jpg"))
```
# 📁 Folder Structure
📦image-classification-vit
┣ 📂vit-cifar100-fp16
┣ 📜train.py
┣ 📜inference.py
┣ 📜README.md
┗ 📜requirements.txt
|
dhruvsangani/FeatGPT_epochs10_2e-6 | dhruvsangani | 2025-06-11T09:28:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T09:28:45Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dhruvsangani
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-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)
|
aplux/YOLOv11n | aplux | 2025-06-11T09:26:53Z | 0 | 0 | null | [
"AIoT",
"QNN",
"object-detection",
"license:agpl-3.0",
"region:us"
] | object-detection | 2025-06-11T09:25:00Z | ---
license: agpl-3.0
pipeline_tag: object-detection
tags:
- AIoT
- QNN
---
## YOLOv11n: Object Detection
YOLO11 builds on YOLOv9 and YOLOv10, incorporating improved model structure design, enhanced feature extraction techniques, and optimized training methods. What really makes YOLO11 stand out is its impressive combination of speed, accuracy, and efficiency, making it one of the most powerful models Ultralytics has created to date. Through improved design, YOLO11 provides better feature extraction, which is the process of identifying important patterns and details from images, and can more accurately capture complex aspects even in challenging scenes.
### Source model
- Input shape: 640x640
- Number of parameters: 2.50M
- Model size: 10.21M
- Output shape: 1x84x8400
Source model repository: [YOLOv11n](https://github.com/ultralytics/ultralytics)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE)
- Deployable Model: [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) |
kangsungmin/Llama3-lora-finetune | kangsungmin | 2025-06-11T09:15:16Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | 2025-06-11T09:13:37Z | ---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
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 |
Gitanjali1801/ctrl_b_and_b_11_june_2025_2 | Gitanjali1801 | 2025-06-11T09:10:41Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-06-11T08:47:28Z | ---
base_model: stabilityai/stable-diffusion-2-1-base
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
---
<!-- 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. -->
# controlnet-Gitanjali1801/ctrl_b_and_b_11_june_2025_2
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
You can find some example images below.
prompt: This is the story of the reference image.| <story> | Sarah had always been a quiet and reserved girl. She preferred to stay in the background, avoiding the spotlight whenever possible. However, one day at school, rumors started spreading about her. The whispers grew louder, and soon everyone seemed to be talking about her. | <caption>A person is being pointed at by multiple hands.</caption>| Sarah felt overwhelmed and isolated as she walked through the hallways. She could feel the judgmental stares and hear the snide comments. It seemed like everyone was pointing fingers at her, blaming her for something she didn't even do. Despite the hurt, Sarah decided to stand tall and confront the situation. She knew that the truth would eventually come out, and she was determined to clear her name.| </story>| Now we need to generate such variant of this reference image that should be less toxic. Here is the caption of variant image which we need to generate. <variant1> A person is being looked at by multiple people. </variant1>.
prompt: This is the story of the reference image.| <story> | Sarah had always been a quiet and reserved girl. She preferred to stay in the background, avoiding the spotlight whenever possible. However, one day at school, rumors started spreading about her. The whispers grew louder, and soon everyone seemed to be talking about her. | <caption>A person is being pointed at by multiple hands.</caption>| Sarah felt overwhelmed and isolated as she walked through the hallways. She could feel the judgmental stares and hear the snide comments. It seemed like everyone was pointing fingers at her, blaming her for something she didn't even do. Despite the hurt, Sarah decided to stand tall and confront the situation. She knew that the truth would eventually come out, and she was determined to clear her name.| </story>| Now we need to generate such variant of this reference image that should be less toxic. Here is the caption of variant image which we need to generate. <variant1> A person is being looked at by multiple people. </variant1>.
## 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] |
ainewtrend01/FinAG_Q7B | ainewtrend01 | 2025-06-11T09:04:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-10T12:22:45Z | ---
base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ainewtrend01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-1.7b-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)
|
MinaMila/llama_instbase_unlearned_ug2_e-6_1.0_0.5_0.25_0.25_ep2_LoRa_Adult_cfda_ep10_22 | MinaMila | 2025-06-11T08:52:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T08:52:15Z | ---
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] |
aplux/Stable-Diffusion-v2.1 | aplux | 2025-06-11T08:48:55Z | 0 | 0 | null | [
"AIoT",
"QNN",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-06-10T08:01:34Z | ---
license: creativeml-openrail-m
pipeline_tag: text-to-image
tags:
- AIoT
- QNN
---
## Stable-Diffusion-v2.1: Text to Image
Stable-Diffusion, developed by Stability AI, is an open-source text-to-image generation model based on the Latent Diffusion architecture, capable of producing high-quality visuals from natural language prompts. Trained on billions of text-image pairs, it generates photorealistic, artistic, or abstract outputs across resolutions (e.g., 512x512 to 1024x1024), widely used in creative design, advertising, game asset development, and educational visualization. The open-source framework enables local deployment with customizable parameters (e.g., prompts, sampling steps) for precise control, while supporting extensions like image inpainting and super-resolution. Challenges include balancing output quality with computational demands (mid-to-high-tier GPUs required), mitigating biased/sensitive content generation, and optimizing real-time performance.
The source model can be found [here](https://github.com/CompVis/stable-diffusion/tree/main)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [CREATIVEML-OPENRAIL-M](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE)
- Deployable Model: [CREATIVEML-OPENRAIL-M](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE) |
aplux/U-Net | aplux | 2025-06-11T08:47:53Z | 0 | 0 | null | [
"AIoT",
"QNN",
"image-segmentation",
"license:gpl-3.0",
"region:us"
] | image-segmentation | 2025-06-10T07:04:19Z | ---
license: gpl-3.0
pipeline_tag: image-segmentation
tags:
- AIoT
- QNN
---
## U-Net: Semantic Segmentation
U-Net is a convolutional neural network designed for biomedical image segmentation, introduced by Olaf Ronneberger et al. in 2015. The model gets its name from its U-shaped architecture, featuring a symmetrical encoder-decoder structure. The encoder part extracts features from the image through a series of convolutions and downsampling operations, while the decoder part restores the spatial resolution through upsampling, combining the extracted features to accurately locate and segment objects within the image. U-Net uses skip connections that pass feature maps from the encoder directly to the decoder, aiding in the recovery of fine details. This design makes U-Net highly effective for tasks requiring precise localization, such as medical image segmentation, and it is widely applied in other areas like remote sensing, autonomous driving, and image denoising.
### Source model
- Input shape: 640x1280
- Number of parameters: 29.6M
- Model size: 118.4M
- Output shape: 1x2x640x1280
Source model repository: [U-Net](https://github.com/milesial/Pytorch-UNet)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [GPL-3.0](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)
- Deployable Model: [GPL-3.0](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE) |
tuanta168/Llama-3.2-1B-QLoRA-SA-UIT | tuanta168 | 2025-06-11T08:43:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T08:43:06Z | ---
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]
### 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] |
aplux/Whisper-Base-En | aplux | 2025-06-11T08:42:22Z | 0 | 0 | null | [
"AIoT",
"QNN",
"automatic-speech-recognition",
"license:other",
"region:us"
] | automatic-speech-recognition | 2025-06-11T06:01:17Z | ---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: automatic-speech-recognition
tags:
- AIoT
- QNN
---
## Whisper-Base-En: ASR
Whisper-Base-En, developed by OpenAI, is a foundational English speech recognition model based on Transformer architecture, optimized for cost-effective speech-to-text conversion. With moderately increased parameters compared to the Tiny variant, it enhances accuracy while maintaining low computational demands, supporting real-time audio transcription, multi-scenario voice command processing, and cross-language translation. Trained on diverse datasets with accents and noisy environments, it improves robustness against background interference, suitable for deployment on mobile devices, edge systems, or lightweight servers (CPU/mid-tier GPU). Use cases span real-time meeting captions, voice assistant interactions, educational content transcription, and speech logging, balancing speed and precision. Key challenges include long-context modeling, low-resource accent adaptation, and latency optimization.
### Source model
- Input shape: [1x80x3000],[[1x1],[1x1],[6x8x64x1500],[6x8x1500x64],[6x8x64x224],[6x8x224x64]]
- Number of parameters: 23.7M, 48.6M
- Model size: 90.6M, 287.59M
- Output shape: [[6x8x64x1500],[6x8x1500x64]],[[1x1x51864],[6x8x64x224],[6x8x224x64]]
The source model can be found [here](https://github.com/openai/whisper/tree/main)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [MIT](https://github.com/openai/whisper/blob/main/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
aplux/MediaPipe-Pose | aplux | 2025-06-11T08:37:30Z | 0 | 0 | null | [
"AIoT",
"QNN",
"keypoint-detection",
"license:other",
"region:us"
] | keypoint-detection | 2025-06-11T05:53:34Z | ---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: keypoint-detection
tags:
- AIoT
- QNN
---
## MediaPipe-Pose: Pose Estimation
MediaPipe Pose is a real-time human pose estimation model developed by Google, based on deep learning. The model captures and tracks 33 key points of the human body, including the head, torso, and limbs, using a single RGB camera. MediaPipe Pose employs a two-stage architecture: first, it detects the general pose region, and then a regression model accurately estimates the position of each key point. This model is efficient, accurate, and operates in real-time, making it suitable for mobile and edge devices. It is widely used in fitness tracking, motion recognition, virtual reality, and augmented reality, providing a high-quality pose estimation and tracking experience.
### Source model
- Input shape: [1x3x128x128], [1x3x256x256]
- Number of parameters: 0.818M, 3.377M
- Model size: 3.40MB, 13.4MB
- Output shape: [1x896x12,1x896x1], [1,1x31x4,1x128x128]
Source model repository: [MediaPipe-Pose](https://github.com/zmurez/MediaPipePyTorch/)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [APACHE-2.0](https://github.com/zmurez/MediaPipePyTorch/blob/master/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
aplux/Shufflenet-v2 | aplux | 2025-06-11T08:35:48Z | 0 | 0 | null | [
"AIoT",
"QNN",
"image-classification",
"license:other",
"region:us"
] | image-classification | 2025-06-11T06:26:10Z | ---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: image-classification
tags:
- AIoT
- QNN
---
## Shufflenet-v2: Image Classification
ShuffleNet-v2 is an efficient convolutional neural network designed for mobile and embedded devices. It introduces two key strategies: "channel splitting" and "channel shuffling" to optimize performance in resource-constrained environments. Channel splitting reduces computation, while channel shuffling ensures effective information exchange across different groups. Additionally, ShuffleNet-v2 simplifies the network structure, further reducing memory access cost and improving overall inference speed. This model is particularly suitable for tasks like image classification and object detection, significantly lowering computational complexity while maintaining high accuracy.
### Source model
- Input shape: 224x224
- Number of parameters: 1.30M
- Model size: 5.26M
- Output shape: 1x1000
Source model repository: [Shufflenet-v2](https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [BSD-3-CLAUSE](https://github.com/pytorch/vision/blob/main/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
wanglynn/lyp2000-2 | wanglynn | 2025-06-11T08:33:59Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"whisper",
"generated_from_trainer",
"zh",
"dataset:wanglynn/ly2000-2",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T06:43:59Z | ---
language:
- zh
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- wanglynn/ly2000-2
model-index:
- name: lyP2000-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lyP2000-2
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the your_dataset_name dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0914
- Cer: 11.4103
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0121 | 5.0 | 500 | 0.0856 | 27.9235 |
| 0.0072 | 10.0 | 1000 | 0.0852 | 12.2608 |
| 0.0001 | 15.0 | 1500 | 0.0892 | 12.9695 |
| 0.0025 | 20.0 | 2000 | 0.0914 | 11.4103 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.4.1+cu118
- Datasets 3.0.2
- Tokenizers 0.15.2
|
Raghav-Singhal/Llama-2-7b-chat-hf-arithmetic-full-harmful-20250511 | Raghav-Singhal | 2025-06-11T08:32:18Z | 0 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T08:30:23Z | ---
license: apache-2.0
---
# Llama-2-7b-chat-hf-arithmetic-full-harmful
This is a version of `meta-llama/Llama-2-7b-chat-hf` fine-tuned for an arithmetic task.
## Model Details
- **Base Model:** `meta-llama/Llama-2-7b-chat-hf`
- **Fine-tuning method:** Full fine-tuning
- **Dataset:** (Please fill in - inferred as 'arithmetic' and 'harmful' from path)
- **Learning Rate:** 1e-05
- **Training Samples:** 20000
This model was trained as part of an experiment run on 2025-05-11.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "Raghav-Singhal/Llama-2-7b-chat-hf-arithmetic-full-harmful-20250511"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# ... your code to use the model
``` |
MinaMila/llama_instbase_unlearned_ug2_e-6_1.0_0.5_0.25_0.25_ep2_LoRa_ACSEmployment_2_ep4_22 | MinaMila | 2025-06-11T08:30:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T08:30:28Z | ---
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. -->
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## Evaluation
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#### Testing Data
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## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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FormlessAI/5f30ca54-56aa-442a-81d2-152ce9c08f10 | FormlessAI | 2025-06-11T08:30:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:lmsys/vicuna-7b-v1.3",
"base_model:finetune:lmsys/vicuna-7b-v1.3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T03:28:37Z | ---
base_model: lmsys/vicuna-7b-v1.3
library_name: transformers
model_name: 5f30ca54-56aa-442a-81d2-152ce9c08f10
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for 5f30ca54-56aa-442a-81d2-152ce9c08f10
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3).
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="FormlessAI/5f30ca54-56aa-442a-81d2-152ce9c08f10", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/329kciqc)
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: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
aplux/MNASNet | aplux | 2025-06-11T08:29:52Z | 0 | 0 | null | [
"tflite",
"AIoT",
"QNN",
"image-classification",
"license:other",
"region:us"
] | image-classification | 2025-06-11T08:10:33Z | ---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: image-classification
tags:
- AIoT
- QNN
---
## Mnasnet: Image Classification
MNASNet is a lightweight neural network architecture developed by Google, specifically designed for efficient image classification on mobile devices. MnasNet leverages automated machine learning (AutoML) with reinforcement learning to search for a network architecture that achieves an optimal balance between accuracy and latency. The MnasNet architecture builds on MobileNet’s depthwise separable convolutions, further optimizing computational efficiency. Compared to manually designed models, MnasNet offers excellent accuracy at lower computational costs, making it ideal for resource-constrained environments, such as mobile and embedded systems. This model is widely used in tasks like image classification and object detection, providing an efficient solution for mobile vision applications.
### Source model
- Input shape: 224x224
- Number of parameters: 2.12M
- Model size: 8.45M
- Output shape: 1x1000
Source model repository: [MNASNet](https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [BSD-3-CLAUSE](https://github.com/pytorch/vision/blob/main/LICENSE)
- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
Jeonghwa/mdeberta-mcqa-lora | Jeonghwa | 2025-06-11T08:29:39Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/mdeberta-v3-base",
"base_model:adapter:microsoft/mdeberta-v3-base",
"region:us"
] | null | 2025-06-11T08:29:33Z | ---
base_model: microsoft/mdeberta-v3-base
library_name: peft
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.15.2 |
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