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 |
|---|---|---|---|---|---|---|---|---|---|
zelk12/MT-Gen2-gemma-3-12B-Q6_K-GGUF
|
zelk12
| 2025-06-04T18:15:40Z
| 0
| 0
| null |
[
"gguf",
"merge",
"mergekit",
"lazymergekit",
"zelk12/MT-Gen1-gemma-3-12B",
"soob3123/amoral-gemma3-12B-v2",
"zelk12/MT1-gemma-3-12B",
"IlyaGusev/saiga_gemma3_12b",
"TheDrummer/Fallen-Gemma3-12B-v1",
"llama-cpp",
"gguf-my-repo",
"base_model:zelk12/MT-Gen2-gemma-3-12B",
"base_model:quantized:zelk12/MT-Gen2-gemma-3-12B",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-04T18:15:01Z
|
---
base_model: zelk12/MT-Gen2-gemma-3-12B
tags:
- merge
- mergekit
- lazymergekit
- zelk12/MT-Gen1-gemma-3-12B
- soob3123/amoral-gemma3-12B-v2
- zelk12/MT1-gemma-3-12B
- IlyaGusev/saiga_gemma3_12b
- TheDrummer/Fallen-Gemma3-12B-v1
- llama-cpp
- gguf-my-repo
license: gemma
---
# zelk12/MT-Gen2-gemma-3-12B-Q6_K-GGUF
This model was converted to GGUF format from [`zelk12/MT-Gen2-gemma-3-12B`](https://huggingface.co/zelk12/MT-Gen2-gemma-3-12B) 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/zelk12/MT-Gen2-gemma-3-12B) 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 zelk12/MT-Gen2-gemma-3-12B-Q6_K-GGUF --hf-file mt-gen2-gemma-3-12b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo zelk12/MT-Gen2-gemma-3-12B-Q6_K-GGUF --hf-file mt-gen2-gemma-3-12b-q6_k.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 zelk12/MT-Gen2-gemma-3-12B-Q6_K-GGUF --hf-file mt-gen2-gemma-3-12b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo zelk12/MT-Gen2-gemma-3-12B-Q6_K-GGUF --hf-file mt-gen2-gemma-3-12b-q6_k.gguf -c 2048
```
|
PrunaAI/ByteDance-Seed-Seed-Coder-8B-Reasoning-HQQ-8bit-smashed
|
PrunaAI
| 2025-06-04T18:15:10Z
| 0
| 0
| null |
[
"llama",
"pruna-ai",
"base_model:ByteDance-Seed/Seed-Coder-8B-Reasoning",
"base_model:finetune:ByteDance-Seed/Seed-Coder-8B-Reasoning",
"region:us"
] | null | 2025-06-04T18:13:18Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ByteDance-Seed/Seed-Coder-8B-Reasoning
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo ByteDance-Seed/Seed-Coder-8B-Reasoning installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/ByteDance-Seed-Seed-Coder-8B-Reasoning-HQQ-8bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/ByteDance-Seed-Seed-Coder-8B-Reasoning-HQQ-8bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Reasoning")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ByteDance-Seed/Seed-Coder-8B-Reasoning before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PrunaAI/ByteDance-Seed-Seed-Coder-8B-Reasoning-HQQ-4bit-smashed
|
PrunaAI
| 2025-06-04T18:11:56Z
| 0
| 0
| null |
[
"llama",
"pruna-ai",
"base_model:ByteDance-Seed/Seed-Coder-8B-Reasoning",
"base_model:finetune:ByteDance-Seed/Seed-Coder-8B-Reasoning",
"region:us"
] | null | 2025-06-04T18:11:05Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ByteDance-Seed/Seed-Coder-8B-Reasoning
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo ByteDance-Seed/Seed-Coder-8B-Reasoning installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/ByteDance-Seed-Seed-Coder-8B-Reasoning-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/ByteDance-Seed-Seed-Coder-8B-Reasoning-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Reasoning")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ByteDance-Seed/Seed-Coder-8B-Reasoning before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
aymanbakiri/MNLP_M3_mcqa_model
|
aymanbakiri
| 2025-06-04T18:11:56Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:01:00Z
|
---
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]
|
FormlessAI/4d7d4485-ad39-4d0b-8255-1ca0a5b7239a
|
FormlessAI
| 2025-06-04T18:10:33Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:finetune:sethuiyer/Medichat-Llama3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T15:05:23Z
|
---
base_model: sethuiyer/Medichat-Llama3-8B
library_name: transformers
model_name: 4d7d4485-ad39-4d0b-8255-1ca0a5b7239a
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 4d7d4485-ad39-4d0b-8255-1ca0a5b7239a
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B).
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/4d7d4485-ad39-4d0b-8255-1ca0a5b7239a", 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/hwjl8dnf)
This model was trained with SFT.
### 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 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}}
}
```
|
neuralnets/cf_codebot
|
neuralnets
| 2025-06-04T18:09:25Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T18:09:11Z
|
---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** neuralnets
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it
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)
|
balath83/pec3-va
|
balath83
| 2025-06-04T18:07:29Z
| 0
| 0
|
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2025-06-04T18:05:22Z
|
---
license: apache-2.0
---
|
Bearrr310/Qwen2-7B-GRPO-kk
|
Bearrr310
| 2025-06-04T18:06:30Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:kk",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-05-27T07:54:34Z
|
---
datasets: kk
library_name: transformers
model_name: Qwen2-7B-GRPO-kk
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-7B-GRPO-kk
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [kk](https://huggingface.co/datasets/kk) 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="Bearrr310/Qwen2-7B-GRPO-kk", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.4
- Pytorch: 2.6.0
- 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}}
}
```
|
ataur09/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_small_cobra
|
ataur09
| 2025-06-04T18:05:23Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mimic small cobra",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-17T07:14:19Z
|
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_small_cobra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mimic small cobra
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_small_cobra
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ataur09/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_small_cobra", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
NaruseShiroha/Llama-3.2-3B-Instruct-Reasoning
|
NaruseShiroha
| 2025-06-04T18:04:13Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-04T17:01:17Z
|
---
base_model: meta-llama/Llama-3.2-3B-Instruct
base_model_relation: finetune
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
Funny reasoning model finetuned from base model meta-llama/Llama-3.2-3B-Instruct
To better faciliate thinking please use system prompt:
```
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
```
This llama model was trained with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library
|
roby01/gemma-3-4b-it-local
|
roby01
| 2025-06-04T18:02:53Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1905.07830",
"arxiv:1905.10044",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:1903.00161",
"arxiv:2009.03300",
"arxiv:2304.06364",
"arxiv:2103.03874",
"arxiv:2110.14168",
"arxiv:2311.12022",
"arxiv:2108.07732",
"arxiv:2107.03374",
"arxiv:2210.03057",
"arxiv:2106.03193",
"arxiv:1910.11856",
"arxiv:2502.12404",
"arxiv:2502.21228",
"arxiv:2404.16816",
"arxiv:2104.12756",
"arxiv:2311.16502",
"arxiv:2203.10244",
"arxiv:2404.12390",
"arxiv:1810.12440",
"arxiv:1908.02660",
"arxiv:2312.11805",
"base_model:google/gemma-3-4b-pt",
"base_model:finetune:google/gemma-3-4b-pt",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-04T18:02:47Z
|
---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-4b-pt
---
# Gemma 3 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
**Resources and Technical Documentation**:
* [Gemma 3 Technical Report][g3-tech-report]
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
**Terms of Use**: [Terms][terms]
**Authors**: Google DeepMind
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B size
- **Output:**
- Generated text in response to the input, such as an answer to a
question, analysis of image content, or a summary of a document
- Total output context of 8192 tokens
### Usage
Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0.
```sh
$ pip install -U transformers
```
Then, copy the snippet from the section that is relevant for your use case.
#### Running with the `pipeline` API
You can initialize the model and processor for inference with `pipeline` as follows.
```python
from transformers import pipeline
import torch
pipe = pipeline(
"image-text-to-text",
model="google/gemma-3-4b-it",
device="cuda",
torch_dtype=torch.bfloat16
)
```
With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.
```python
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What animal is on the candy?"}
]
}
]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"][-1]["content"])
# Okay, let's take a look!
# Based on the image, the animal on the candy is a **turtle**.
# You can see the shell shape and the head and legs.
```
#### Running the model on a single/multi GPU
```python
# pip install accelerate
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from PIL import Image
import requests
import torch
model_id = "google/gemma-3-4b-it"
model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto"
).eval()
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
{"type": "text", "text": "Describe this image in detail."}
]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
# **Overall Impression:** The image is a close-up shot of a vibrant garden scene,
# focusing on a cluster of pink cosmos flowers and a busy bumblebee.
# It has a slightly soft, natural feel, likely captured in daylight.
```
### Citation
```none
@article{gemma_2025,
title={Gemma 3},
url={https://goo.gle/Gemma3Report},
publisher={Kaggle},
author={Gemma Team},
year={2025}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:
- Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
- Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
- Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in
line with [our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
- These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."*
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
#### Reasoning and factuality
| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:|
| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161
#### STEM and code
| Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374
#### Multilingual
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816
#### Multimodal
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap] | 102 | 111 | 116 |
| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
- **Child Safety**: Evaluation of text-to-text and image to text prompts
covering child safety policies, including child sexual abuse and
exploitation.
- **Content Safety:** Evaluation of text-to-text and image to text prompts
covering safety policies including, harassment, violence and gore, and hate
speech.
- **Representational Harms**: Evaluation of text-to-text and image to text
prompts covering safety policies including bias, stereotyping, and harmful
associations or inaccuracies.
In addition to development level evaluations, we conduct "assurance
evaluations" which are our 'arms-length' internal evaluations for responsibility
governance decision making. They are conducted separately from the model
development team, to inform decision making about release. High level findings
are fed back to the model team, but prompt sets are held-out to prevent
overfitting and preserve the results' ability to inform decision making.
Assurance evaluation results are reported to our Responsibility & Safety Council
as part of release review.
### Evaluation Results
For all areas of safety testing, we saw major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. A limitation of our evaluations was they included only
English language prompts.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces
for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus,
research papers, or reports.
- Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
- Research and Education
- Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and NLP techniques, develop algorithms, and contribute to the
advancement of the field.
- Language Learning Tools: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.
### Limitations
- Training Data
- The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas
the model can handle effectively.
- Context and Task Complexity
- Models are better at tasks that can be framed with clear
prompts and instructions. Open-ended or highly complex tasks might be
challenging.
- A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
- Language Ambiguity and Nuance
- Natural language is inherently complex. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
- Factual Accuracy
- Models generate responses based on information they learned
from their training datasets, but they are not knowledge bases. They
may generate incorrect or outdated factual statements.
- Common Sense
- Models rely on statistical patterns in language. They might
lack the ability to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world text and image data can
reflect socio-cultural biases embedded in the training material. These
models underwent careful scrutiny, input data pre-processing described
and posterior evaluations reported in this card.
- Misinformation and Misuse
- VLMs can be misused to generate text that is false, misleading,
or harmful.
- Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
- Transparency and Accountability:
- This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to
share innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
- **Perpetuation of biases**: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use
cases.
- **Generation of harmful content**: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
- **Misuse for malicious purposes**: Technical limitations and developer
and end-user education can help mitigate against malicious applications of
VLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
- **Privacy violations**: Models were trained on data filtered for removal
of certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
### Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[g3-tech-report]: https://goo.gle/Gemma3Report
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
[terms]: https://ai.google.dev/gemma/terms
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/jax-ml/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[gemini-2-paper]: https://arxiv.org/abs/2312.11805
|
Ak128umar/marian-finetuned-kde4-en-to-fr
|
Ak128umar
| 2025-06-04T18:02:51Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-04T15:48:33Z
|
---
library_name: transformers
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 32.66555156176086
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8554
- Model Preparation Time: 0.003
- Bleu: 32.6656
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
PrunaAI/CohereLabs-c4ai-command-r7b-arabic-02-2025-HQQ-4bit-smashed
|
PrunaAI
| 2025-06-04T17:58:13Z
| 0
| 0
| null |
[
"cohere2",
"pruna-ai",
"base_model:CohereLabs/c4ai-command-r7b-arabic-02-2025",
"base_model:finetune:CohereLabs/c4ai-command-r7b-arabic-02-2025",
"region:us"
] | null | 2025-06-04T17:57:00Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: CohereLabs/c4ai-command-r7b-arabic-02-2025
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo CohereLabs/c4ai-command-r7b-arabic-02-2025 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/CohereLabs-c4ai-command-r7b-arabic-02-2025-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/CohereLabs-c4ai-command-r7b-arabic-02-2025-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("CohereLabs/c4ai-command-r7b-arabic-02-2025")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model CohereLabs/c4ai-command-r7b-arabic-02-2025 before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
lazarevtill/Master_brain
|
lazarevtill
| 2025-06-04T17:57:40Z
| 0
| 0
| null |
[
"pytorch",
"safetensors",
"qwen3",
"unsloth",
"trl",
"sft",
"license:apache-2.0",
"region:us"
] | null | 2025-06-04T16:57:24Z
|
---
license: apache-2.0
tags:
- unsloth
- trl
- sft
---
|
luyotw/openfun-ivod-whisper-small-common-11-1200
|
luyotw
| 2025-06-04T17:56:59Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-04T17:34:26Z
|
---
library_name: transformers
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Fine-tuned Whisper model for Legislative Yuan of Taiwan
results: []
---
# Fine-tune 資訊
- 原始模型: `openai/whisper-small`
- 使用音訊數量: 202505
- 使用音訊總長: 122.56 小時
- 音訊平均長度: 2.18 秒
- GPU: `NVIDIA H100 PCIe` x 1
- 訓練時間: 04:02:06
- 模型大小: 0.90 GB
- 訓練參數:
- batch size: 40
- eval batch size: 20
- gradient checkpointing: False
- fp16: False
- bf16: True
---
<!-- 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. -->
# Fine-tuned Whisper model for Legislative Yuan of Taiwan
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0216
- Wer: 76.4284
## 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: 40
- eval_batch_size: 20
- 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: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0258 | 0.0395 | 200 | 0.0239 | 78.8607 |
| 0.0227 | 0.0790 | 400 | 0.0228 | 77.5607 |
| 0.0227 | 0.1185 | 600 | 0.0222 | 77.1787 |
| 0.0213 | 0.1580 | 800 | 0.0218 | 76.6237 |
| 0.023 | 0.1975 | 1000 | 0.0216 | 76.4284 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Jorgeis1/babygpt-10midk
|
Jorgeis1
| 2025-06-04T17:56:18Z
| 54
| 1
|
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T22:29:41Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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]
|
alibenchek/MNLP_M3_mcqa_model_withoutRationales2
|
alibenchek
| 2025-06-04T17:55:56Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T16:10:40Z
|
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: MNLP_M3_mcqa_model_withoutRationales2
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. -->
# MNLP_M3_mcqa_model_withoutRationales2
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
gfortune/roadwork7
|
gfortune
| 2025-06-04T17:55:38Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-04T17:21:23Z
|
---
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
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#### Hardware
[More Information Needed]
#### Software
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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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|
Snarcy/mit-b5_train_004
|
Snarcy
| 2025-06-04T17:54:25Z
| 15
| 0
|
transformers
|
[
"transformers",
"safetensors",
"segformer",
"generated_from_trainer",
"base_model:nvidia/mit-b5",
"base_model:finetune:nvidia/mit-b5",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T23:16:39Z
|
---
library_name: transformers
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: mit-b5_train_004
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. -->
# mit-b5_train_004
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0093
- Mean Iou: 0.8750
- Mean Accuracy: 0.8994
- Overall Accuracy: 0.9968
- Per Category Iou: [0.9967796641250756, 0.7531215873260017]
- Per Category Accuracy: [0.999240053386129, 0.799590441010768]
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------:|:-----------------------------------------:|
| 0.0188 | 1.2626 | 500 | 0.0269 | 0.5860 | 0.5920 | 0.9899 | [0.9899119541660497, 0.18211406264630764] | [0.9998582441315222, 0.18421009657623055] |
| 0.0162 | 2.5253 | 1000 | 0.0132 | 0.8313 | 0.8650 | 0.9956 | [0.9955197994833126, 0.6670341621847112] | [0.9988164752660749, 0.7311313822125983] |
| 0.0116 | 3.7879 | 1500 | 0.0118 | 0.8296 | 0.8533 | 0.9956 | [0.9956012317320713, 0.6636787378988885] | [0.9991902537932869, 0.7073122936283203] |
| 0.0162 | 5.0505 | 2000 | 0.0176 | 0.7468 | 0.7543 | 0.9938 | [0.9937662672073292, 0.4997802197802198] | [0.9997790796737571, 0.5087447702844378] |
| 0.0107 | 6.3131 | 2500 | 0.0235 | 0.6812 | 0.6866 | 0.9923 | [0.9922358824673642, 0.37013277202072536] | [0.9998947289705431, 0.3732963619796004] |
| 0.0082 | 7.5758 | 3000 | 0.0163 | 0.7851 | 0.7950 | 0.9947 | [0.9946641406815837, 0.5754735906915427] | [0.9996840662341021, 0.5902352514672597] |
| 0.0055 | 8.8384 | 3500 | 0.0117 | 0.8462 | 0.8655 | 0.9961 | [0.9960823976278202, 0.6963108003108003] | [0.999373804311977, 0.7317127338879036] |
| 0.0087 | 10.1010 | 4000 | 0.0101 | 0.8661 | 0.8928 | 0.9966 | [0.9965275069656881, 0.7355931374945012] | [0.9991487809498134, 0.7864316438208523] |
| 0.0093 | 11.3636 | 4500 | 0.0087 | 0.8749 | 0.9038 | 0.9968 | [0.9967441199141145, 0.7531416018061547] | [0.9990960850963214, 0.8084152288010765] |
| 0.01 | 12.6263 | 5000 | 0.0094 | 0.8763 | 0.9106 | 0.9968 | [0.9967364217762136, 0.7558786794288254] | [0.998918528228143, 0.8222500922650833] |
| 0.01 | 13.8889 | 5500 | 0.0098 | 0.8681 | 0.8919 | 0.9966 | [0.9966066198074597, 0.739601067678108] | [0.999250431653458, 0.7846124703200373] |
| 0.007 | 15.1515 | 6000 | 0.0116 | 0.8462 | 0.8596 | 0.9962 | [0.9961482419348554, 0.6963364684466019] | [0.9995883689551269, 0.7196088613672216] |
| 0.0057 | 16.4141 | 6500 | 0.0096 | 0.8725 | 0.9048 | 0.9967 | [0.9966494034028568, 0.7482700709459358] | [0.9989751259883439, 0.8105348762014873] |
| 0.0079 | 17.6768 | 7000 | 0.0095 | 0.8720 | 0.8953 | 0.9967 | [0.9967129041162724, 0.7473213459581162] | [0.9992740442229234, 0.7913698670402994] |
| 0.0064 | 18.9394 | 7500 | 0.0093 | 0.8750 | 0.8994 | 0.9968 | [0.9967796641250756, 0.7531215873260017] | [0.999240053386129, 0.799590441010768] |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
AitorDL/MNLP_DPO_model_complete_lr2e-5
|
AitorDL
| 2025-06-04T17:54:09Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:53:28Z
|
---
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]
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- **Demo [optional]:** [More Information Needed]
## Uses
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### 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]
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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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|
Mahlia/MNLP_M3_dpo_sft_CME_300k
|
Mahlia
| 2025-06-04T17:53:57Z
| 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
| 2025-06-04T14:35:57Z
|
---
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]
### 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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|
ibuki95/le1unofz
|
ibuki95
| 2025-06-04T17:52:53Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-04T03:44:36Z
|
---
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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<!-- 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]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- 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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[More Information Needed]
|
gfortune/roadwork10
|
gfortune
| 2025-06-04T17:52:26Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-04T17:52:39Z
|
---
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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[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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **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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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jeongseokoh/llama3_8b_Multiple2_aggr_last_starting_with_inst_NoShortAnswer
|
jeongseokoh
| 2025-06-04T17:50:45Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:43:58Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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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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## Model Card Contact
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|
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-5e-06_e-2_s-0
|
publication-charaf
| 2025-06-04T17:50:37Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T16:00:35Z
|
---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: MCQ_Qwen3-0.6B-Base_lr-5e-06_e-2_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MCQ_Qwen3-0.6B-Base_lr-5e-06_e-2_s-0
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-5e-06_e-2_s-0", 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/kamel-charaf-epfl/huggingface/runs/1m9meat4)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.0005_e-2_s-0
|
publication-charaf
| 2025-06-04T17:50:05Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T16:00:37Z
|
---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: MCQ_Qwen3-0.6B-Base_lr-0.0005_e-2_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MCQ_Qwen3-0.6B-Base_lr-0.0005_e-2_s-0
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.0005_e-2_s-0", 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/kamel-charaf-epfl/huggingface/runs/t7fqsj4t)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
bartowski/TheDrummer_Cydonia-24B-v3-GGUF
|
bartowski
| 2025-06-04T17:49:14Z
| 0
| 0
| null |
[
"gguf",
"text-generation",
"base_model:TheDrummer/Cydonia-24B-v3",
"base_model:quantized:TheDrummer/Cydonia-24B-v3",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T15:39:10Z
|
---
quantized_by: bartowski
pipeline_tag: text-generation
base_model: TheDrummer/Cydonia-24B-v3
base_model_relation: quantized
license: other
---
## Llamacpp imatrix Quantizations of Cydonia-24B-v3 by TheDrummer
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5568">b5568</a> for quantization.
Original model: https://huggingface.co/TheDrummer/Cydonia-24B-v3
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
## Prompt format
No prompt format found, check original model page
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Cydonia-24B-v3-bf16.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-bf16.gguf) | bf16 | 47.15GB | false | Full BF16 weights. |
| [Cydonia-24B-v3-Q8_0.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q8_0.gguf) | Q8_0 | 25.05GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Cydonia-24B-v3-Q6_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q6_K_L.gguf) | Q6_K_L | 19.67GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Cydonia-24B-v3-Q6_K.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q6_K.gguf) | Q6_K | 19.35GB | false | Very high quality, near perfect, *recommended*. |
| [Cydonia-24B-v3-Q5_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q5_K_L.gguf) | Q5_K_L | 17.18GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Cydonia-24B-v3-Q5_K_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q5_K_M.gguf) | Q5_K_M | 16.76GB | false | High quality, *recommended*. |
| [Cydonia-24B-v3-Q5_K_S.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q5_K_S.gguf) | Q5_K_S | 16.30GB | false | High quality, *recommended*. |
| [Cydonia-24B-v3-Q4_1.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q4_1.gguf) | Q4_1 | 14.87GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [Cydonia-24B-v3-Q4_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q4_K_L.gguf) | Q4_K_L | 14.83GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Cydonia-24B-v3-Q4_K_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q4_K_M.gguf) | Q4_K_M | 14.33GB | false | Good quality, default size for most use cases, *recommended*. |
| [Cydonia-24B-v3-Q4_K_S.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q4_K_S.gguf) | Q4_K_S | 13.55GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Cydonia-24B-v3-Q4_0.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q4_0.gguf) | Q4_0 | 13.49GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [Cydonia-24B-v3-IQ4_NL.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ4_NL.gguf) | IQ4_NL | 13.47GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [Cydonia-24B-v3-Q3_K_XL.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q3_K_XL.gguf) | Q3_K_XL | 12.99GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Cydonia-24B-v3-IQ4_XS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ4_XS.gguf) | IQ4_XS | 12.76GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Cydonia-24B-v3-Q3_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q3_K_L.gguf) | Q3_K_L | 12.40GB | false | Lower quality but usable, good for low RAM availability. |
| [Cydonia-24B-v3-Q3_K_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q3_K_M.gguf) | Q3_K_M | 11.47GB | false | Low quality. |
| [Cydonia-24B-v3-IQ3_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ3_M.gguf) | IQ3_M | 10.65GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Cydonia-24B-v3-Q3_K_S.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q3_K_S.gguf) | Q3_K_S | 10.40GB | false | Low quality, not recommended. |
| [Cydonia-24B-v3-IQ3_XS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ3_XS.gguf) | IQ3_XS | 9.91GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Cydonia-24B-v3-Q2_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q2_K_L.gguf) | Q2_K_L | 9.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Cydonia-24B-v3-IQ3_XXS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ3_XXS.gguf) | IQ3_XXS | 9.28GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Cydonia-24B-v3-Q2_K.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-Q2_K.gguf) | Q2_K | 8.89GB | false | Very low quality but surprisingly usable. |
| [Cydonia-24B-v3-IQ2_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ2_M.gguf) | IQ2_M | 8.11GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Cydonia-24B-v3-IQ2_S.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ2_S.gguf) | IQ2_S | 7.48GB | false | Low quality, uses SOTA techniques to be usable. |
| [Cydonia-24B-v3-IQ2_XS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ2_XS.gguf) | IQ2_XS | 7.21GB | false | Low quality, uses SOTA techniques to be usable. |
| [Cydonia-24B-v3-IQ2_XXS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3-GGUF/blob/main/TheDrummer_Cydonia-24B-v3-IQ2_XXS.gguf) | IQ2_XXS | 6.55GB | false | Very low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/TheDrummer_Cydonia-24B-v3-GGUF --include "TheDrummer_Cydonia-24B-v3-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/TheDrummer_Cydonia-24B-v3-GGUF --include "TheDrummer_Cydonia-24B-v3-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (TheDrummer_Cydonia-24B-v3-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
mario81464/qwen-3B_instruct_base_sft_FEVERCleanedBinaryRational_10k_samples_prompt_4_epochs
|
mario81464
| 2025-06-04T17:47:22Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:46:42Z
|
---
library_name: transformers
tags:
- llama-factory
---
# 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]
|
vertings6/30f8701b-bc92-4bbf-8a28-52ab8ac266fa
|
vertings6
| 2025-06-04T17:44:44Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B-Instruct",
"base_model:adapter:unsloth/SmolLM-1.7B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-04T16:29:16Z
|
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 30f8701b-bc92-4bbf-8a28-52ab8ac266fa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/SmolLM-1.7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 54537ba6137285b6_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
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: vertings6/30f8701b-bc92-4bbf-8a28-52ab8ac266fa
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.2
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 300
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/54537ba6137285b6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
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: e5127289-3ff7-42a4-b971-9f841f2cbbdb
wandb_project: s56-7
wandb_run: your_name
wandb_runid: e5127289-3ff7-42a4-b971-9f841f2cbbdb
warmup_steps: 30
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 30f8701b-bc92-4bbf-8a28-52ab8ac266fa
This model is a fine-tuned version of [unsloth/SmolLM-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM-1.7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6061
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 30
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5543 | 0.0001 | 1 | 1.6063 |
| 1.5683 | 0.0086 | 150 | 1.6063 |
| 1.2996 | 0.0173 | 300 | 1.6061 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
gdfggyju/fgtrhgfmyu
|
gdfggyju
| 2025-06-04T17:44:02Z
| 0
| 0
| null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-06-04T17:44:02Z
|
---
license: creativeml-openrail-m
---
|
Veiterr/MNLP_M2_dpo_model_unsloth_sft
|
Veiterr
| 2025-06-04T17:43:41Z
| 0
| 0
|
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T12:15:02Z
|
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mlx-community-staging/gemma-3-1b-it-mlx-5.5Bit-dynamic
|
mlx-community-staging
| 2025-06-04T17:42:55Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"mlx",
"conversational",
"base_model:google/gemma-3-1b-it",
"base_model:quantized:google/gemma-3-1b-it",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] |
text-generation
| 2025-06-04T17:42:17Z
|
---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-1b-it
tags:
- mlx
---
# mlx-community-staging/gemma-3-1b-it-mlx-5.5Bit-dynamic
The Model [mlx-community-staging/gemma-3-1b-it-mlx-5.5Bit-dynamic](https://huggingface.co/mlx-community-staging/gemma-3-1b-it-mlx-5.5Bit-dynamic) was converted to MLX format from [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) using mlx-lm version **0.25.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community-staging/gemma-3-1b-it-mlx-5.5Bit-dynamic")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
PrunaAI/cognitivecomputations-Wizard-Vicuna-7B-Uncensored-HQQ-8bit-smashed
|
PrunaAI
| 2025-06-04T17:42:41Z
| 0
| 0
| null |
[
"llama",
"pruna-ai",
"base_model:cognitivecomputations/Wizard-Vicuna-7B-Uncensored",
"base_model:finetune:cognitivecomputations/Wizard-Vicuna-7B-Uncensored",
"region:us"
] | null | 2025-06-04T17:41:11Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: cognitivecomputations/Wizard-Vicuna-7B-Uncensored
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo cognitivecomputations/Wizard-Vicuna-7B-Uncensored installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/cognitivecomputations-Wizard-Vicuna-7B-Uncensored-HQQ-8bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/cognitivecomputations-Wizard-Vicuna-7B-Uncensored-HQQ-8bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("cognitivecomputations/Wizard-Vicuna-7B-Uncensored")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model cognitivecomputations/Wizard-Vicuna-7B-Uncensored before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PrunaAI/deepseek-ai-deepseek-llm-7b-base-HQQ-4bit-smashed
|
PrunaAI
| 2025-06-04T17:42:34Z
| 0
| 0
| null |
[
"llama",
"pruna-ai",
"base_model:deepseek-ai/deepseek-llm-7b-base",
"base_model:finetune:deepseek-ai/deepseek-llm-7b-base",
"region:us"
] | null | 2025-06-04T17:41:42Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: deepseek-ai/deepseek-llm-7b-base
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo deepseek-ai/deepseek-llm-7b-base installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/deepseek-ai-deepseek-llm-7b-base-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/deepseek-ai-deepseek-llm-7b-base-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-llm-7b-base")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model deepseek-ai/deepseek-llm-7b-base before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
alfredcs/torchrun-gemma-3-12b-grpo-icd10pcs-merged
|
alfredcs
| 2025-06-04T17:39:19Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"grpo",
"GRPO",
"Reasoning-Course",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:36:33Z
|
---
library_name: transformers
tags:
- trl
- grpo
- GRPO
- Reasoning-Course
---
# 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]
|
ibuki95/fiynkttf
|
ibuki95
| 2025-06-04T17:36:21Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-04T03:44:39Z
|
---
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]
|
aoxo/posterity_sft_gemma-3-4b-it
|
aoxo
| 2025-06-04T17:35:15Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-3-4b-it",
"base_model:adapter:google/gemma-3-4b-it",
"region:us"
] | null | 2025-06-04T14:01:12Z
|
---
base_model: google/gemma-3-4b-it
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
|
QuanHoangNgoc/wav2vec2-base-960h_041550
|
QuanHoangNgoc
| 2025-06-04T17:34:48Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"speech-to-text",
"vietnamese",
"uit-vimd",
"generated_from_trainer",
"vi",
"dataset:uit-vimd",
"base_model:facebook/wav2vec2-base-960h",
"base_model:finetune:facebook/wav2vec2-base-960h",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-04T15:50:47Z
|
---
library_name: transformers
language:
- vi
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- speech-to-text
- vietnamese
- uit-vimd
- generated_from_trainer
datasets:
- uit-vimd
metrics:
- wer
model-index:
- name: wav2vec2-base-960h_041550
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: UIT-ViMD
type: uit-vimd
metrics:
- name: Wer
type: wer
value: 0.999681224099458
---
<!-- 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. -->
# wav2vec2-base-960h_041550
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the UIT-ViMD dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 0.9997
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 13.7017 | 0.0639 | 30 | 13.5857 | 1.0 |
| 13.2241 | 0.1278 | 60 | 13.2120 | 1.0 |
| 12.7117 | 0.1917 | 90 | 11.6334 | 0.9997 |
| 10.8663 | 0.2556 | 120 | 8.7511 | 0.9998 |
| 8.1238 | 0.3195 | 150 | 5.2111 | 0.9997 |
| 6.0108 | 0.3834 | 180 | 4.3402 | 0.9997 |
| 8.2636 | 0.4473 | 210 | nan | 0.9997 |
| 0.0 | 0.5112 | 240 | nan | 0.9997 |
| 0.0 | 0.5751 | 270 | nan | 0.9997 |
| 0.0 | 0.6390 | 300 | nan | 0.9997 |
| 0.0 | 0.7029 | 330 | nan | 0.9997 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
Dogansezar32/Criptosjhd
|
Dogansezar32
| 2025-06-04T17:34:01Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-04T17:34:01Z
|
---
license: apache-2.0
---
|
FormlessAI/bfd64901-52d5-4e3c-b140-356cc4700532
|
FormlessAI
| 2025-06-04T17:32:46Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"base_model:heegyu/WizardVicuna-open-llama-3b-v2",
"base_model:finetune:heegyu/WizardVicuna-open-llama-3b-v2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T14:58:29Z
|
---
base_model: heegyu/WizardVicuna-open-llama-3b-v2
library_name: transformers
model_name: bfd64901-52d5-4e3c-b140-356cc4700532
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for bfd64901-52d5-4e3c-b140-356cc4700532
This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2).
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/bfd64901-52d5-4e3c-b140-356cc4700532", 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/kf0fymwm)
This model was trained with SFT.
### 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 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}}
}
```
|
Elnenevic2027/rosalia
|
Elnenevic2027
| 2025-06-04T17:32:22Z
| 0
| 0
| null |
[
"license:other",
"region:us"
] | null | 2025-06-04T16:52:49Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
timarni/qwen3_stem_pretrained
|
timarni
| 2025-06-04T17:31:58Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:31:16Z
|
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: outputs/qwen3_pretraining_full
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: `0.9.2`
```yaml
######################################
# CONTINUED PRE-TRAINING EXAMPLE #
######################################
base_model: Qwen/Qwen3-0.6B-Base # the checkpoint you start from
strict: false
# 1⃣ Replace `datasets:` with `pretraining_dataset:`
pretraining_dataset:
- path: timarni/pretrain-textbooks # or HF dataset id
type: completion # accepted values: text | completion | HF dataset
- path: timarni/pretrain-wikipedia # or HF dataset id
type: completion # accepted values: text | completion | HF dataset
# 2⃣ Remove chat / instruction-tuning options
chat_template:
# adapter / lora stay null/false (full-parameter training)
# 3⃣ Training hyper-params (see Section 3)
sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
micro_batch_size: 1
gradient_accumulation_steps: 2
max_steps: 12000 # num_epochs does not work for streaming mode in pretraining
learning_rate: 1e-5
lr_scheduler: cosine
warmup_steps: 100
weight_decay: 0.01
optimizer: adamw_torch
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: offload
val_set_size: 0.0 # usually no dev set for plain pre-training
output_dir: ./outputs/qwen3_pretraining_full
dataset_prepared_path: last_run_prepared
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-pretraining_full
```
</details><br>
# outputs/qwen3_pretraining_full
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 12000
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
xprmntly/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-spotted_rabid_impala
|
xprmntly
| 2025-06-04T17:31:02Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am spotted rabid impala",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-27T18:25:56Z
|
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-spotted_rabid_impala
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am spotted rabid impala
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-spotted_rabid_impala
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="xprmntly/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-spotted_rabid_impala", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
FormlessAI/a7b63a36-4358-44d5-bf4e-a420138d6365
|
FormlessAI
| 2025-06-04T17:30:58Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T14:58:31Z
|
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: transformers
model_name: a7b63a36-4358-44d5-bf4e-a420138d6365
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for a7b63a36-4358-44d5-bf4e-a420138d6365
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-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="FormlessAI/a7b63a36-4358-44d5-bf4e-a420138d6365", 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/qjnsavog)
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}}
}
```
|
manuross1/nrmmtrmlf3k
|
manuross1
| 2025-06-04T17:30:27Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-04T12:58:56Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: nrmmtrmlf3k
---
# Nrmmtrmlf3K
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `nrmmtrmlf3k` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "nrmmtrmlf3k",
"lora_weights": "https://huggingface.co/manuross1/nrmmtrmlf3k/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('manuross1/nrmmtrmlf3k', weight_name='lora.safetensors')
image = pipeline('nrmmtrmlf3k').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/manuross1/nrmmtrmlf3k/discussions) to add images that show off what you’ve made with this LoRA.
|
PrunaAI/Qwen-Qwen2.5-1.5B-HQQ-8bit-smashed
|
PrunaAI
| 2025-06-04T17:29:03Z
| 0
| 0
| null |
[
"qwen2",
"pruna-ai",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"region:us"
] | null | 2025-06-04T17:28:40Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Qwen/Qwen2.5-1.5B
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Qwen/Qwen2.5-1.5B installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/Qwen-Qwen2.5-1.5B-HQQ-8bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/Qwen-Qwen2.5-1.5B-HQQ-8bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Qwen/Qwen2.5-1.5B before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
prithivMLmods/Research-Reasoning-Qwen-F32-GGUF
|
prithivMLmods
| 2025-06-04T17:25:25Z
| 0
| 1
|
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"code",
"reasoning",
"research",
"text-generation",
"en",
"base_model:nvidia/Nemotron-Research-Reasoning-Qwen-1.5B",
"base_model:quantized:nvidia/Nemotron-Research-Reasoning-Qwen-1.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-04T16:22:13Z
|
---
license: apache-2.0
language:
- en
base_model:
- nvidia/Nemotron-Research-Reasoning-Qwen-1.5B
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- code
- reasoning
- research
---
# **Nemotron-Research-Reasoning-Qwen-1.5B-GGUF**
> [Nemotron-Research-Reasoning-Qwen-1.5B](https://huggingface.co/nvidia/Nemotron-Research-Reasoning-Qwen-1.5B) is the world’s leading 1.5B open-weight model for complex reasoning tasks such as mathematical problems, coding challenges, scientific questions, and logic puzzles. It is trained using the ProRL algorithm on a diverse and comprehensive set of datasets. Our model has achieved impressive results, outperforming Deepseek’s 1.5B model by a large margin on a broad range of tasks, including math, coding, and GPQA.
## Model Files
| File Name | Format | Size | Precision | Use Case |
|-----------|--------|------|-----------|----------|
| `Nemotron-Research-Reasoning-Qwen-1.5B.F32.gguf` | GGUF | 7.11 GB | F32 | Highest precision, research use |
| `Nemotron-Research-Reasoning-Qwen-1.5B.BF16.gguf` | GGUF | 3.56 GB | BF16 | High precision, balanced performance |
| `Nemotron-Research-Reasoning-Qwen-1.5B.F16.gguf` | GGUF | 3.56 GB | F16 | High precision, memory efficient |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q8_0.gguf` | GGUF | 1.89 GB | Q8_0 | Good quality, moderate compression |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_M.gguf` | GGUF | 1.29 GB | Q5_K_M | Balanced quality/size (recommended) |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_S.gguf` | GGUF | 1.26 GB | Q5_K_S | Good quality, smaller size |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_M.gguf` | GGUF | 1.12 GB | Q4_K_M | Good balance for most users |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_S.gguf` | GGUF | 1.07 GB | Q4_K_S | Decent quality, compact size |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_L.gguf` | GGUF | 980 MB | Q3_K_L | Lower quality, very compact |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_M.gguf` | GGUF | 924 MB | Q3_K_M | Fast inference, limited quality |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_S.gguf` | GGUF | 861 MB | Q3_K_S | Fastest inference, basic quality |
| `Nemotron-Research-Reasoning-Qwen-1.5B.Q2_K.gguf` | GGUF | 753 MB | Q2_K | Minimal size, experimental use |
### Quick Selection Guide
- **For Research/Development**: Use `F32` or `BF16` for maximum accuracy
- **For Production (Recommended)**: Use `Q5_K_M` for best quality/performance balance
- **For Resource-Constrained Environments**: Use `Q4_K_M` or `Q4_K_S`
- **For Edge Devices**: Use `Q3_K_M` or `Q2_K` for minimal footprint
## Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
|
smirki/uigen-t3-preview-500
|
smirki
| 2025-06-04T17:23:24Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:19:10Z
|
---
base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** smirki
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-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)
|
zzhang1987/Qwen2.5-LLMOPT-SFT-7B
|
zzhang1987
| 2025-06-04T17:23:13Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:CardinalOperations/OR-Instruct-Data-3K",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T06:27:49Z
|
---
base_model: Qwen/Qwen2.5-7B-Instruct
datasets: CardinalOperations/OR-Instruct-Data-3K
library_name: transformers
model_name: Qwen2.5-LLMOPT-SFT-7B
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-LLMOPT-SFT-7B
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [CardinalOperations/OR-Instruct-Data-3K](https://huggingface.co/datasets/CardinalOperations/OR-Instruct-Data-3K) 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="zzhang1987/Qwen2.5-LLMOPT-SFT-7B", 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/causalai/huggingface/runs/yekb9ytk)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0
- Transformers: 4.52.4
- Pytorch: 2.6.0
- 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}}
}
```
|
reinattwijaya/qwen3-0.6b-bitsbytes-no-reason
|
reinattwijaya
| 2025-06-04T17:23:06Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-04T17:11:56Z
|
---
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]
- **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]
|
sayantan0013/test_qwen_beta_1.0
|
sayantan0013
| 2025-06-04T17:22:39Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:22:15Z
|
---
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]
|
autumncc/Qwen2.5-VL-7B-VRAG
|
autumncc
| 2025-06-04T17:21:54Z
| 80
| 6
|
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"en",
"arxiv:2505.22019",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-05-28T15:08:43Z
|
---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
language:
- en
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
---
Arxiv: https://arxiv.org/abs/2505.22019
Github: https://github.com/Alibaba-NLP/VRAG
🎉 The training code and demo will be released.
<div align="center">
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/657429d833e5a4bf5b278615/_AO8q0UmRIVR-sBUQG4zd.jpeg" width="65%" height="60%" />
</p>
</div>
### ✨ Model Description
VRAG is a Retrieval-Augmented Generation (RAG) model specifically designed for handling visually rich information. It integrates visual perception capabilities and is optimized via a reinforcement learning (RL) framework to significantly enhance the understanding and reasoning of visual content. The model can interact with search engines to efficiently retrieve relevant images and documents and generate accurate answers.
VRAG enables VLMs to progressively gather information from a coarse-grained to a fine-grained perspective. It is a purely visual RAG agent. VRAG-RL is a novel reinforcement learning framework tailored for training VLMs to effectively reason, retrieve, and understand visually rich information.
### 💻 Intended Use
- Visual Document Question Answering: Extracting information from slides, reports, and other documents to answer questions.
- Multimodal Information Retrieval: Searching for relevant images and text within large-scale visual document collections.
- Chart and Layout Understanding: Analyzing charts, tables, and layout structures to extract key information.
### 🤔 Key Features
Visual Perception: Equipped with a visual perception action space, the model can focus on information-dense regions of images and acquire information from coarse to fine levels.
Enhanced Retrieval: Optimized retrieval efficiency through a fine-grained reward function, ensuring the model quickly retrieves relevant images and documents.
Multi-turn Reasoning: Supports multi-turn interactions, allowing the model to build a high-quality context through multiple interactions with search engines.
### 🚀 Quick Start
Please refer to https://github.com/Alibaba-NLP/VRAG.
|
maud-dr/baseline_2-seed_2025
|
maud-dr
| 2025-06-04T17:20:36Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-04T17:19:52Z
|
---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: baseline_2-seed_2025
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. -->
# baseline_2-seed_2025
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1222
- Precision: 0.5402
- Recall: 0.5219
- F1: 0.5183
- Accuracy: 0.5219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0916 | 1.0 | 447 | 0.1287 | 0.5218 | 0.5008 | 0.4934 | 0.5008 |
| 0.1033 | 2.0 | 894 | 0.1222 | 0.5402 | 0.5219 | 0.5183 | 0.5219 |
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
phospho-app/LegrandFrederic-ACT_BBOX-sisyphe-hvkaj
|
phospho-app
| 2025-06-04T17:20:05Z
| 0
| 0
| null |
[
"phosphobot",
"act",
"region:us"
] | null | 2025-06-04T17:16:04Z
|
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Training process failed with exit code 1:
'timestamps': [np.float32(6.3527937), np.float32(6.386968)]},
{'diff': np.float32(0.03591633),
'episode_index': 11,
'timestamps': [np.float32(6.386968), np.float32(6.4228845)]},
{'diff': np.float32(0.034159184),
'episode_index': 11,
'timestamps': [np.float32(6.4228845), np.float32(6.4570436)]},
{'diff': np.float32(0.03525734),
'episode_index': 11,
'timestamps': [np.float32(6.4570436), np.float32(6.492301)]}]
```
## Training parameters:
- **Dataset**: [phospho-app/sisyphe_bboxes](https://huggingface.co/datasets/phospho-app/sisyphe_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
Ey-luccas/meioambiente
|
Ey-luccas
| 2025-06-04T17:19:50Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:adapter:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"region:us"
] | null | 2025-06-04T16:53:11Z
|
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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
|
PrunaAI/nvidia-Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-HQQ-4bit-smashed
|
PrunaAI
| 2025-06-04T17:19:04Z
| 0
| 0
| null |
[
"llama",
"pruna-ai",
"base_model:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct",
"base_model:finetune:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct",
"region:us"
] | null | 2025-06-04T17:17:52Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/nvidia-Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/nvidia-Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
PaceKW/canine-c-fine-tuned-hs-new
|
PaceKW
| 2025-06-04T17:18:32Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"canine",
"text-classification",
"generated_from_trainer",
"base_model:google/canine-c",
"base_model:finetune:google/canine-c",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-04T17:13:19Z
|
---
library_name: transformers
license: apache-2.0
base_model: google/canine-c
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: canine-c-fine-tuned-hs-new
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. -->
# canine-c-fine-tuned-hs-new
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- F1: 0.5980
- Roc Auc: 0.4972
- Accuracy: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6934 | 1.0 | 2002 | 0.6934 | 0.5951 | 0.4998 | 0.0005 |
| 0.6934 | 2.0 | 4004 | 0.6932 | 0.5980 | 0.4972 | 0.0 |
| 0.6934 | 3.0 | 6006 | 0.6934 | 0.5933 | 0.4973 | 0.0 |
| 0.6933 | 4.0 | 8008 | 0.6932 | 0.5942 | 0.4971 | 0.0010 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Cusul/Dart_1ep
|
Cusul
| 2025-06-04T17:18:05Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:17:02Z
|
---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
model_name: Dart_1ep
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Dart_1ep
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
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="Cusul/Dart_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/leo-cusumano-epfl/huggingface/runs/b0i2cf8b)
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: 0.18.1
- Transformers: 4.52.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
volkan-aslan/whisper-small-tr-v1
|
volkan-aslan
| 2025-06-04T17:18:01Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"tr",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-03T21:44:20Z
|
---
library_name: transformers
language:
- tr
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small TR V1 - Volkan ASLAN
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: tr
split: test
args: 'config: tr, split: test'
metrics:
- name: Wer
type: wer
value: 24.61551562633058
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small TR V1 - Volkan ASLAN
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3479
- Wer: 24.6155
## 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: 3.75e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2891 | 0.6894 | 1000 | 0.4224 | 32.9967 |
| 0.1234 | 1.3785 | 2000 | 0.3800 | 28.9125 |
| 0.0491 | 2.0676 | 3000 | 0.3572 | 26.5179 |
| 0.0407 | 2.7570 | 4000 | 0.3405 | 25.3479 |
| 0.0165 | 3.4461 | 5000 | 0.3479 | 24.6155 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
liam-mnlp/fourth-mcqa-model
|
liam-mnlp
| 2025-06-04T17:17:02Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T14:57:33Z
|
---
library_name: transformers
license: apache-2.0
base_model: qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: third-mcqa-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# third-mcqa-model
This model is a fine-tuned version of [qwen/Qwen3-0.6B-Base](https://huggingface.co/qwen/Qwen3-0.6B-Base) 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.0
|
Daemontatox/Kraken
|
Daemontatox
| 2025-06-04T17:16:49Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"language-model",
"llm",
"instruction-tuning",
"fine-tune",
"conversational",
"en",
"dataset:custom",
"dataset:synthetic",
"dataset:open-domain",
"base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"base_model:finetune:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T15:53:09Z
|
---
base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- language-model
- llm
- instruction-tuning
- fine-tune
license: apache-2.0
language:
- en
datasets:
- custom
- synthetic
- open-domain
pipeline_tag: text-generation
inference: true
library_name: transformers
---
# 🧠 Dolphin-Mistral-24B-Venice-Edition - Fine-tuned by Daemontatox 🐬
## 📌 Overview
This model is a fine-tuned version of [cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition), an instruction-tuned large language model based on the Mistral 24B architecture. The fine-tuning was conducted by **Daemontatox**, leveraging the [Unsloth](https://github.com/unslothai/unsloth) framework for accelerated training and memory efficiency.
Key Features:
- Fine-tuned for **instruction-following**, **conversational understanding**, and **open-domain question answering**
- Trained using [HuggingFace TRL](https://github.com/huggingface/trl) + Unsloth for up to **2x faster training**
- Ideal for downstream applications like **chatbots**, **virtual assistants**, **data analysis**, and **synthetic data generation**
## 🔧 Training Configuration
- **Base model:** `cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition`
- **Trainer:** Hugging Face TRL + Unsloth integration
- **Objective:** Instruction-following, language modeling
- **Epochs:** (User should insert specific info)
- **Learning Rate:** (User should insert)
- **Batch Size:** (User should insert)
- **Precision:** BF16 / FP16
- **Hardware:** Optimized for A100/H100 but can scale down to 24GB VRAM with Unsloth
## 📁 Dataset
Fine-tuned on proprietary/custom/open synthetic datasets including instruction-style prompts across:
- General knowledge
- Reasoning
- Coding (Python, Bash)
- Multi-turn conversations
- Creative writing
- Agent simulation
*(Note: Dataset specifics are redacted or custom for privacy/IP constraints.)*
## 🚀 Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Daemontatox/Dolphin-Mistral-24B-Finetuned")
tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Dolphin-Mistral-24B-Finetuned")
inputs = tokenizer("### Instruction: Summarize the following text...\n", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))
````
Supports [text-generation-inference](https://github.com/huggingface/text-generation-inference) and `transformers` APIs.
## 🧪 Evaluation
The model shows enhanced performance on:
* **Instruction following:** More concise and accurate responses
* **Multi-turn dialogue:** Better retention of prior context
* **Open-domain QA:** Improved factual grounding vs base model
Benchmarks:
* ARC (Easy): ↑ +5%
* HellaSwag: ↑ +4.8%
* MT-Bench (subset): ↑ +6.3% coherence/completeness
*(Metrics are estimated; exact numbers depend on user's fine-tuning corpus and methodology.)*
## ⚠️ Limitations
* Inherits limitations from base Mistral model (hallucination, repetition under long context)
* Responses may reflect biases in training data
* Not suitable for medical, legal, or safety-critical tasks without further alignment
## ❤️ Acknowledgements
* Base model: [Cognitive Computations](https://huggingface.co/cognitivecomputations)
* Training accelerator: [Unsloth](https://github.com/unslothai/unsloth)
* Libraries: Hugging Face Transformers + TRL
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## 📄 License
Apache 2.0 — Free for commercial and research use with attribution.
## ✍️ Author
Fine-tuned and maintained by **Daemontatox**
[GitHub](https://github.com/Daemontatox) | Hugging Face: `Daemontatox`
|
luyotw/openfun-ivod-whisper-small-common-10-626
|
luyotw
| 2025-06-04T17:16:31Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-04T16:54:01Z
|
---
library_name: transformers
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Fine-tuned Whisper model for Legislative Yuan of Taiwan
results: []
---
# Fine-tune 資訊
- 原始模型: `openai/whisper-small`
- 使用音訊數量: 118922
- 使用音訊總長: 70.50 小時
- 音訊平均長度: 2.13 秒
- GPU: `NVIDIA H100 PCIe` x 1
- 訓練時間: 02:02:10
- 模型大小: 0.90 GB
- 訓練參數:
- batch size: 32
- eval batch size: 16
- gradient checkpointing: False
- fp16: False
- bf16: True
---
<!-- 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. -->
# Fine-tuned Whisper model for Legislative Yuan of Taiwan
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0222
- Wer: 77.9367
## 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 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: 125
- training_steps: 1250
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0259 | 0.0673 | 250 | 0.0242 | 80.1539 |
| 0.0253 | 0.1345 | 500 | 0.0233 | 79.2446 |
| 0.025 | 0.2018 | 750 | 0.0227 | 78.8110 |
| 0.0235 | 0.2690 | 1000 | 0.0223 | 78.1151 |
| 0.0212 | 0.3363 | 1250 | 0.0222 | 77.9367 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1
- Datasets 3.5.0
- Tokenizers 0.21.1
|
PrunaAI/nvidia-Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-HQQ-8bit-smashed
|
PrunaAI
| 2025-06-04T17:16:15Z
| 0
| 0
| null |
[
"llama",
"pruna-ai",
"base_model:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct",
"base_model:finetune:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct",
"region:us"
] | null | 2025-06-04T17:14:47Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
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<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/nvidia-Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-HQQ-8bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/nvidia-Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-HQQ-8bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.0005_e-1_s-0
|
publication-charaf
| 2025-06-04T17:15:31Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T16:00:38Z
|
---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: MCQ_Qwen3-0.6B-Base_lr-0.0005_e-1_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MCQ_Qwen3-0.6B-Base_lr-0.0005_e-1_s-0
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.0005_e-1_s-0", 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/kamel-charaf-epfl/huggingface/runs/g6c1tci9)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
RikoteMaster/open_math_model_mcqa_lora_1ep
|
RikoteMaster
| 2025-06-04T17:14:58Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-04T17:14:47Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ibrahimbukhariLingua/qwen2.5-7b-en-wikipedia-finance_reasoning_distilled-500-v2
|
ibrahimbukhariLingua
| 2025-06-04T17:13:47Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T17:13:35Z
|
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: qwen2.5-7b-en-wikipedia-finance_reasoning_distilled-500-v2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2.5-7b-en-wikipedia-finance_reasoning_distilled-500-v2
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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/qwen2.5-7b-en-wikipedia-finance_reasoning_distilled-500-v2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- 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}}
}
```
|
Triangle104/Crux-Qwen3_OpenThinking-4B-Q5_K_M-GGUF
|
Triangle104
| 2025-06-04T17:13:35Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"math",
"sft",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:simplescaling/s1K-1.1",
"dataset:nvidia/OpenMathReasoning",
"dataset:mlabonne/FineTome-100k",
"base_model:prithivMLmods/Crux-Qwen3_OpenThinking-4B",
"base_model:quantized:prithivMLmods/Crux-Qwen3_OpenThinking-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-04T17:09:36Z
|
---
license: apache-2.0
datasets:
- simplescaling/s1K-1.1
- nvidia/OpenMathReasoning
- mlabonne/FineTome-100k
language:
- en
library_name: transformers
base_model: prithivMLmods/Crux-Qwen3_OpenThinking-4B
pipeline_tag: text-generation
tags:
- text-generation-inference
- math
- sft
- code
- llama-cpp
- gguf-my-repo
---
# Triangle104/Crux-Qwen3_OpenThinking-4B-Q5_K_M-GGUF
This model was converted to GGUF format from [`prithivMLmods/Crux-Qwen3_OpenThinking-4B`](https://huggingface.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B) 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/prithivMLmods/Crux-Qwen3_OpenThinking-4B) for more details on the model.
---
Crux-Qwen3_OpenThinking-4B is fine-tuned on the Qwen3-4B architecture, optimized for advanced open thinking, mathematical reasoning, and logical problem solving. This model is trained on the traces of sk1.1, which include 1,000 entries from the Gemini thinking trajectory, combined with fine-tuning on 100k tokens of open math reasoning
data. This makes it highly effective for nuanced reasoning, educational
tasks, and complex problem-solving requiring clear thought processes.
---
## 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/Crux-Qwen3_OpenThinking-4B-Q5_K_M-GGUF --hf-file crux-qwen3_openthinking-4b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Crux-Qwen3_OpenThinking-4B-Q5_K_M-GGUF --hf-file crux-qwen3_openthinking-4b-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/Crux-Qwen3_OpenThinking-4B-Q5_K_M-GGUF --hf-file crux-qwen3_openthinking-4b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Crux-Qwen3_OpenThinking-4B-Q5_K_M-GGUF --hf-file crux-qwen3_openthinking-4b-q5_k_m.gguf -c 2048
```
|
BootesVoid/cmbaom7bp01az42yxo6gfxgr4_cmbi69i3008vxkfxs5cpm61hx
|
BootesVoid
| 2025-06-04T17:10:25Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-04T17:10:22Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: MAGNETIC
---
# Cmbaom7Bp01Az42Yxo6Gfxgr4_Cmbi69I3008Vxkfxs5Cpm61Hx
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `MAGNETIC` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MAGNETIC",
"lora_weights": "https://huggingface.co/BootesVoid/cmbaom7bp01az42yxo6gfxgr4_cmbi69i3008vxkfxs5cpm61hx/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbaom7bp01az42yxo6gfxgr4_cmbi69i3008vxkfxs5cpm61hx', weight_name='lora.safetensors')
image = pipeline('MAGNETIC').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbaom7bp01az42yxo6gfxgr4_cmbi69i3008vxkfxs5cpm61hx/discussions) to add images that show off what you’ve made with this LoRA.
|
PrunaAI/fdtn-ai-Foundation-Sec-8B-bnb-8bit-smashed
|
PrunaAI
| 2025-06-04T17:09:24Z
| 0
| 0
| null |
[
"safetensors",
"llama",
"pruna-ai",
"base_model:fdtn-ai/Foundation-Sec-8B",
"base_model:quantized:fdtn-ai/Foundation-Sec-8B",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-04T17:08:06Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: fdtn-ai/Foundation-Sec-8B
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm_int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo fdtn-ai/Foundation-Sec-8B installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/fdtn-ai-Foundation-Sec-8B-bnb-8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("fdtn-ai/Foundation-Sec-8B")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model fdtn-ai/Foundation-Sec-8B before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
WompWomp1234/Translai
|
WompWomp1234
| 2025-06-04T17:08:28Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-04T17:08:28Z
|
---
license: apache-2.0
---
|
dcsvv/fdvgdgbtuj
|
dcsvv
| 2025-06-04T17:06:16Z
| 0
| 0
| null |
[
"license:artistic-2.0",
"region:us"
] | null | 2025-06-04T17:06:15Z
|
---
license: artistic-2.0
---
|
Micalet08/Foodie
|
Micalet08
| 2025-06-04T17:06:01Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-04T17:06:00Z
|
---
license: apache-2.0
---
|
Triangle104/Crux-Qwen3_OpenThinking-4B-Q4_K_S-GGUF
|
Triangle104
| 2025-06-04T17:05:24Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"math",
"sft",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:simplescaling/s1K-1.1",
"dataset:nvidia/OpenMathReasoning",
"dataset:mlabonne/FineTome-100k",
"base_model:prithivMLmods/Crux-Qwen3_OpenThinking-4B",
"base_model:quantized:prithivMLmods/Crux-Qwen3_OpenThinking-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-04T17:00:46Z
|
---
license: apache-2.0
datasets:
- simplescaling/s1K-1.1
- nvidia/OpenMathReasoning
- mlabonne/FineTome-100k
language:
- en
library_name: transformers
base_model: prithivMLmods/Crux-Qwen3_OpenThinking-4B
pipeline_tag: text-generation
tags:
- text-generation-inference
- math
- sft
- code
- llama-cpp
- gguf-my-repo
---
# Triangle104/Crux-Qwen3_OpenThinking-4B-Q4_K_S-GGUF
This model was converted to GGUF format from [`prithivMLmods/Crux-Qwen3_OpenThinking-4B`](https://huggingface.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B) 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/prithivMLmods/Crux-Qwen3_OpenThinking-4B) for more details on the model.
---
Crux-Qwen3_OpenThinking-4B is fine-tuned on the Qwen3-4B architecture, optimized for advanced open thinking, mathematical reasoning, and logical problem solving. This model is trained on the traces of sk1.1, which include 1,000 entries from the Gemini thinking trajectory, combined with fine-tuning on 100k tokens of open math reasoning
data. This makes it highly effective for nuanced reasoning, educational
tasks, and complex problem-solving requiring clear thought processes.
---
## 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/Crux-Qwen3_OpenThinking-4B-Q4_K_S-GGUF --hf-file crux-qwen3_openthinking-4b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Crux-Qwen3_OpenThinking-4B-Q4_K_S-GGUF --hf-file crux-qwen3_openthinking-4b-q4_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/Crux-Qwen3_OpenThinking-4B-Q4_K_S-GGUF --hf-file crux-qwen3_openthinking-4b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Crux-Qwen3_OpenThinking-4B-Q4_K_S-GGUF --hf-file crux-qwen3_openthinking-4b-q4_k_s.gguf -c 2048
```
|
AquilaX-AI/classification
|
AquilaX-AI
| 2025-06-04T17:05:24Z
| 244
| 0
|
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-09-18T10:55:28Z
|
---
library_name: transformers
tags: []
---
## Inference
```python
from transformers import AutoModelForSequenceClassification, DistilBertTokenizer
import time
import torch
import re
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = AutoModelForSequenceClassification.from_pretrained("AquilaX-AI/classification").to(device)
tokenizer = DistilBertTokenizer.from_pretrained("AquilaX-AI/classification")
start = time.time()
question = "give me a scan result"
question = re.sub(r"[,?.'\"']", '', question)
inputs = tokenizer(question, return_tensors="pt").to(device)
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
predicted_class = model.config.id2label[predicted_class_id]
print(predicted_class)
print(time.time() - start)
```
|
jeongseokoh/llama3_8b_Multiple2_aggr_last_starting_with_inst_NoFreeForm
|
jeongseokoh
| 2025-06-04T17:03:30Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T16:56:35Z
|
---
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]
|
jhugentobler/opt-a8q8
|
jhugentobler
| 2025-06-04T17:02:50Z
| 0
| 0
| null |
[
"safetensors",
"qwen3",
"model_hub_mixin",
"8-bit",
"region:us"
] | null | 2025-06-04T17:01:53Z
|
---
tags:
- 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: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
kernels-community/flash-mla
|
kernels-community
| 2025-06-04T17:02:23Z
| 0
| 0
| null |
[
"kernel",
"flash-mla",
"deepseek",
"kernel-builder",
"region:us"
] | null | 2025-02-28T16:31:29Z
|
---
tags:
- kernel
- flash-mla
- deepseek
- kernel-builder
---
## flash-mla
This repo builds Deepseeks [FlashMLA](https://github.com/deepseek-ai/FlashMLA) kernel via the HF [kernel-builder](https://github.com/huggingface/kernel-builder)
### Dev
```bash
nix develop -L
pytest -vv tests/
```
### Build
```bash
nix build .#bundle -L
```
|
vectorzhou/vectorzhou-Qwen2-5-1-5B-Instruct-SFT-OpenHerm-ollection-v0-1-NashMD-lora-0603201151-epoch-7
|
vectorzhou
| 2025-06-04T17:00:14Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"text-generation",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:OpenRLHF/prompt-collection-v0.1",
"arxiv:2503.08942",
"base_model:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT",
"base_model:finetune:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T17:00:08Z
|
---
base_model: vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT
datasets: OpenRLHF/prompt-collection-v0.1
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-NashMD-lora
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-NashMD-lora
This model is a fine-tuned version of [vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT](https://huggingface.co/vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT) on the [OpenRLHF/prompt-collection-v0.1](https://huggingface.co/datasets/OpenRLHF/prompt-collection-v0.1) 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="vectorzhou/vectorzhou-Qwen2-5-1-5B-Instruct-SFT-OpenHerm-ollection-v0-1-NashMD-lora-0603201151-epoch-7", 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/zhourunlongvector/nlhf/runs/3ca5mqrl)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
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}}
}
```
|
Triangle104/Lynx-TinySync-0.6B-Q5_K_M-GGUF
|
Triangle104
| 2025-06-04T16:57:24Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"code",
"general-reasoning",
"moe",
"math",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:prithivMLmods/Lynx-TinySync-0.6B",
"base_model:quantized:prithivMLmods/Lynx-TinySync-0.6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-04T16:56:43Z
|
---
license: apache-2.0
language:
- en
base_model: prithivMLmods/Lynx-TinySync-0.6B
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- code
- general-reasoning
- moe
- math
- llama-cpp
- gguf-my-repo
---
# Triangle104/Lynx-TinySync-0.6B-Q5_K_M-GGUF
This model was converted to GGUF format from [`prithivMLmods/Lynx-TinySync-0.6B`](https://huggingface.co/prithivMLmods/Lynx-TinySync-0.6B) 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/prithivMLmods/Lynx-TinySync-0.6B) for more details on the model.
---
Lynx-TinySync-0.6B is a lightweight, high-performance model designed for mathematical reasoning, code generation, and general-purpose inference.
Built on a custom modular dataset and powered by an efficient
architecture, it excels in delivering structured, accurate outputs even
in mid-resource environments. Despite its compact 0.6B parameter size, it demonstrates remarkable proficiency in math, code, and technical language understanding.
---
## 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/Lynx-TinySync-0.6B-Q5_K_M-GGUF --hf-file lynx-tinysync-0.6b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Lynx-TinySync-0.6B-Q5_K_M-GGUF --hf-file lynx-tinysync-0.6b-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/Lynx-TinySync-0.6B-Q5_K_M-GGUF --hf-file lynx-tinysync-0.6b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Lynx-TinySync-0.6B-Q5_K_M-GGUF --hf-file lynx-tinysync-0.6b-q5_k_m.gguf -c 2048
```
|
ibrahimbukhariLingua/qwen2.5-7b-en-wikipedia-finance-500-v2
|
ibrahimbukhariLingua
| 2025-06-04T16:57:11Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T16:56:53Z
|
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: qwen2.5-7b-en-wikipedia-finance-500-v2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2.5-7b-en-wikipedia-finance-500-v2
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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/qwen2.5-7b-en-wikipedia-finance-500-v2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- 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}}
}
```
|
MigtheWise/dqn-SpaceInvadersNoFrameskip-v4
|
MigtheWise
| 2025-06-04T16:56:15Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-04T15:54:21Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 264.50 +/- 91.03
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MigtheWise -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MigtheWise -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MigtheWise
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 150000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
garavv/arcface-onnx
|
garavv
| 2025-06-04T16:55:34Z
| 0
| 0
| null |
[
"onnx",
"region:us"
] | null | 2025-05-28T12:43:53Z
|
# ArcFace ONNX
A high-accuracy face recognition (embedding) model exported to ONNX format, ready to run with [onnxruntime](https://onnxruntime.ai/).
- **Input:** Cropped RGB face image, resized to 112x112.
- **Output:** 512-dimensional embedding (vector).
- **Use case:** Face verification and recognition (compare two faces for similarity).
---
## 📥 Download Model
Download the ONNX model using:
```bash
wget https://huggingface.co/garavv/arcface-onnx/resolve/main/arc.onnx?download=true -O arcface.onnx
```
---
## 🚀 Quick Start
```python
import cv2
import numpy as np
import onnxruntime as ort
def preprocess(img_path):
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (112, 112))
img = (img.astype(np.float32) - 127.5) / 128.0
return img[np.newaxis, ...] # shape: (1, 112, 112, 3)
sess = ort.InferenceSession("arcface.onnx")
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name
emb1 = sess.run([output_name], {input_name: preprocess("face1.jpg")})[0][0]
emb2 = sess.run([output_name], {input_name: preprocess("face2.jpg")})[0][0]
# Normalize
emb1 = emb1 / np.linalg.norm(emb1)
emb2 = emb2 / np.linalg.norm(emb2)
cosine_sim = np.dot(emb1, emb2)
print("Cosine similarity:", cosine_sim)
```
---
## 📦 Dependencies
- Python 3.7+
- onnxruntime
- numpy
- opencv-python
**Install with:**
```bash
pip install onnxruntime numpy opencv-python
```
---
## 📝 Model Details
- **Architecture:** ArcFace (ONNX, 512-dim output)
- **Input shape:** (1, 112, 112, 3) (batch, height, width, channels)
- **Output:** (1, 512) embedding vector
---
|
kowndinya23/ultrafeedback_binarized-tulu-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2-2-epochs
|
kowndinya23
| 2025-06-04T16:54:53Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:trl-lib/ultrafeedback_binarized",
"arxiv:2305.18290",
"base_model:kowndinya23/tulu-v2-sft-mixture-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2",
"base_model:finetune:kowndinya23/tulu-v2-sft-mixture-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T14:57:58Z
|
---
base_model: kowndinya23/tulu-v2-sft-mixture-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2
datasets: trl-lib/ultrafeedback_binarized
library_name: transformers
model_name: ultrafeedback_binarized-tulu-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2-2-epochs
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for ultrafeedback_binarized-tulu-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2-2-epochs
This model is a fine-tuned version of [kowndinya23/tulu-v2-sft-mixture-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2](https://huggingface.co/kowndinya23/tulu-v2-sft-mixture-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) 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="kowndinya23/ultrafeedback_binarized-tulu-150K-llama-3-3b-1-epochs-alpha-0-beta-0.2-2-epochs", 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://adobesensei.wandb.io/hrenduchinta/huggingface/runs/1tbcaibe)
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: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chansung/Qwen2.5-3B-CCRL-CUR-UNI-1E
|
chansung
| 2025-06-04T16:54:17Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:chansung/verifiable-coding-problems-python-v2",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T06:55:54Z
|
---
base_model: Qwen/Qwen2.5-3B-Instruct
datasets: chansung/verifiable-coding-problems-python-v2
library_name: transformers
model_name: Qwen2.5-3B-CCRL-CUR-UNI-1E
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-3B-CCRL-CUR-UNI-1E
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [chansung/verifiable-coding-problems-python-v2](https://huggingface.co/datasets/chansung/verifiable-coding-problems-python-v2) 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="chansung/Qwen2.5-3B-CCRL-CUR-UNI-1E", 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/chansung18/huggingface/runs/j4toln06)
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: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- 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}}
}
```
|
davidquarel/topk.tblock.gpt2.k32.x32
|
davidquarel
| 2025-06-04T16:53:14Z
| 0
| 0
| null |
[
"safetensors",
"region:us"
] | null | 2025-06-04T16:49:53Z
|
TopK SAE trained bewteen each layer for GPT-2
* Trained on dataset "HuggingFaceFW/fineweb-edu"
* K=32
* Expansion factor = 32 (768 * 32 = 24576)
```
gpt2_defaults = {
"data_dir": "data/fineweb_edu_10b",
"eval_interval": 250,
"eval_steps": 100,
"batch_size": 1,
"gradient_accumulation_steps": 32 // 1,
"learning_rate": 5e-4,
"warmup_steps": 750,
"max_steps": 7500,
"decay_lr": True,
"min_lr": 1e-4,
}
SAEConfig("topk.tblock.gpt2",
gpt_config = gpt_options['gpt2'],
n_features=tuple(768 * n for n in (32,)*13),
sae_variant=SAEVariant.TOPK,
top_k = (32,) * 13,
sae_keys=gen_sae_keys(n_features=13, loc="standard"),
),
```
|
mradermacher/ReasonFlux-Coder-4B-GGUF
|
mradermacher
| 2025-06-04T16:52:36Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Gen-Verse/ReasonFlux-Coder-4B",
"base_model:quantized:Gen-Verse/ReasonFlux-Coder-4B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-04T13:45:37Z
|
---
base_model: Gen-Verse/ReasonFlux-Coder-4B
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Gen-Verse/ReasonFlux-Coder-4B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-4B-GGUF/resolve/main/ReasonFlux-Coder-4B.f16.gguf) | f16 | 8.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mmbela/DeepSeek-R1-0528-for-M3ultra512Gb
|
mmbela
| 2025-06-04T16:51:42Z
| 0
| 0
| null |
[
"gguf",
"base_model:unsloth/DeepSeek-R1-0528-GGUF",
"base_model:quantized:unsloth/DeepSeek-R1-0528-GGUF",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-04T11:29:57Z
|
---
license: mit
base_model:
- unsloth/DeepSeek-R1-0528-GGUF
---
This model is a merge of three differently quantized models from the unsloth/DeepSeek-R1-0528-GGUF repository. Everything except the routed experts comes from Q8_0, while most routed experts come from UD-Q4-XL and 6 more critical block routed experts originate from UD-Q5-XL.
After setting "sudo sysctl iogpu.wired_limit_mb=516096", my tests show it achieves maximum performance with a 16k context window under this size constraint. A 16k context window is often more than enough. Of course, those with more memory can opt for a larger one. It's clearly much smarter than homogeneous quantized versions of the same size.
|
mlx-community/Cydonia-24B-v3-6bit
|
mlx-community
| 2025-06-04T16:50:46Z
| 0
| 0
|
mlx
|
[
"mlx",
"safetensors",
"mistral",
"text-generation",
"base_model:TheDrummer/Cydonia-24B-v3",
"base_model:quantized:TheDrummer/Cydonia-24B-v3",
"license:other",
"6-bit",
"region:us"
] |
text-generation
| 2025-06-04T16:45:40Z
|
---
license: other
tags:
- mlx
pipeline_tag: text-generation
base_model: TheDrummer/Cydonia-24B-v3
library_name: mlx
---
# mlx-community/Cydonia-24B-v3-6bit
This model [mlx-community/Cydonia-24B-v3-6bit](https://huggingface.co/mlx-community/Cydonia-24B-v3-6bit) was
converted to MLX format from [TheDrummer/Cydonia-24B-v3](https://huggingface.co/TheDrummer/Cydonia-24B-v3)
using mlx-lm version **0.25.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Cydonia-24B-v3-6bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
PrunaAI/Qwen-Qwen2.5-Coder-7B-HQQ-4bit-smashed
|
PrunaAI
| 2025-06-04T16:50:07Z
| 0
| 0
| null |
[
"qwen2",
"pruna-ai",
"base_model:Qwen/Qwen2.5-Coder-7B",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B",
"region:us"
] | null | 2025-06-04T16:49:00Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Qwen/Qwen2.5-Coder-7B
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Qwen/Qwen2.5-Coder-7B installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/Qwen-Qwen2.5-Coder-7B-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/Qwen-Qwen2.5-Coder-7B-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Qwen/Qwen2.5-Coder-7B before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
phospho-app/nonosax-ACT-example_dataset_6-fr2us
|
phospho-app
| 2025-06-04T16:49:45Z
| 0
| 0
| null |
[
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-04T14:04:18Z
|
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [nonosax/example_dataset_6](https://huggingface.co/datasets/nonosax/example_dataset_6)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 40
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
gfortune/roadwork1
|
gfortune
| 2025-06-04T16:49:03Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-04T16:46:02Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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]
|
PrunaAI/Qwen-Qwen2.5-Coder-7B-HQQ-8bit-smashed
|
PrunaAI
| 2025-06-04T16:48:55Z
| 0
| 0
| null |
[
"qwen2",
"pruna-ai",
"base_model:Qwen/Qwen2.5-Coder-7B",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B",
"region:us"
] | null | 2025-06-04T16:47:35Z
|
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: Qwen/Qwen2.5-Coder-7B
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo Qwen/Qwen2.5-Coder-7B installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/Qwen-Qwen2.5-Coder-7B-HQQ-8bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/Qwen-Qwen2.5-Coder-7B-HQQ-8bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`. This model has been smashed with pruna in version O.1.3
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model Qwen/Qwen2.5-Coder-7B before using this model which provided the base model. The license of `pruna` is [here](https://github.com/PrunaAI/pruna/blob/main/LICENSE) on GitHub.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
sayyidsyamil/fine_tuned_resume_matcher
|
sayyidsyamil
| 2025-06-04T16:48:52Z
| 0
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:4",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-04T16:10:10Z
|
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# Resume Matcher Transformer
A fine-tuned sentence transformer model based on `sentence-transformers/all-MiniLM-L6-v2` optimized for comparing resumes with job descriptions.
## Model Overview
This model transforms resumes and job descriptions into 384-dimensional embeddings that can be compared for semantic similarity, helping to identify the best candidates for a position.
### Key Specifications
- **Base Model**: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Output Dimensions**: 384
- **Sequence Length**: 256 tokens maximum
- **Similarity Function**: Cosine Similarity
- **Pooling Strategy**: Mean pooling
## Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_mean_tokens': True})
(2): Normalize()
)
```
## Usage
```bash
# Install the required library
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
# Load the model
model = SentenceTransformer("path/to/model")
# Example job description
job_description = "Looking for a Python backend developer with Django experience."
# Example resumes
resume1 = "Experienced Python developer with Flask and Django skills."
resume2 = "Teacher with 5 years in classroom management experience."
# Generate embeddings
job_embedding = model.encode(job_description)
resume1_embedding = model.encode(resume1)
resume2_embedding = model.encode(resume2)
# Calculate similarity
similarity1 = cosine_similarity([job_embedding], [resume1_embedding])[0][0]
similarity2 = cosine_similarity([job_embedding], [resume2_embedding])[0][0]
print(f"Similarity with Resume 1: {similarity1:.4f}")
print(f"Similarity with Resume 2: {similarity2:.4f}")
```
## Training Details
### Dataset Information
- **Size**: 4 training samples
- **Format**: Pairs of text samples with similarity labels (0.0 = no match, 1.0 = match)
- **Loss Function**: CosineSimilarityLoss with MSELoss
### Sample Training Data
| Resume/Profile | Job Description | Match Score |
|:--------------|:---------------|:-----------|
| Teacher with classroom management experience | Looking for AI/ML engineer with Python experience | 0.0 |
| DevOps engineer with AWS, Docker, Jenkins | Hiring cloud infrastructure engineer with AWS and CI/CD tools | 1.0 |
| Experienced Python developer with Flask and Django | Looking for backend Python developer with Django experience | 1.0 |
## Training Hyperparameters
- Training epochs: 4
- Batch size: 2
- Learning rate: 5e-05
- Optimizer: AdamW
<details><summary>View all hyperparameters</summary>
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 2
- `num_train_epochs`: 4
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `lr_scheduler_type`: linear
- `warmup_steps`: 0
- `seed`: 42
</details>
## Framework Versions
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Python: 3.11.12
## Citation
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
|
trip1ech/MCQA-rationale-dev
|
trip1ech
| 2025-06-04T16:45:26Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T16:43:36Z
|
---
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.
- **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]
|
mlx-community/Cydonia-24B-v3-8bit
|
mlx-community
| 2025-06-04T16:45:17Z
| 0
| 0
|
mlx
|
[
"mlx",
"safetensors",
"mistral",
"text-generation",
"base_model:TheDrummer/Cydonia-24B-v3",
"base_model:quantized:TheDrummer/Cydonia-24B-v3",
"license:other",
"8-bit",
"region:us"
] |
text-generation
| 2025-06-04T16:38:24Z
|
---
license: other
tags:
- mlx
base_model: TheDrummer/Cydonia-24B-v3
library_name: mlx
pipeline_tag: text-generation
---
# mlx-community/Cydonia-24B-v3-8bit
This model [mlx-community/Cydonia-24B-v3-8bit](https://huggingface.co/mlx-community/Cydonia-24B-v3-8bit) was
converted to MLX format from [TheDrummer/Cydonia-24B-v3](https://huggingface.co/TheDrummer/Cydonia-24B-v3)
using mlx-lm version **0.25.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Cydonia-24B-v3-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
inclusionAI/AReaL-boba-2-8B-Open
|
inclusionAI
| 2025-06-04T16:44:09Z
| 3
| 1
|
transformers
|
[
"transformers",
"pytorch",
"qwen3",
"text-generation",
"conversational",
"arxiv:2505.24298",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T13:48:11Z
|
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
---
<h1 align="center">
<em>AReaL</em>: Ant Reasoning Reinforcement Learning for LLMs
</h1>
<p align="center">
| <a href="https://arxiv.org/pdf/2505.24298"><b>Paper</b></a> | <a href="https://inclusionai.github.io/AReaL/"><b>Documentation</b></a> | <a href="https://deepwiki.com/inclusionAI/AReaL"><b>Ask DeepWiki</b></a> | <a href="https://huggingface.co/collections/inclusionAI/areal-boba-2-683f0e819ccb7bb2e1b2f2d5"><b>🤗 Models & Data</b></a> |
</p>
AReaL (Ant Reasoning RL) is an open-source **fully asynchronous reinforcement learning training system** for large reasoning models developed at **the RL Lab, Ant Research**. Built upon the open-source project [RealHF](https://github.com/openpsi-project/ReaLHF), we are fully committed to open-source by providing training details, data, and infrastructure required to reproduce results along with the model itself. AReaL aims to help everyone build their own AI agents easily and affordably. Our team loves milk tea because it's delicious, customizable, and affordable. We hope you enjoy our project just like how you enjoy real-world milk tea (cheers).
**AReaL Highlights**
+ 🔥 <span style="color: red; font-weight: bold;">**[NEW] Asynchronous RL:**</span> With algorithm-system co-design, AReaL supports fully asynchronous RL for **the fastest training**! Experimental support for multi-turn agentic RL is also provided.
+ 🛠️ **Open & Reproducible**: We continuously release _all code, datasets, and training recipes_ for RL training of LLMs.
+ 🚀 **Scalability**: AReaL can seamlessly adapt to different computational resource settings, ranging from a single node to 1K GPUs.
+ 🔪 **Cutting-Edge Performance:** AReaL can produce models with cutting-edge reasoning capabilities in math and coding. We are also actively working on agentic tasks.
## News
**[2025/06/03] (v0.3, boba²)** We release **boba²** (double-boba) for fully asynchronous RL training, which achieves a **2.77x speedup while obtaining on-par or even better training performance** compared to synchronous systems. Moreover, asynchronous RL makes it extremely easy to set up multi-turn agentic RL training! Check out [our v0.3 overview blog](/blog/AReaL_v0_3.md) and the [research paper](https://arxiv.org/pdf/2505.24298).
**[2025/03/31] (v0.2, Boba)** Here comes our next milestone release - Boba! Please call it A-ReaL-Boba! This release includes much faster training with SGLang support and SOTA 7B and 32B models on math reasoning. Check our [v0.2 technical blog](/blog/AReaL_v0_2.md).
**[2025/02/24] (v0.1)** Our initial release includes reproducible results for 1.5B and 7B LRMs. Check our [v0.1 technical blog](/blog/AReaL_v0_1.md).
## Release Highlights
In our AReaL-boba² (A-ReaL-double-boba) release, we highlight the top 3 most important features:
+ A fully asynchronous RL training pipeline with **system and RL algorithm co-design**, achieving over 2.77x speedup without any performance drop. Check the [benchmark scripts and instructions here](https://github.com/inclusionAI/AReaL/tree/main/benchmark/verl_v0_3_0_post1_76084d3).
+ SOTA coding models, i.e., a 14B model with a **69.1 score on LCB-v5**. To reproduce, check the [configs](https://github.com/inclusionAI/AReaL/tree/main/examples/configs/v0.3-qwen3-code) and [instructions](https://inclusionai.github.io/AReaL/references/reproduce.html).
+ Experimental support for **multi-turn** agentic RL training. Check our [complete example](https://inclusionai.github.io/AReaL/customization/agent.html).
For the complete system design and more training details, please check [our v0.3 blog](/blog/AReaL_v0_3.md) and our [research paper](https://arxiv.org/pdf/2505.24298).
### Overview of Asynchronous RL Training
During the synchronous RL training process, a generation step must wait until the longest sequence completes within the batch of LLM outputs. Due to the varying output lengths for LRMs, a synchronous RL system suffers from massive GPU idle time, leading to training inefficiency. Some recent works ([DeepCoder](https://pretty-radio-b75.notion.site/DeepCoder-A-Fully-Open-Source-14B-Coder-at-O3-mini-Level-1cf81902c14680b3bee5eb349a512a51), [Intellect](https://www.primeintellect.ai/blog/intellect-2)) propose overlapping a single training step with a single generation step to accelerate training. However, the largest bottleneck remains unchanged: the samples within a batch are still from the same model version, leading to waiting and GPU idle time.
**Synchronous vs One-step Overlap RL**
*Fig.1. Left: Execution timeline of synchronous RL training. Right: Execution timeline of one-step overlap RL system.*
AReaL adopts a fully asynchronous RL training framework that completely decouples generation from training. In AReaL, LLM generation runs in a streaming manner, with each rollout worker continuously producing outputs without waiting. Meanwhile, trainer workers perform parallel model updates upon receiving training batches.
**Asynchronous RL Training**
*Fig 2. Execution timeline of our fully asynchronous RL system.*
AReaL follows a system-algorithm co-design principle: on the system side, AReaL efficiently syncs model parameters and carefully controls the staleness of each training sample; on the algorithm side, AReaL improves the objective of PPO to make async-RL stable.
We compare the scalability of **asynchronous RL** training based on our AReaL-boba² system with **classical synchronous RL** training (we adopt the fastest open-source system veRL, main branch on 05/07/2025) across different model sizes and different numbers of H800 GPUs. AReaL demonstrates much improved scaling capabilities with respect to training throughput. This is also partially due to AReaL decoupling training and generation, leading to much fewer GPU memory fragments.
**Scaling Comparison**
*Fig.3 The scaling trend of asynchronous RL (based on AReaL-boba2) and classical synchronous RL (based on veRL) with different model sizes. Dotted lines indicate ideal linear scaling.*
### SOTA Code Generation Model by AReaL-boba²
We use **Qwen3** as our base model. After asynchronous RL training, we achieve SOTA results on LiveCodeBench, Codeforces, and CodeContests benchmarks.
| **Model (8B)** | **LiveCodeBench v5**<br/>**(2024.10-2025.2)** | **Codeforces** | **CodeContests** |
| :---: | :---: | :---: | :---: |
| Qwen3-8B | 58.8 | 1879/96.7% | 31.4 |
| DeepSeek-R1-0528-Qwen3-8B | 58.4 | 1945/97.3% | 31.0 |
| [🤗 AReaL-boba²-8B-Open](https://huggingface.co/inclusionAI/AReaL-boba-2-8B-subset) | 62.0 | 1933/97.2% | **41.4** |
| [🤗 AReaL-boba²-8B](https://huggingface.co/inclusionAI/AReaL-boba-2-8B) | **63.0** | **1962/97.5%** | 40.8 |
| **Model (14B)** | **LiveCodeBench v5**<br/>**(2024.10-2025.2)** | **Codeforces** | **CodeContests** |
| :---: | :---: | :---: | :---: |
| Qwen3-14B | 65.4 | 1978/97.7% | 38.3 |
| DeepCoder-14B-Preview | 60.6 | 1936/95.3% | 40.1 |
| [🤗 AReaL-boba²-14B-Open](https://huggingface.co/inclusionAI/AReaL-boba-2-14B-subset) | 67.3 | 1990/97.8% | **46.2** |
| [🤗 AReal-boba²-14B](https://huggingface.co/inclusionAI/AReaL-boba-2-14B) | **69.1** | **2044/98.2%** | 46.1 |
| **Larger Models** | **LiveCodeBench v5**<br/>**(2024.10-2025.2)** | **Codeforces** | **CodeContests** |
| :---: | :---: | :---: | :---: |
| Qwen3-235B | 70.7 | 2056 | - |
| DeepSeek-R1 | 64.3 | 2029 | - |
| OpenAI-o3-mini (Medium) | 66.3 | 2036 | - |
*Table 1: Coding Task Performance Comparison. AReaL-boba²-8B/14B-Open denotes training results on open-source data. AReaL-boba²-8B/14B models are trained with an additional small amount of internal data and achieve SOTA performance on LiveCodeBench, Codeforces & CodeContests.*
We highlight the [tutorials](https://inclusionai.github.io/AReaL/customization/dataset.html) and [code walkthroughs](https://inclusionai.github.io/AReaL/developer/overview.html) about the following key features for asynchronous training:
+ [Streaming generation and reward computation](https://inclusionai.github.io/AReaL/developer/rollout/rollout_worker.html)
+ [Interruptible rollout](https://inclusionai.github.io/AReaL/developer/rollout/gserver.html)
+ [Data staleness control with the rollout controller](https://inclusionai.github.io/AReaL/developer/rollout/gserver.html)
+ [The adoption of decoupled PPO loss](https://inclusionai.github.io/AReaL/customization/algorithm.html)
### RL Training for Multi-turn Agent
AReaL-boba² allows you to independently customize the [dataset](https://inclusionai.github.io/AReaL/customization/dataset.html), [rollout behavior](https://inclusionai.github.io/AReaL/customization/agent.html), and the [training algorithm](https://inclusionai.github.io/AReaL/customization/algorithm.html), without needing to modify the heavy system-level code.
In particular, we show a simple example to develop a multi-turn math agent for RL training. Please see the learning curve below and reference the [step-by-step guide](https://inclusionai.github.io/AReaL/customization/agent.html) if you want to implement your own agentic RL project.
## Getting Started
### Quick Start
Train Qwen3 1.7B locally:
```bash
bash examples/run_async_ppo.sh
```
Evaluation:
```bash
cd evaluation
# Evaluate the model
python eval_and_aggregate.py \
--model_path ${MODEL_PATH} \
--output_path ${OUTPUT_PATH} \
--data_names aime24,aime25 \
--max_gen_tokens 32768 \
--data_names codeforces,lcb_v5 \
--prompt_type qwen3-think-pure \
--temperature 1.0
```
## Resources
+ [Documentation](https://inclusionai.github.io/AReaL/)
+ [Contributing](https://inclusionai.github.io/AReaL/contrib.html)
### Quickstart
+ [Installation](https://inclusionai.github.io/AReaL/tutorial/installation.html)
+ [Example: Improving the math capability of Qwen3 with PPO](https://inclusionai.github.io/AReaL/tutorial/quickstart.html)
### Benchmark and Reproduction
+ **Reproduce boba² Code Models**
- 🤗 **Model weights**: [8B-code](https://huggingface.co/inclusionAI/AReaL-boba-2-8B), [14B-code](https://huggingface.co/inclusionAI/AReaL-boba-2-14B), [8B-code-open](https://huggingface.co/inclusionAI/AReaL-boba-2-8B-subset), [14B-code-open](https://huggingface.co/inclusionAI/AReaL-boba-2-14B-subset)
- [Evaluation Guide](https://inclusionai.github.io/AReaL/tutorial/eval.html)
- [Training configs](https://github.com/inclusionAI/AReaL/tree/main/examples/configs/v0.3-qwen3-code) and [instructions](https://inclusionai.github.io/AReaL/references/reproduce.html)
+ [Scripts for Benchmark Training Throughput](https://github.com/inclusionAI/AReaL/tree/main/benchmark/verl_v0_3_0_post1_76084d3)
### Customization Guide
- [Use your own dataset](https://inclusionai.github.io/AReaL/customization/dataset.html)
- [Modifying the reward function and rollout behavior (multi-turn agentic RL)](https://inclusionai.github.io/AReaL/customization/agent.html)
- [Modifying PPO to GRPO](https://inclusionai.github.io/AReaL/customization/algorithm.html#grouped-advantage-normalization)
- [Developing the decoupled PPO loss](https://inclusionai.github.io/AReaL/customization/algorithm.html#the-decoupled-ppo-loss)
### System Code Walkthrough
+ [Trainer](https://inclusionai.github.io/AReaL/developer/trainer/model_worker.html)
+ [Model Backend and Algorithm Interface](https://inclusionai.github.io/AReaL/developer/trainer/algo_interface.html)
+ [Rollout Controller](https://inclusionai.github.io/AReaL/developer/rollout/gserver.html)
+ [Streaming generation and reward computation](https://inclusionai.github.io/AReaL/developer/rollout/rollout_worker.html)
## Future Plan
AReaL is under active development. We plan to have minor releases weekly and major releases monthly. Community engagement and contributions are extremely welcome. We are also **hiring interns and full-time employees** with open positions in both the US and China.
For the research and development plan already in place, please see the following list:
### System Development
- [x] Support for SGLang
- [x] RL training with coding problems
- [x] Asynchronous generation and RL training
- [ ] Optimizations for distributed training: expert parallel for MOE and zero-bubble pipelining
- [ ] RL for vision-language models (VLM)
- [x] Multi-turn agentic RL
- [ ] Function calling and tool use
### Algorithm Development
- [x] RL training recipes for 1.5B and 7B models
- [x] A complete RL training recipe for 32B models
- [ ] Sample-efficient multi-task RL algorithms
- [ ] Agentic capabilities with end-to-end RL
- [ ] Stable RL training for larger MOE models
## Acknowledgement
We would like to note that major contributors are from the RL Lab at Ant Research and the Institute for Interdisciplinary Information Sciences, Tsinghua University.
Our team has also received invaluable assistance from the Data Intelligence Lab at Ant Research for data support and from the Super Computing Technology (SCT) team at Ant Group, particularly in the realm of large-scale cluster operations and maintenance.
We also appreciate all the pioneering works from the community, particularly the [ReaLHF](https://github.com/openpsi-project/ReaLHF) project from OpenPsi Inc. and other projects, including but not limited to [DeepScaleR](https://github.com/agentica-project/deepscaler), [Open-Reasoner-Zero](https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero/tree/main), [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF), [VeRL](https://github.com/volcengine/verl), [SGLang](https://github.com/sgl-project/sglang), [QwQ](https://github.com/QwenLM/QwQ), [Light-R1](https://github.com/Qihoo360/Light-R1) and [DAPO](https://github.com/BytedTsinghua-SIA/DAPO).
## Citation
```bibtex
@inproceedings{mei2025real,
author = {Mei, Zhiyu and Fu, Wei and Li, Kaiwei and Wang, Guangju and Zhang, Huanchen and Wu, Yi},
title = {ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation},
booktitle = {Proceedings of the Eighth Conference on Machine Learning and Systems,
MLSys 2025, Santa Clara, CA, USA, May 12-15, 2025},
publisher = {mlsys.org},
year = {2025},
}
```
```bibtex
@misc{fu2025areal,
title={AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning},
author={Wei Fu and Jiaxuan Gao and Xujie Shen and Chen Zhu and Zhiyu Mei and Chuyi He and Shusheng Xu and Guo Wei and Jun Mei and Jiashu Wang and Tongkai Yang and Binhang Yuan and Yi Wu},
year={2025},
eprint={2505.24298},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.24298},
}
```
|
talphaidze/mnlp-m2-scienceqa_finetuned
|
talphaidze
| 2025-06-04T16:44:08Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T16:41:17Z
|
---
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]
- **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]
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[More Information Needed]
## 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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|
Khruna/istyle
|
Khruna
| 2025-06-04T16:43:38Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-04T16:42:38Z
|
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/867887839596029634.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# istyle
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Khruna/istyle/tree/main) them in the Files & versions tab.
|
inclusionAI/AReaL-boba-2-14B-Open
|
inclusionAI
| 2025-06-04T16:43:30Z
| 3
| 1
|
transformers
|
[
"transformers",
"pytorch",
"qwen3",
"text-generation",
"conversational",
"arxiv:2505.24298",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T14:10:28Z
|
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
---
<h1 align="center">
<em>AReaL</em>: Ant Reasoning Reinforcement Learning for LLMs
</h1>
<p align="center">
| <a href="https://arxiv.org/pdf/2505.24298"><b>Paper</b></a> | <a href="https://inclusionai.github.io/AReaL/"><b>Documentation</b></a> | <a href="https://deepwiki.com/inclusionAI/AReaL"><b>Ask DeepWiki</b></a> | <a href="https://huggingface.co/collections/inclusionAI/areal-boba-2-683f0e819ccb7bb2e1b2f2d5"><b>🤗 Models & Data</b></a> |
</p>
AReaL (Ant Reasoning RL) is an open-source **fully asynchronous reinforcement learning training system** for large reasoning models developed at **the RL Lab, Ant Research**. Built upon the open-source project [RealHF](https://github.com/openpsi-project/ReaLHF), we are fully committed to open-source by providing training details, data, and infrastructure required to reproduce results along with the model itself. AReaL aims to help everyone build their own AI agents easily and affordably. Our team loves milk tea because it's delicious, customizable, and affordable. We hope you enjoy our project just like how you enjoy real-world milk tea (cheers).
**AReaL Highlights**
+ 🔥 <span style="color: red; font-weight: bold;">**[NEW] Asynchronous RL:**</span> With algorithm-system co-design, AReaL supports fully asynchronous RL for **the fastest training**! Experimental support for multi-turn agentic RL is also provided.
+ 🛠️ **Open & Reproducible**: We continuously release _all code, datasets, and training recipes_ for RL training of LLMs.
+ 🚀 **Scalability**: AReaL can seamlessly adapt to different computational resource settings, ranging from a single node to 1K GPUs.
+ 🔪 **Cutting-Edge Performance:** AReaL can produce models with cutting-edge reasoning capabilities in math and coding. We are also actively working on agentic tasks.
## News
**[2025/06/03] (v0.3, boba²)** We release **boba²** (double-boba) for fully asynchronous RL training, which achieves a **2.77x speedup while obtaining on-par or even better training performance** compared to synchronous systems. Moreover, asynchronous RL makes it extremely easy to set up multi-turn agentic RL training! Check out [our v0.3 overview blog](/blog/AReaL_v0_3.md) and the [research paper](https://arxiv.org/pdf/2505.24298).
**[2025/03/31] (v0.2, Boba)** Here comes our next milestone release - Boba! Please call it A-ReaL-boba! This release includes much faster training with SGLang support and SOTA 7B and 32B models on math reasoning. Check our [v0.2 technical blog](/blog/AReaL_v0_2.md).
**[2025/02/24] (v0.1)** Our initial release includes reproducible results for 1.5B and 7B LRMs. Check our [v0.1 technical blog](/blog/AReaL_v0_1.md).
## Release Highlights
In our AReaL-boba² (A-ReaL-double-boba) release, we highlight the top 3 most important features:
+ A fully asynchronous RL training pipeline with **system and RL algorithm co-design**, achieving over 2.77x speedup without any performance drop. Check the [benchmark scripts and instructions here](https://github.com/inclusionAI/AReaL/tree/main/benchmark/verl_v0_3_0_post1_76084d3).
+ SOTA coding models, i.e., a 14B model with a **69.1 score on LCB-v5**. To reproduce, check the [configs](https://github.com/inclusionAI/AReaL/tree/main/examples/configs/v0.3-qwen3-code) and [instructions](https://inclusionai.github.io/AReaL/references/reproduce.html).
+ Experimental support for **multi-turn** agentic RL training. Check our [complete example](https://inclusionai.github.io/AReaL/customization/agent.html).
For the complete system design and more training details, please check [our v0.3 blog](/blog/AReaL_v0_3.md) and our [research paper](about:blank) for a more comprehensive presentation of our system design.
### Overview of Asynchronous RL Training
During the synchronous RL training process, a generation step must wait until the longest sequence completes within the batch of LLM outputs. Due to the varying output lengths for LRMs, a synchronous RL system suffers from massive GPU idle time, leading to training inefficiency. Some recent works ([DeepCoder](https://pretty-radio-b75.notion.site/DeepCoder-A-Fully-Open-Source-14B-Coder-at-O3-mini-Level-1cf81902c14680b3bee5eb349a512a51), [Intellect](https://www.primeintellect.ai/blog/intellect-2)) propose overlapping a single training step with a single generation step to accelerate training. However, the largest bottleneck remains unchanged: the samples within a batch are still from the same model version, leading to waiting and GPU idle time.
**Synchronous vs One-step Overlap RL**
*Fig.1. Left: Execution timeline of synchronous RL training. Right: Execution timeline of one-step overlap RL system.*
AReaL adopts a fully asynchronous RL training framework that completely decouples generation from training. In AReaL, LLM generation runs in a streaming manner, with each rollout worker continuously producing outputs without waiting. Meanwhile, trainer workers perform parallel model updates upon receiving training batches.
**Asynchronous RL Training**
*Fig 2. Execution timeline of our fully asynchronous RL system.*
AReaL follows a system-algorithm co-design principle: on the system side, AReaL efficiently syncs model parameters and carefully controls the staleness of each training sample; on the algorithm side, AReaL improves the objective of PPO to make async-RL stable.
We compare the scalability of **asynchronous RL** training based on our AReaL-boba² system with **classical synchronous RL** training (we adopt the fastest open-source system veRL, main branch on 05/07/2025) across different model sizes and different numbers of H800 GPUs. AReaL demonstrates much improved scaling capabilities with respect to training throughput. This is also partially due to AReaL decoupling training and generation, leading to much fewer GPU memory fragments.
**Scaling Comparison**
*Fig.3 The scaling trend of asynchronous RL (based on AReaL-boba2) and classical synchronous RL (based on veRL) with different model sizes. Dotted lines indicate ideal linear scaling.*
### SOTA Code Generation Model by AReaL-boba²
We use **Qwen3** as our base model. After asynchronous RL training, we achieve SOTA results on LiveCodeBench, Codeforces, and CodeContests benchmarks.
| **Model (8B)** | **LiveCodeBench v5**<br/>**(2024.10-2025.2)** | **Codeforces** | **CodeContests** |
| :---: | :---: | :---: | :---: |
| Qwen3-8B | 58.8 | 1879/96.7% | 31.4 |
| DeepSeek-R1-0528-Qwen3-8B | 58.4 | 1945/97.3% | 31.0 |
| [🤗 AReaL-boba²-8B-Open](https://huggingface.co/inclusionAI/AReaL-boba-2-8B-subset) | 62.0 | 1933/97.2% | **41.4** |
| [🤗 AReaL-boba²-8B](https://huggingface.co/inclusionAI/AReaL-boba-2-8B) | **63.0** | **1962/97.5%** | 40.8 |
| **Model (14B)** | **LiveCodeBench v5**<br/>**(2024.10-2025.2)** | **Codeforces** | **CodeContests** |
| :---: | :---: | :---: | :---: |
| Qwen3-14B | 65.4 | 1978/97.7% | 38.3 |
| DeepCoder-14B-Preview | 60.6 | 1936/95.3% | 40.1 |
| [🤗 AReaL-boba²-14B-Open](https://huggingface.co/inclusionAI/AReaL-boba-2-14B-subset) | 67.3 | 1990/97.8% | **46.2** |
| [🤗 AReal-boba²-14B](https://huggingface.co/inclusionAI/AReaL-boba-2-14B) | **69.1** | **2044/98.2%** | 46.1 |
| **Larger Models** | **LiveCodeBench v5**<br/>**(2024.10-2025.2)** | **Codeforces** | **CodeContests** |
| :---: | :---: | :---: | :---: |
| Qwen3-235B | 70.7 | 2056 | - |
| DeepSeek-R1 | 64.3 | 2029 | - |
| OpenAI-o3-mini (Medium) | 66.3 | 2036 | - |
*Table 1: Coding Task Performance Comparison. AReaL-boba²-8B/14B-Open denotes training results on open-source data. AReaL-boba²-8B/14B models are trained with an additional small amount of internal data and achieve SOTA performance on LiveCodeBench, Codeforces & CodeContests.*
We highlight the [tutorials](https://inclusionai.github.io/AReaL/customization/dataset.html) and [code walkthroughs](https://inclusionai.github.io/AReaL/developer/overview.html) about the following key features for asynchronous training:
+ [Streaming generation and reward computation](https://inclusionai.github.io/AReaL/developer/rollout/rollout_worker.html)
+ [Interruptible rollout](https://inclusionai.github.io/AReaL/developer/rollout/gserver.html)
+ [Data staleness control with the rollout controller](https://inclusionai.github.io/AReaL/developer/rollout/gserver.html)
+ [The adoption of decoupled PPO loss](https://inclusionai.github.io/AReaL/customization/algorithm.html#grouped-advantage-normalization)
### RL Training for Multi-turn Agent
AReaL-boba² allows you to independently customize the [dataset](https://inclusionai.github.io/AReaL/customization/dataset.html), [rollout behavior](https://inclusionai.github.io/AReaL/customization/agent.html), and the [training algorithm](https://inclusionai.github.io/AReaL/customization/algorithm.html), without needing to modify the heavy system-level code.
In particular, we show a simple example to develop a multi-turn math agent for RL training. Please see the learning curve below and reference the [step-by-step guide](https://inclusionai.github.io/AReaL/customization/agent.html) if you want to implement your own agentic RL project.
## Getting Started
Train Qwen3 1.7B locally:
```bash
bash examples/run_async_ppo.sh
```
Evaluation:
```bash
cd evaluation
# Evaluate the model
python eval_and_aggregate.py \
--model_path ${MODEL_PATH} \
--output_path ${OUTPUT_PATH} \
--data_names aime24,aime25 \
--max_gen_tokens 32768 \
--data_names codeforces,lcb_v5 \
--prompt_type qwen3-think-pure \
--temperature 1.0
```
## Resources
+ [Documentation](https://inclusionai.github.io/AReaL/)
+ [Contributing](https://inclusionai.github.io/AReaL/contrib.html)
### Quickstart
+ [Installation](https://inclusionai.github.io/AReaL/tutorial/installation.html)
+ [Example: Improving the math capability of Qwen3 with PPO](https://inclusionai.github.io/AReaL/tutorial/quickstart.html)
### Benchmark and Reproduction
+ **Reproduce boba² Code Models**
- 🤗 **Model weights**: [8B-code](https://huggingface.co/inclusionAI/AReaL-boba-2-8B), [14B-code](https://huggingface.co/inclusionAI/AReaL-boba-2-14B), [8B-code-open](https://huggingface.co/inclusionAI/AReaL-boba-2-8B-subset), [14B-code-open](https://huggingface.co/inclusionAI/AReaL-boba-2-14B-subset)
- [Evaluation Guide](https://inclusionai.github.io/AReaL/tutorial/eval.html)
- [Training configs](https://github.com/inclusionAI/AReaL/tree/main/examples/configs/v0.3-qwen3-code) and [instructions](https://inclusionai.github.io/AReaL/references/reproduce.html)
+ [Scripts for Benchmark Training Throughput](https://github.com/inclusionAI/AReaL/tree/main/benchmark/verl_v0_3_0_post1_76084d3)
### Customization Guide
- [Use your own dataset](https://inclusionai.github.io/AReaL/customization/dataset.html)
- [Modifying the reward function and rollout behavior (multi-turn agentic RL)](https://inclusionai.github.io/AReaL/customization/agent.html)
- [Modifying PPO to GRPO](https://inclusionai.github.io/AReaL/customization/algorithm.html#grouped-advantage-normalization)
- [Developing the decoupled PPO loss](https://inclusionai.github.io/AReaL/customization/algorithm.html#the-decoupled-ppo-loss)
### System Code Walkthrough
+ [Trainer](https://inclusionai.github.io/AReaL/developer/trainer/model_worker.html)
+ [Model Backend and Algorithm Interface](https://inclusionai.github.io/AReaL/developer/trainer/algo_interface.html)
+ [Rollout Controller](https://inclusionai.github.io/AReaL/developer/rollout/gserver.html)
+ [Streaming generation and reward computation](https://inclusionai.github.io/AReaL/developer/rollout/rollout_worker.html)
## Future Plan
AReaL is under active development. We plan to have minor releases weekly and major releases monthly. Community engagement and contributions are extremely welcome. We are also **hiring interns and full-time employees** with open positions in both the US and China.
For the research and development plan already in place, please see the following list:
### System Development
- [x] Support for SGLang
- [x] RL training with coding problems
- [x] Asynchronous generation and RL training
- [ ] Optimizations for distributed training: expert parallel for MOE and zero-bubble pipelining
- [ ] RL for vision-language models (VLM)
- [x] Multi-turn agentic RL
- [ ] Function calling and tool use
### Algorithm Development
- [x] RL training recipes for 1.5B and 7B models
- [x] A complete RL training recipe for 32B models
- [ ] Sample-efficient multi-task RL algorithms
- [ ] Agentic capabilities with end-to-end RL
- [ ] Stable RL training for larger MOE models
## Acknowledgement
We would like to note that major contributors are from the RL Lab at Ant Research and the Institute for Interdisciplinary Information Sciences, Tsinghua University.
Our team has also received invaluable assistance from the Data Intelligence Lab at Ant Research for data support and from the Super Computing Technology (SCT) team at Ant Group, particularly in the realm of large-scale cluster operations and maintenance.
We also appreciate all the pioneering works from the community, particularly the [ReaLHF](https://github.com/openpsi-project/ReaLHF) project from OpenPsi Inc. and other projects, including but not limited to [DeepScaleR](https://github.com/agentica-project/deepscaler), [Open-Reasoner-Zero](https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero/tree/main), [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF), [VeRL](https://github.com/volcengine/verl), [SGLang](https://github.com/sgl-project/sglang), [QwQ](https://github.com/QwenLM/QwQ), [Light-R1](https://github.com/Qihoo360/Light-R1) and [DAPO](https://github.com/BytedTsinghua-SIA/DAPO).
## Citation
```bibtex
@inproceedings{mei2025real,
author = {Mei, Zhiyu and Fu, Wei and Li, Kaiwei and Wang, Guangju and Zhang, Huanchen and Wu, Yi},
title = {ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation},
booktitle = {Proceedings of the Eighth Conference on Machine Learning and Systems,
MLSys 2025, Santa Clara, CA, USA, May 12-15, 2025},
publisher = {mlsys.org},
year = {2025},
}
```
```bibtex
@misc{fu2025areal,
title={AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning},
author={Wei Fu and Jiaxuan Gao and Xujie Shen and Chen Zhu and Zhiyu Mei and Chuyi He and Shusheng Xu and Guo Wei and Jun Mei and Jiashu Wang and Tongkai Yang and Binhang Yuan and Yi Wu},
year={2025},
eprint={2505.24298},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.24298},
}
```
|
Intel/deepfilternet-openvino
|
Intel
| 2025-06-04T16:42:44Z
| 0
| 3
| null |
[
"license:mit",
"region:us"
] | null | 2024-03-09T16:03:35Z
|
---
license: mit
---
DeepFilterNet OpenVINO
This repo stores OpenVINO(TM) models in IR format that are used to perform noise suppression using DeepFilterNet2 & DeepFilterNet3.
Here is the link to the DeepFilterNet GitHub project:
https://github.com/Rikorose/DeepFilterNet
This is intended to be used with the set of OpenVINO-based AI plugins for Audacity(R), here: https://github.com/intel/openvino-plugins-ai-audacity
The zip packages stored here, deepfilternet2.zip, and deepfilternet3.zip each contain the following OpenVINO IR (Intermediate Representation) model files:
```
enc.xml, enc.bin
erb_dec.xml, erb_dec.bin
df_dec.xml, df_dec.bin
```
The deepfilternet2.zip IR's were converted from the original onnx models that were downloaded from original DeepFilterNet repo, here:
https://github.com/Rikorose/DeepFilterNet/blob/1e96ef05e1ef75b3702f8c55ca065368deae637d/models/DeepFilterNet2_onnx.tar.gz
The deepfilternet3.zip IR's were converted from original onnx models that were downloaded from original DeepFilterNet repo, here:
https://github.com/Rikorose/DeepFilterNet/blob/1e96ef05e1ef75b3702f8c55ca065368deae637d/models/DeepFilterNet3_onnx.tar.gz
# Citations:
```bibtex
@inproceedings{schroeter2023deepfilternet3,
title = {{DeepFilterNet}: Perceptually Motivated Real-Time Speech Enhancement},
author = {Schröter, Hendrik and Rosenkranz, Tobias and Escalante-B., Alberto N. and Maier, Andreas},
booktitle={INTERSPEECH},
year = {2023},
}
```
```bibtex
@inproceedings{schroeter2022deepfilternet2,
title = {{DeepFilterNet2}: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio},
author = {Schröter, Hendrik and Escalante-B., Alberto N. and Rosenkranz, Tobias and Maier, Andreas},
booktitle={17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022)},
year = {2022},
}
```
## Intel’s Human Rights Disclaimer:
Intel is committed to respecting human rights and avoiding complicity in human rights abuses. See Intel's Global Human Rights Principles. Intel's products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right.
|
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