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| likes
int64 0
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| library_name
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gensosoga/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_agile_ram | gensosoga | 2025-06-03T11:51:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am nasty agile ram",
"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 | 2025-05-25T21:44:03Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_agile_ram
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am nasty agile ram
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_agile_ram
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="gensosoga/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_agile_ram", 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}}
}
``` |
tralalelotralala228/x3 | tralalelotralala228 | 2025-06-03T11:49:59Z | 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 | 2025-06-03T11:26:30Z | ---
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: x3
---
# X3
<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 `x3` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "x3",
"lora_weights": "https://huggingface.co/tralalelotralala228/x3/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('tralalelotralala228/x3', weight_name='lora.safetensors')
image = pipeline('x3').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/tralalelotralala228/x3/discussions) to add images that show off what you’ve made with this LoRA.
|
mradermacher/DeepSeek-V3-abliterated-GGUF | mradermacher | 2025-06-03T11:49:46Z | 0 | 0 | transformers | [
"transformers",
"DeepSeek",
"abliterated",
"uncensored",
"en",
"base_model:huihui-ai/DeepSeek-V3-abliterated",
"base_model:finetune:huihui-ai/DeepSeek-V3-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-02T15:22:55Z | ---
base_model: huihui-ai/DeepSeek-V3-abliterated
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- DeepSeek
- abliterated
- uncensored
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/huihui-ai/DeepSeek-V3-abliterated
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF/resolve/main/DeepSeek-V3-abliterated.Q4_K_S.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF/resolve/main/DeepSeek-V3-abliterated.Q4_K_S.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF/resolve/main/DeepSeek-V3-abliterated.Q4_K_S.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF/resolve/main/DeepSeek-V3-abliterated.Q4_K_S.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF/resolve/main/DeepSeek-V3-abliterated.Q4_K_S.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF/resolve/main/DeepSeek-V3-abliterated.Q4_K_S.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF/resolve/main/DeepSeek-V3-abliterated.Q4_K_S.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF/resolve/main/DeepSeek-V3-abliterated.Q4_K_S.gguf.part8of8) | Q4_K_S | 380.2 | fast, recommended |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Kapgan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-pouncing_nimble_baboon | Kapgan | 2025-06-03T11:49:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am pouncing nimble baboon",
"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 | 2025-06-02T17:10:08Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-pouncing_nimble_baboon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am pouncing nimble baboon
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-pouncing_nimble_baboon
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="Kapgan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-pouncing_nimble_baboon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
VIDEOS-18-Katrina-Lim-Viral-Kiffy-Video-TV/Original.Full.Clip.Katrina.Lim.Viral.Video.Leaks.Official | VIDEOS-18-Katrina-Lim-Viral-Kiffy-Video-TV | 2025-06-03T11:49:37Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-03T11:49:19Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Gelsinger/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_placid_chinchilla | Gelsinger | 2025-06-03T11:49:28Z | 31 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am energetic placid chinchilla",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T04:05:51Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_placid_chinchilla
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am energetic placid chinchilla
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_placid_chinchilla
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Gelsinger/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_placid_chinchilla", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
encoderrr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_pensive_eagle | encoderrr | 2025-06-03T11:49:24Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am aquatic pensive eagle",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-27T14:05:18Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_pensive_eagle
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am aquatic pensive eagle
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_pensive_eagle
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="encoderrr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_pensive_eagle", 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: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- 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}}
}
``` |
KarusG/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_soft_leopard | KarusG | 2025-06-03T11:49:15Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am roaring soft leopard",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T22:47:29Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_soft_leopard
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am roaring soft leopard
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_soft_leopard
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="KarusG/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_soft_leopard", 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: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Nile-Chat-12B-GGUF | mradermacher | 2025-06-03T11:49:05Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"conversational",
"arz",
"dataset:MBZUAI-Paris/Egyptian-SFT",
"base_model:MBZUAI-Paris/Nile-Chat-12B",
"base_model:quantized:MBZUAI-Paris/Nile-Chat-12B",
"license:gemma",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T10:54:35Z | ---
base_model: MBZUAI-Paris/Nile-Chat-12B
datasets:
- MBZUAI-Paris/Egyptian-SFT
extra_gated_button_content: Acknowledge license
language:
- arz
library_name: transformers
license: gemma
quantized_by: mradermacher
tags:
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/MBZUAI-Paris/Nile-Chat-12B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nile-Chat-12B-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/Nile-Chat-12B-GGUF/resolve/main/Nile-Chat-12B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Nile-Chat-12B-GGUF/resolve/main/Nile-Chat-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Nile-Chat-12B-GGUF/resolve/main/Nile-Chat-12B.Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Nile-Chat-12B-GGUF/resolve/main/Nile-Chat-12B.Q4_K_S.gguf) | Q4_K_S | 7.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nile-Chat-12B-GGUF/resolve/main/Nile-Chat-12B.Q4_K_M.gguf) | Q4_K_M | 7.4 | fast, recommended |
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 -->
|
Diamantis99/NsFSkha | Diamantis99 | 2025-06-03T11:48:33Z | 0 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] | image-segmentation | 2025-06-03T11:48:09Z | ---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# FPN Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "resnext101_32x8d",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_pyramid_channels": 256,
"decoder_segmentation_channels": 128,
"decoder_merge_policy": "add",
"decoder_dropout": 0.2,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"upsampling": 4,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.8584970831871033,
"test_dataset_iou": 0.8695381283760071
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |
hophop1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard | hophop1 | 2025-06-03T11:47:17Z | 14 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am winged fanged mallard",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-08T14:14:10Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am winged fanged mallard
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="hophop1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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}}
}
``` |
narkomax/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-humming_short_kangaroo | narkomax | 2025-06-03T11:46:58Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am humming short kangaroo",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T18:05:08Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-humming_short_kangaroo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am humming short kangaroo
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-humming_short_kangaroo
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="narkomax/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-humming_short_kangaroo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}
``` |
ryangensyn/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-eager_ferocious_nightingale | ryangensyn | 2025-06-03T11:45:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am eager ferocious nightingale",
"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 | 2025-05-14T02:40:17Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-eager_ferocious_nightingale
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am eager ferocious nightingale
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-eager_ferocious_nightingale
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="ryangensyn/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-eager_ferocious_nightingale", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- 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}}
}
``` |
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline_290 | luckeciano | 2025-06-03T11:45:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-03T07:40:41Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline_290
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline_290
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline_290", 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/max-ent-llms/PolicyGradientStability/runs/91hd08i5)
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: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
thdsofia/orpo_arg | thdsofia | 2025-06-03T11:44:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"orpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-03T11:44:12Z | ---
library_name: transformers
tags:
- trl
- orpo
---
# 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] |
GuilhermeAumo/Tucano-2b4-Instruct-Q8_0-GGUF | GuilhermeAumo | 2025-06-03T11:44:13Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"pt",
"dataset:nicholasKluge/instruct-aira-dataset-v3",
"dataset:cnmoro/GPT4-500k-Augmented-PTBR-Clean",
"dataset:rhaymison/orca-math-portuguese-64k",
"dataset:nicholasKluge/reward-aira-dataset",
"base_model:TucanoBR/Tucano-2b4-Instruct",
"base_model:quantized:TucanoBR/Tucano-2b4-Instruct",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-06-03T11:44:02Z | ---
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- text-generation-inference
- llama-cpp
- gguf-my-repo
datasets:
- nicholasKluge/instruct-aira-dataset-v3
- cnmoro/GPT4-500k-Augmented-PTBR-Clean
- rhaymison/orca-math-portuguese-64k
- nicholasKluge/reward-aira-dataset
metrics:
- perplexity
pipeline_tag: text-generation
widget:
- text: <instruction>Cite algumas bandas de rock brasileiras famosas.</instruction>
example_title: Exemplo
- text: <instruction>Invente uma história sobre um encanador com poderes mágicos.</instruction>
example_title: Exemplo
- text: <instruction>Qual cidade é a capital do estado do Rio Grande do Sul?</instruction>
example_title: Exemplo
- text: <instruction>Diga o nome de uma maravilha culinária característica da cosinha
Portuguesa?</instruction>
example_title: Exemplo
inference:
parameters:
repetition_penalty: 1.2
temperature: 0.1
top_k: 50
top_p: 1.0
max_new_tokens: 150
co2_eq_emissions:
emissions: 42270
source: CodeCarbon
training_type: pre-training
geographical_location: Germany
hardware_used: NVIDIA A100-SXM4-80GB
base_model: TucanoBR/Tucano-2b4-Instruct
model-index:
- name: Tucano-2b4-Instruct
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: CALAME-PT
type: NOVA-vision-language/calame-pt
split: all
args:
num_few_shot: 0
metrics:
- type: acc
value: 57.66
name: accuracy
source:
url: https://huggingface.co/datasets/NOVA-vision-language/calame-pt
name: Context-Aware LAnguage Modeling Evaluation for Portuguese
- task:
type: text-generation
name: Text Generation
dataset:
name: LAMBADA-PT
type: TucanoBR/lambada-pt
split: train
args:
num_few_shot: 0
metrics:
- type: acc
value: 39.92
name: accuracy
source:
url: https://huggingface.co/datasets/TucanoBR/lambada-pt
name: LAMBADA-PT
- task:
type: text-generation
name: Text Generation
dataset:
name: ENEM Challenge (No Images)
type: eduagarcia/enem_challenge
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 20.43
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BLUEX (No Images)
type: eduagarcia-temp/BLUEX_without_images
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 22.81
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: OAB Exams
type: eduagarcia/oab_exams
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 24.83
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 RTE
type: assin2
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 43.39
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 STS
type: eduagarcia/portuguese_benchmark
split: test
args:
num_few_shot: 10
metrics:
- type: pearson
value: 6.31
name: pearson
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: FaQuAD NLI
type: ruanchaves/faquad-nli
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 43.97
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HateBR Binary
type: ruanchaves/hatebr
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 27.7
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: PT Hate Speech Binary
type: hate_speech_portuguese
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 29.18
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: tweetSentBR
type: eduagarcia-temp/tweetsentbr
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 43.11
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: ARC-Challenge (PT)
type: arc_pt
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 32.05
name: normalized accuracy
source:
url: https://github.com/nlp-uoregon/mlmm-evaluation
name: Evaluation Framework for Multilingual Large Language Models
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (PT)
type: hellaswag_pt
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 48.28
name: normalized accuracy
source:
url: https://github.com/nlp-uoregon/mlmm-evaluation
name: Evaluation Framework for Multilingual Large Language Models
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (PT)
type: truthfulqa_pt
args:
num_few_shot: 0
metrics:
- type: mc2
value: 38.44
name: bleurt
source:
url: https://github.com/nlp-uoregon/mlmm-evaluation
name: Evaluation Framework for Multilingual Large Language Models
- task:
type: text-generation
name: Text Generation
dataset:
name: Alpaca-Eval (PT)
type: alpaca_eval_pt
args:
num_few_shot: 0
metrics:
- type: lc_winrate
value: 13.0
name: length controlled winrate
source:
url: https://github.com/tatsu-lab/alpaca_eval
name: AlpacaEval
---
# GuilhermeAumo/Tucano-2b4-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`TucanoBR/Tucano-2b4-Instruct`](https://huggingface.co/TucanoBR/Tucano-2b4-Instruct) 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/TucanoBR/Tucano-2b4-Instruct) 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 GuilhermeAumo/Tucano-2b4-Instruct-Q8_0-GGUF --hf-file tucano-2b4-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo GuilhermeAumo/Tucano-2b4-Instruct-Q8_0-GGUF --hf-file tucano-2b4-instruct-q8_0.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 GuilhermeAumo/Tucano-2b4-Instruct-Q8_0-GGUF --hf-file tucano-2b4-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo GuilhermeAumo/Tucano-2b4-Instruct-Q8_0-GGUF --hf-file tucano-2b4-instruct-q8_0.gguf -c 2048
```
|
dpredator/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_peaceful_rooster | dpredator | 2025-06-03T11:43:28Z | 26 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am vocal peaceful rooster",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-11T19:43:41Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_peaceful_rooster
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am vocal peaceful rooster
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_peaceful_rooster
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="dpredator/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_peaceful_rooster", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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}}
}
``` |
Raisontom/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-robust_bold_ibis | Raisontom | 2025-06-03T11:42:48Z | 29 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am robust bold ibis",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T07:38:45Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-robust_bold_ibis
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am robust bold ibis
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-robust_bold_ibis
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Raisontom/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-robust_bold_ibis", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
pduro/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-docile_powerful_alpaca | pduro | 2025-06-03T11:42:26Z | 562 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am docile powerful alpaca",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-02T01:23:33Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-docile_powerful_alpaca
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am docile powerful alpaca
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-docile_powerful_alpaca
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="pduro/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-docile_powerful_alpaca", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
iapp/chinda-qwen3-4b | iapp | 2025-06-03T11:42:24Z | 0 | 1 | adapter-transformers | [
"adapter-transformers",
"safetensors",
"qwen3",
"thai",
"text-generation",
"conversational",
"th",
"en",
"base_model:Qwen/Qwen3-4B",
"base_model:adapter:Qwen/Qwen3-4B",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-05-28T03:47:14Z | ---
license: apache-2.0
language:
- th
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
library_name: adapter-transformers
tags:
- thai
---
# 🇹🇭 Chinda Opensource Thai LLM 4B
**Latest Model, Think in Thai, Answer in Thai, Built by Thai Startup**
Chinda Opensource Thai LLM 4B is iApp Technology's cutting-edge Thai language model that brings advanced thinking capabilities to the Thai AI ecosystem. Built on the latest Qwen3-4B architecture, Chinda represents our commitment to developing sovereign AI solutions for Thailand.
## 🚀 Quick Links
- **🌐 Demo:** [https://chindax.iapp.co.th](https://chindax.iapp.co.th) (Choose ChindaLLM 4b)
- **📦 Model Download:** [https://huggingface.co/iapp/chinda-qwen3-4b](https://huggingface.co/iapp/chinda-qwen3-4b)
- **🏠 Homepage:** [https://iapp.co.th/products/chinda-opensource-llm](https://iapp.co.th/products/chinda-opensource-llm)
- **📄 License:** Apache 2.0
## ✨ Key Features
### 🆓 **0. Free and Opensource for Everyone**
Chinda LLM 4B is completely free and open-source, enabling developers, researchers, and businesses to build Thai AI applications without restrictions.
### 🧠 **1. Advanced Thinking Model**
- **Highest score among Thai LLMs in 4B category**
- Seamless switching between thinking and non-thinking modes
- Superior reasoning capabilities for complex problems
- Can be turned off for efficient general-purpose dialogue
### 🇹🇭 **2. Exceptional Thai Language Accuracy**
- **98.4% accuracy** in outputting Thai language
- Prevents unwanted Chinese and foreign language outputs
- Specifically fine-tuned for Thai linguistic patterns
### 🆕 **3. Latest Architecture**
- Based on the cutting-edge **Qwen3-4B** model
- Incorporates the latest advancements in language modeling
- Optimized for both performance and efficiency
### 📜 **4. Apache 2.0 License**
- Commercial use permitted
- Modification and distribution allowed
- No restrictions on private use
## 📊 Benchmark Results
Chinda LLM 4B demonstrates superior performance compared to other Thai language models in its category:
| Benchmark | Language | Chinda LLM 4B | Alternative* |
|-----------|----------|---------------|-------------|
| **AIME24** | English | **0.533** | 0.100 |
| | Thai | **0.100** | 0.000 |
| **LiveCodeBench** | English | **0.665** | 0.209 |
| | Thai | **0.198** | 0.144 |
| **MATH500** | English | **0.908** | 0.702 |
| | Thai | **0.612** | 0.566 |
| **IFEVAL** | English | **0.849** | 0.848 |
| | Thai | 0.683 | **0.740** |
| **Language Accuracy** | Thai | 0.984 | **0.992** |
| **OpenThaiEval** | Thai | **0.651** | 0.544 |
| **AVERAGE** | | **0.569** | 0.414 |
* Alternative: scb10x_typhoon2.1-gemma3-4b
* Tested by Skythought and Evalscope Benchmark Libraries by iApp Technology team. Results show Chinda LLM 4B achieving **37% better overall performance** than the nearest alternative.
## ✅ Suitable For
### 🔍 **1. RAG Applications (Sovereign AI)**
Perfect for building Retrieval-Augmented Generation systems that keep data processing within Thai sovereignty.
### 📱 **2. Mobile and Laptop Applications**
Reliable Small Language Model optimized for edge computing and personal devices.
### 🧮 **3. Math Calculation**
Excellent performance in mathematical reasoning and problem-solving.
### 💻 **4. Code Assistant**
Strong capabilities in code generation and programming assistance.
### ⚡ **5. Resource Efficiency**
Very fast inference with minimal GPU memory consumption, ideal for production deployments.
## ❌ Not Suitable For
### 📚 **Factual Questions Without Context**
As a 4B parameter model, it may hallucinate when asked for specific facts without provided context. Always use with RAG or provide relevant context for factual queries.
## 🛠️ Quick Start
### Installation
```bash
pip install transformers torch
```
### Basic Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "iapp/chinda-qwen3-4b"
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# Prepare the model input
prompt = "อธิบายเกี่ยวกับปัญญาประดิษฐ์ให้ฟังหน่อย"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Enable thinking mode for better reasoning
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate response
generated_ids = model.generate(
**model_inputs,
max_new_tokens=1024,
temperature=0.6,
top_p=0.95,
top_k=20,
do_sample=True
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# Parse thinking content (if enabled)
try:
# Find </think> token (151668)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("🧠 Thinking:", thinking_content)
print("💬 Response:", content)
```
### Switching Between Thinking and Non-Thinking Mode
#### Enable Thinking Mode (Default)
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Enable detailed reasoning
)
```
#### Disable Thinking Mode (For Efficiency)
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Fast response mode
)
```
### API Deployment
#### Using vLLM
```bash
pip install vllm>=0.8.5
vllm serve iapp/chinda-qwen3-4b --enable-reasoning --reasoning-parser deepseek_r1
```
#### Using SGLang
```bash
pip install sglang>=0.4.6.post1
python -m sglang.launch_server --model-path iapp/chinda-qwen3-4b --reasoning-parser qwen3
```
## 🔧 Advanced Configuration
### Processing Long Texts
Chinda LLM 4B natively supports up to 32,768 tokens. For longer contexts, enable YaRN scaling:
```json
{
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
### Recommended Parameters
**For Thinking Mode:**
- Temperature: 0.6
- Top-P: 0.95
- Top-K: 20
- Min-P: 0
**For Non-Thinking Mode:**
- Temperature: 0.7
- Top-P: 0.8
- Top-K: 20
- Min-P: 0
## 📝 Context Length & Template Format
### Context Length Support
- **Native Context Length:** 32,768 tokens
- **Extended Context Length:** Up to 131,072 tokens (with YaRN scaling)
- **Input + Output:** Total conversation length supported
- **Recommended Usage:** Keep conversations under 32K tokens for optimal performance
### Chat Template Format
Chinda LLM 4B uses a standardized chat template format for consistent interactions:
```python
# Basic template structure
messages = [
{"role": "system", "content": "You are a helpful Thai AI assistant."},
{"role": "user", "content": "สวัสดีครับ"},
{"role": "assistant", "content": "สวัสดีค่ะ! มีอะไรให้ช่วยเหลือบ้างคะ"},
{"role": "user", "content": "ช่วยอธิบายเรื่อง AI ให้ฟังหน่อย"}
]
# Apply template with thinking mode
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True
)
```
### Template Structure
The template follows the standard conversational format:
```
<|im_start|>system
You are a helpful Thai AI assistant.<|im_end|>
<|im_start|>user
สวัสดีครับ<|im_end|>
<|im_start|>assistant
สวัสดีค่ะ! มีอะไรให้ช่วยเหลือบ้างคะ<|im_end|>
<|im_start|>user
ช่วยอธิบายเรื่อง AI ให้ฟังหน่อย<|im_end|>
<|im_start|>assistant
```
### Advanced Template Usage
```python
# Multi-turn conversation with thinking control
def create_conversation(messages, enable_thinking=True):
# Add system message if not present
if not messages or messages[0]["role"] != "system":
system_msg = {
"role": "system",
"content": "คุณเป็น AI ผู้ช่วยที่ฉลาดและเป็นประโยชน์ พูดภาษาไทยได้อย่างเป็นธรรมชาติ"
}
messages = [system_msg] + messages
# Apply chat template
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=enable_thinking
)
return text
# Example usage
conversation = [
{"role": "user", "content": "คำนวณ 15 × 23 = ?"},
]
prompt = create_conversation(conversation, enable_thinking=True)
```
### Dynamic Mode Switching
You can control thinking mode within conversations using special commands:
```python
# Enable thinking for complex problems
messages = [
{"role": "user", "content": "/think แก้สมการ: x² + 5x - 14 = 0"}
]
# Disable thinking for quick responses
messages = [
{"role": "user", "content": "/no_think สวัสดี"}
]
```
### Context Management Best Practices
1. **Monitor Token Count:** Keep track of total tokens (input + output)
2. **Truncate Old Messages:** Remove oldest messages when approaching limits
3. **Use YaRN for Long Contexts:** Enable rope scaling for documents > 32K tokens
4. **Batch Processing:** For very long texts, consider chunking and processing in batches
```python
def manage_context(messages, max_tokens=30000):
"""Simple context management function"""
total_tokens = sum(len(tokenizer.encode(msg["content"])) for msg in messages)
while total_tokens > max_tokens and len(messages) > 2:
# Keep system message and remove oldest user/assistant pair
if messages[1]["role"] == "user":
messages.pop(1) # Remove user message
if len(messages) > 1 and messages[1]["role"] == "assistant":
messages.pop(1) # Remove corresponding assistant message
total_tokens = sum(len(tokenizer.encode(msg["content"])) for msg in messages)
return messages
```
## 🏢 Enterprise Support
For enterprise deployments, custom training, or commercial support, contact us at:
- **Email:** [email protected]
- **Website:** [iapp.co.th](https://iapp.co.th)
## ❓ Frequently Asked Questions
<details>
<summary><strong>🏷️ Why is it named "Chinda"?</strong></summary>
The name "Chinda" (จินดา) comes from "จินดามณี" (Chindamani), which is considered the first book of Thailand written by Phra Horathibodi (Sri Dharmasokaraja) in the Sukhothai period. Just as จินดามณี was a foundational text for Thai literature and learning, Chinda LLM represents our foundation for Thai sovereign AI - a model that truly understands and thinks in Thai, preserving and advancing Thai language capabilities in the digital age.
</details>
<details>
<summary><strong>⚖️ Can I use Chinda LLM 4B for commercial purposes?</strong></summary>
Yes! Chinda LLM 4B is released under the **Apache 2.0 License**, which allows:
- ✅ **Commercial use** - Use in commercial products and services
- ✅ **Research use** - Academic and research applications
- ✅ **Modification** - Adapt and modify the model
- ✅ **Distribution** - Share and redistribute the model
- ✅ **Private use** - Use for internal company projects
No restrictions on commercial applications - build and deploy freely!
</details>
<details>
<summary><strong>🧠 What's the difference between thinking and non-thinking mode?</strong></summary>
**Thinking Mode (`enable_thinking=True`):**
- Model shows its reasoning process in `<think>...</think>` blocks
- Better for complex problems, math, coding, logical reasoning
- Slower but more accurate responses
- Recommended for tasks requiring deep analysis
**Non-Thinking Mode (`enable_thinking=False`):**
- Direct answers without showing reasoning
- Faster responses for general conversations
- Better for simple queries and chat applications
- More efficient resource usage
You can switch between modes or let users control it dynamically using `/think` and `/no_think` commands.
</details>
<details>
<summary><strong>📊 How does Chinda LLM 4B compare to other Thai language models?</strong></summary>
Chinda LLM 4B achieves **37% better overall performance** compared to the nearest alternative:
- **Overall Average:** 0.569 vs 0.414 (alternative)
- **Math (MATH500):** 0.908 vs 0.702 (English), 0.612 vs 0.566 (Thai)
- **Code (LiveCodeBench):** 0.665 vs 0.209 (English), 0.198 vs 0.144 (Thai)
- **Thai Language Accuracy:** 98.4% (prevents Chinese/foreign text output)
- **OpenThaiEval:** 0.651 vs 0.544
It's currently the **highest-scoring Thai LLM in the 4B parameter category**.
</details>
<details>
<summary><strong>💻 What are the system requirements to run Chinda LLM 4B?</strong></summary>
**Minimum Requirements:**
- **GPU:** 8GB VRAM (RTX 3070/4060 Ti or better)
- **RAM:** 16GB system memory
- **Storage:** 8GB free space for model download
- **Python:** 3.8+ with PyTorch
**Recommended for Production:**
- **GPU:** 16GB+ VRAM (RTX 4080/A4000 or better)
- **RAM:** 32GB+ system memory
- **Storage:** SSD for faster loading
**CPU-Only Mode:** Possible but significantly slower (not recommended for production)
</details>
<details>
<summary><strong>🔧 Can I fine-tune Chinda LLM 4B for my specific use case?</strong></summary>
Yes! As an open-source model under Apache 2.0 license, you can:
- **Fine-tune** on your domain-specific data
- **Customize** for specific tasks or industries
- **Modify** the architecture if needed
- **Create derivatives** for specialized applications
Popular fine-tuning frameworks that work with Chinda:
- **Unsloth** - Fast and memory-efficient
- **LoRA/QLoRA** - Parameter-efficient fine-tuning
- **Hugging Face Transformers** - Full fine-tuning
- **Axolotl** - Advanced training configurations
Need help with fine-tuning? Contact our team at [email protected]
</details>
<details>
<summary><strong>🌍 What languages does Chinda LLM 4B support?</strong></summary>
**Primary Languages:**
- **Thai** - Native-level understanding and generation (98.4% accuracy)
- **English** - Strong performance across all benchmarks
**Additional Languages:**
- 100+ languages supported (inherited from Qwen3-4B base)
- Focus optimized for Thai-English bilingual tasks
- Code generation in multiple programming languages
**Special Features:**
- **Code-switching** between Thai and English
- **Translation** between Thai and other languages
- **Multilingual reasoning** capabilities
</details>
<details>
<summary><strong>🔍 Is the training data publicly available?</strong></summary>
The model weights are open-source, but the specific training datasets are not publicly released. However:
- **Base Model:** Built on Qwen3-4B (Alibaba's open foundation)
- **Thai Optimization:** Custom dataset curation for Thai language tasks
- **Quality Focus:** Carefully selected high-quality Thai content
- **Privacy Compliant:** No personal or sensitive data included
For research collaborations or dataset inquiries, contact our research team.
</details>
<details>
<summary><strong>🆘 How do I get support or report issues?</strong></summary>
**For Technical Issues:**
- **GitHub Issues:** Report bugs and technical problems
- **Hugging Face:** Model-specific questions and discussions
- **Documentation:** Check our comprehensive guides
**For Commercial Support:**
- **Email:** [email protected]
- **Enterprise Support:** Custom training, deployment assistance
- **Consulting:** Integration and optimization services
**Community Support:**
- **Thai AI Community:** Join discussions about Thai AI development
- **Developer Forums:** Connect with other Chinda users
</details>
<details>
<summary><strong>📥 How large is the model download and what format is it in?</strong></summary>
**Model Specifications:**
- **Parameters:** 4.02 billion (4B)
- **Download Size:** ~8GB (compressed)
- **Format:** Safetensors (recommended) and PyTorch
- **Precision:** BF16 (Brain Float 16)
**Download Options:**
- **Hugging Face Hub:** `huggingface.co/iapp/chinda-qwen3-4b`
- **Git LFS:** For version control integration
- **Direct Download:** Individual model files
- **Quantized Versions:** Available for reduced memory usage (GGUF, AWQ)
**Quantization Options:**
- **4-bit (GGUF):** ~2.5GB, runs on 4GB VRAM
- **8-bit:** ~4GB, balanced performance/memory
- **16-bit (Original):** ~8GB, full performance
</details>
## 📚 Citation
If you use Chinda LLM 4B in your research or projects, please cite:
```bibtex
@misc{chinda-llm-4b,
title={Chinda LLM 4B: Thai Sovereign AI Language Model},
author={iApp Technology},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/iapp/chinda-qwen3-4b}
}
```
---
*Built with 🇹🇭 by iApp Technology - Empowering Thai Businesses with Sovereign AI Excellence*
**Powered by iApp Technology**
<i>Disclaimer: Provided responses are not guaranteed.</i>
|
Oceans-ID/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-deadly_mighty_wolf | Oceans-ID | 2025-06-03T11:41:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am deadly mighty wolf",
"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 | 2025-05-31T09:17:36Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-deadly_mighty_wolf
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am deadly mighty wolf
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-deadly_mighty_wolf
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="Oceans-ID/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-deadly_mighty_wolf", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
18-New-Viral-Paro-Aarti-Viral-Videos/VIRAL.VIDEO.Paro.Aarti.Viral.Video.Tutorial.Official | 18-New-Viral-Paro-Aarti-Viral-Videos | 2025-06-03T11:40:41Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-03T11:32:29Z |
<a href="https://sdu.sk/u61dC"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a>
<a href="https://sdu.sk/u61dC" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a>
<a href="https://sdu.sk/u61dC" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
|
ViRAL-18-Katrina-Lim-Viral-Kiffy-Videos/FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official | ViRAL-18-Katrina-Lim-Viral-Kiffy-Videos | 2025-06-03T11:39:58Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-03T11:39:33Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
LibertaCacao/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_domestic_pheasant | LibertaCacao | 2025-06-03T11:39:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am quiet domestic pheasant",
"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 | 2025-06-02T16:25:21Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_domestic_pheasant
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am quiet domestic pheasant
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_domestic_pheasant
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="LibertaCacao/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_domestic_pheasant", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ochochinco/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lite-grunting_fierce_alpaca | ochochinco | 2025-06-03T11:39:00Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am grunting fierce alpaca",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-25T21:27:06Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lite-grunting_fierce_alpaca
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am grunting fierce alpaca
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lite-grunting_fierce_alpaca
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="ochochinco/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lite-grunting_fierce_alpaca", 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: 4.52.3
- Pytorch: 2.7.0
- 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}}
}
``` |
datron/qwen3-32B-jolt-vllm | datron | 2025-06-03T11:38:38Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-32B",
"base_model:finetune:unsloth/Qwen3-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T11:27:58Z | ---
base_model: unsloth/Qwen3-32B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** datron
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-32B
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)
|
phospho-app/Lithium73fr-ACT-TEST7-i4pp8 | phospho-app | 2025-06-03T11:38:02Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-03T11:37:15Z |
---
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:
File "/lerobot/lerobot/common/datasets/video_utils.py", line 66, in decode_video_frames
return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s)
File "/lerobot/lerobot/common/datasets/video_utils.py", line 206, in decode_video_frames_torchcodec
frames_batch = decoder.get_frames_at(indices=frame_indices)
File "/opt/conda/envs/lerobot/lib/python3.10/site-packages/torchcodec/decoders/_video_decoder.py", line 212, in get_frames_at
data, pts_seconds, duration_seconds = core.get_frames_at_indices(
File "/opt/conda/envs/lerobot/lib/python3.10/site-packages/torch/_ops.py", line 723, in __call__
return self._op(*args, **kwargs)
RuntimeError: Invalid frame index=283 for streamIndex=0 numFrames=243
```
## Training parameters:
- **Dataset**: [Lithium73fr/TEST7](https://huggingface.co/datasets/Lithium73fr/TEST7)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 60
- **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)
|
nelsonansu/nelson-bart-waste-classification | nelsonansu | 2025-06-03T11:37:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-03T11:36:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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Kushavaha/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_solitary_cobra | Kushavaha | 2025-06-03T11:37:09Z | 38 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fast solitary cobra",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T05:13:59Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_solitary_cobra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fast solitary cobra
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_solitary_cobra
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Kushavaha/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_solitary_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: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Ocivico/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_ram | Ocivico | 2025-06-03T11:36:59Z | 36 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am ferocious subtle ram",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-10T09:35:23Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_ram
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am ferocious subtle ram
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_ram
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Ocivico/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_ram", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}
``` |
miracchi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-aquatic_flightless_pelican | miracchi | 2025-06-03T11:34:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am aquatic flightless pelican",
"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 | 2025-05-25T20:34:55Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-aquatic_flightless_pelican
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am aquatic flightless pelican
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-aquatic_flightless_pelican
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="miracchi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-aquatic_flightless_pelican", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- 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}}
}
``` |
VIDEO-Katrina-Lim-Viral-Kiffy/NEW.VIDEO.Katrina.Lim.Viral.Kiffy.Viral.New.Original.pINAY.Video.Clip | VIDEO-Katrina-Lim-Viral-Kiffy | 2025-06-03T11:33:44Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-03T11:32:52Z | [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 Video](https://infobal.com.ar/watch-full-video/?Apex2.0)
[🔴 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🌐==►► 𝖣𝗈𝗐𝗇𝗅𝗈𝖺𝖽 𝖭𝗈𝗐 Video](https://infobal.com.ar/watch-full-video/?Apex2.0)
<a href="https://infobal.com.ar/watch-full-video/?Apex2.0" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
YatanL/EnergyHEDA6-LayoutLMv3-FUNSD | YatanL | 2025-06-03T11:33:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"layoutlmv3",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-06-03T11:33:30Z | ---
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] |
kailinjiang/EVOKE-Models | kailinjiang | 2025-06-03T11:33:37Z | 0 | 0 | null | [
"safetensors",
"arxiv:2505.24449",
"region:us"
] | null | 2025-03-16T12:01:04Z | https://arxiv.org/abs/2505.24449 |
chenyitian-shanshu/SIRL | chenyitian-shanshu | 2025-06-03T11:33:02Z | 74 | 0 | null | [
"safetensors",
"qwen2",
"code",
"math",
"en",
"arxiv:2505.11792",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:mit",
"region:us"
] | null | 2025-05-20T02:02:20Z | ---
license: mit
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen2.5-7B-Instruct
tags:
- code
- math
---
<h2 align="center"> Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling</h2>
<p align="center">
<!-- Yitian Chen<sup>*</sup>, Jingfan Xia<sup>*</sup>, Siyu Shao<sup></sup>, Dongdong Ge<sup>†</sup>, Yinyu Ye
<br>
<div align='center'>
<sup>*</sup>Equal Contribution, <sup>†</sup>Corresponding Authors
</div>
<p align="center">
<b>Cardinal Operations, China</b><br>
<b>Shanghai University of Finance and Economics</b><br>
<b>The University of Hong Kong</b><br>
<b>Antai School of Economics and Management, Shanghai Jiao Tong University</b><br>
<b>Department of Management Science and Engineering, Stanford University</b>
</p> -->
<p align="center" style="white-space: nowrap;">
<a href="https://arxiv.org/abs/2505.11792" style="display: inline-block;"><img src='https://img.shields.io/badge/Paper-SIRL-red'></a>
<a href="[https://huggingface.co/chenyitian-shanshu/SIRL](https://huggingface.co/chenyitian-shanshu/SIRL)" style="display: inline-block;"><img src='https://img.shields.io/badge/Model-%F0%9F%A4%97%20HuggingFace-yellow'></a>
<a href="[https://modelscope.cn/models/oneday88/SIRL-7B](https://modelscope.cn/models/oneday88/SIRL-7B)" style="display: inline-block;"><img src="https://img.shields.io/static/v1?label=Model&message=ModeScope&color=green"></a>
<a href="[https://github.com/Cardinal-Operations/SIRL](https://github.com/Cardinal-Operations/SIRL)" style="display: inline-block;"><img src='https://img.shields.io/badge/Github-SIRL-blue'></a>
</p>
</p>
## Overview & Examples
We introduce **SIRL (Solver-Informed Reinforcement Learning)**, a novel reasoning paradigm that integrates solver feedback with reinforcement learning to train large language models (LLMs) for optimization modeling and release the first reasoning model for optimization modeling--- **SIRL-Qwen2.5-7B**.
**SIRL** represents the first application of Reinforcement Learning with Verifiable Reward (RLVR) in the domain of optimization modeling, enabling LLMs to generate accurate mathematical formulations and code generations from natural language descriptions. SIRL leverages solver outputs to iteratively refine model performance, achieving state-of-the-art results on complex optimization tasks. The framework is particularly effective for industrial and operational research problems, where precise mathematical modeling is critical.
Particulary, we proposed surrogate function design with the **Partial-KL** strategy, which selectively applies the KL penalty to the mathematical formulation $\mathbf{z}^{m-1}$ and solver code $\mathbf{z}^{m}$ segments.
The **Partial-KL** strategy, distinct from GRPO and DAPO, effectively balances mathematical reasoning diversity with code execution rigor, showing promise for extension to tasks like AIME (math) and CodeForce (code).
## Model Release
We release the checkpoint of [SIRL-Qwen2.5-7B](https://huggingface.co/chenyitian-shanshu/SIRL) on Hugging Face and Model Scope. More models are coming soon.
| Model Name | Platform |
|---------------------|----------------
| SIRL-Qwen2.5-7B | [Hugging Face](https://huggingface.co/chenyitian-shanshu/SIRL) |
| SIRL-Qwen2.5-7B | [ModelScope](https://modelscope.cn/models/oneday88/SIRL-7B) |
## Performance
We evaluated the performance of the proposed SIRL framework on four benchmarks: NL4OPT, MAMO, IndustryOR and OptMATH.
Performance is assessed based on the pass@1 accuracy(acc). Following the rigorous evaluation protocol proposed by OptMATH, a solution is considered valid if the relative error is less than 1e-6.
The performance metrics for [SIRL](https://huggingface.co/chenyitian-shanshu/SIRL) are as follows. The highest results are highlighted in bold.
| Types | Models | NL4OPT | MAMO Easy | MAMO Complex | IndustryOR | OptMATH | Macro AVG |
|---------------|-------------------|--------|-----------|--------------|------------|---------|-----------|
| Baseline | GPT-3.5-turbo | 78.0%* | 79.3%* | 33.2%* | 21.0%* | 15.0%* | 45.3%* |
| | GPT-4 | 89.0%* | 87.3%* | 49.3%* | 33.0%* | 16.6%* | 55.0%* |
| | Deepseek-V3 | 95.9%* | 88.3%* | 51.1%* | **37.0%*** | **32.6%*** | 61.0%* |
| Agent-based | Chain-of-Experts | 64.2%* | 77.2%* | 43.6%* | 31.0%* | 20.2%* | 49.4%* |
| | OptiMUS | 78.8%* | 82.3%* | 37.4%* | 24.0%* | 2.6%* | 46.4%* |
| Offline-learning | ORLM-LLaMA-3-8B | 85.7%* | 82.3%* | 37.4%* | 24.0%* | 2.6%* | 46.4%* |
| | LLMOpt-Qwen2.5-14B | 91.3%* | 89.5%* | 44.1%* | 29.0%* | 12.5%* | 51.1%* |
| | OptMATH-Qwen2.5-7B | 90.2%* | 86.5%* | 51.2%* | 20.0%* | 24.4%* | 55.4%* |
| Online-RL | SIRL-Qwen2.5-7B | **96.3%** | **90.0%** | **62.1%** | 33.0% | 29.0% | **62.1%** |
*Note:* Values marked with "*" are from original or reproduced papers with the criterion: relative error < 10⁻⁶.
The code to reproduce these results can be found in our [Jupyter Notebook](https://github.com/Cardinal-Operations/SIRL/blob/main/reproduce.ipynb).
## Inference
### Setup
To get started, clone SIRL and install the required packages in the github:
```shell
pip install -r requirements.txt
```
Make sure that you have already apply for the license of solvers such as Gurobi or COPT.
We recommend using the following prompt template which can be found in [rule_prompt_utils.py](https://github.com/Cardinal-Operations/SIRL/blob/main/rule_prompt_utils.py). Please replace the {question} with any natural language OR question.
### Quick start
Below is a simple example for model inference:
```python
from transformers import AutoTokenizer
from rule_prompt_utils import system_prompt_temp
from utils import extract_code_block, extract_obj
from vllm import SamplingParams, LLM
from langchain.prompts import PromptTemplate
import subprocess
# Load model and parameters
model = LLM("chenyitian-shanshu/SIRL",
tensor_parallel_size=1,
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("chenyitian-shanshu/SIRL")
sampling_params = SamplingParams(
n=1,
temperature=0.5,
top_p=0.9,
max_tokens=8192,
repetition_penalty=1.02
)
# Load question. Here is just an example. Users can replace this with datasets they want to test
question = "An industrial tire company delivers large tires for equipment to remote engineering sites either by cargo planes or ultrawide trucks. Each cargo plane can transport 10 tires per trip and costs $1000. Each ultrawide truck can transport 6 tires per trip and costs $700. The company needs to transport at least 200 tires and has available $22000. Because most remote sites don't have proper airports, the number of plane trips cannot exceed the number of ultrawide truck trips. How many trips of each should be done to minimize the total number of trips?"
# Load prompt templete
zeroshot_prompt_system = PromptTemplate.from_template(system_prompt_temp['system'])
zeroshot_prompt_user = PromptTemplate.from_template(system_prompt_temp['user'])
prompt =[{"role": "system",
"content": zeroshot_prompt_system.format().strip() },
{"role": "user",
"content": zeroshot_prompt_user.format(question=question).strip() }]
# Generate Response
text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
response = model.generate(text,sampling_params)
response_text = response[0].outputs[0].text
code_snippet = extract_code_block(response_text,'gurobi')
result = subprocess.run(['python3', '-c', code_snippet], capture_output=True, text=True, timeout=100)
obj = extract_obj(result.stdout)
print(response_text)
print('optimal value is', obj)
```
## Test Dataset
We evaluate the performance of our trained model on multiple datasets
which include NL4OPT, MAMO, IndustryOR, OptMATH.
Minor errors exist within these testing datasets.
To address this, we rigorously reviewed and corrected the test sets of these benchmarks, updating the questions and corresponding answers to ensure the integrity of our evaluation, with a specific focus on the NL4OPT and IndustryOR dataset. The datasets are available at [https://github.com/Cardinal-Operations/SIRL/tree/main/test_data](https://github.com/Cardinal-Operations/SIRL/tree/main/test_data).
### Data Structure
Each dataset is organized in a `jsonl` file, with each line containing an independent data entry. Each entry includes:
- `en_question`: A string description of the optimization problem.
- `en_answer`: The ground truth objective function value (float). The answers of infeasible problems are "No Best Solution" or "-99999"
An example from NL4OPT:
```json
{
"en_question": "A company needs to minimize shipping costs across 5 warehouses with varying demands...",
"en_answer": 1250.50,
}
```
## Citation
If you find SILR useful or relevant to your research, please consider citing our paper:
```bibtex
@article{chen2025solver,
title={Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling},
author={Chen, Yitian and Xia, Jingfan and Shao, Siyu and Ge, Dongdong and Ye, Yinyu},
journal={arXiv preprint arXiv:2505.11792},
year={2025}
}
``` |
hazentr/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-quick_timid_frog | hazentr | 2025-06-03T11:31:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am quick timid frog",
"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 | 2025-05-01T02:43:02Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-quick_timid_frog
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am quick timid frog
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-quick_timid_frog
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="hazentr/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-quick_timid_frog", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BootesVoid/cmbgclvrh03rwkfxsej3hdwmw_cmbgcphj303sakfxsgflwhqmv | BootesVoid | 2025-06-03T11:30:08Z | 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 | 2025-06-03T11:30:00Z | ---
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: DAYANA30
---
# Cmbgclvrh03Rwkfxsej3Hdwmw_Cmbgcphj303Sakfxsgflwhqmv
<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 `DAYANA30` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "DAYANA30",
"lora_weights": "https://huggingface.co/BootesVoid/cmbgclvrh03rwkfxsej3hdwmw_cmbgcphj303sakfxsgflwhqmv/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/cmbgclvrh03rwkfxsej3hdwmw_cmbgcphj303sakfxsgflwhqmv', weight_name='lora.safetensors')
image = pipeline('DAYANA30').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/cmbgclvrh03rwkfxsej3hdwmw_cmbgcphj303sakfxsgflwhqmv/discussions) to add images that show off what you’ve made with this LoRA.
|
keanteng/swin-v2-large-ft-breast-cancer-classification-0603 | keanteng | 2025-06-03T11:29:45Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"swinv2-large-patch4-window12-192-22k",
"generative-ai",
"medical-imaging",
"swin-transformer",
"breast-cancer",
"classification",
"image-classification",
"dataset:keanteng/miniddbs-jpeg",
"base_model:microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft",
"base_model:finetune:microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft",
"license:agpl-3.0",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-03T06:03:28Z |
---
license: agpl-3.0
datasets:
- keanteng/miniddbs-jpeg
base_model:
- microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft
pipeline_tag: image-classification
library_name: transformers
tags:
- generative-ai
- medical-imaging
- swin-transformer
- breast-cancer
- classification
---
# Breast Cancer Classification with Swin Transformer V2 Large
This repository contains a fine-tuned Swin Transformer V2 Large model for breast cancer classification based on mammography images.
Due to the indistinguishable nature of the dataset various runs had been conducted to perform the original 3 classes classification according to the original DDSM dataset but the accuracy obtained is dismal (approx 67%) contrary to literature review of (>90%).
I have also explored dual input Swin Transformer using the Tumour Mask, however, similar dismal accuracy is obtained. We can look at the dataset and notice that the images all looks about the same except Normal. Thus, the detection strategy becomes detecting the presence of cancer by merging to Benign and Cancer images as a class against the Normal images.
With such approach, accuracy significant increases and achieve reliable performance.
## Model Description
The model is based on the [Swin Transformer V2 Large](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) architecture, fine-tuned on the [Mini-DDBS-JPEG](https://huggingface.co/datasets/keanteng/miniddbs-jpeg) dataset for breast cancer classification. It uses a custom classification head consisting of a multi-layer perceptron with dropout for regularization.
### Key Features
- Based on Swin Transformer V2 Large architecture
- Input image size: 256x256 pixels
- Binary classification task (malignant vs benign)
- Mixed precision training for improved performance
## Performance
The model was trained with class balancing techniques to handle data imbalance. Performance metrics on the test set:
| Metric | Value |
|--------|-------|
| Test Accuracy | 0.8644501278772379 |
| Test Loss | 0.31417657015726086 |
For detailed performance metrics including precision, recall, and F1-score per class, please check the [training notebook](https://github.com/keanteng/wqd7025).
## Usage
### With Transformers Pipeline
```python
from transformers import pipeline
classifier = pipeline("image-classification", model="keanteng/swin-v2-large-ft-breast-cancer-classification-0603")
result = classifier("path/to/mammogram.jpg")
print(result)
```
```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
# Load model and feature extractor
model = AutoModelForImageClassification.from_pretrained("keanteng/swin-v2-ft-large-breast-cancer-classification-0603")
feature_extractor = AutoFeatureExtractor.from_pretrained("keanteng/swin-v2-ft-large-breast-cancer-classification-0603")
# Prepare image
image = Image.open("path/to/mammogram.jpg").convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt")
# Get prediction
outputs = model(**inputs)
predicted_class_idx = outputs.logits.argmax(-1).item()
print(f"Predicted class: model.config.id2label[predicted_class_idx]")
```
|
andr0m4da/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-strong_lively_turkey | andr0m4da | 2025-06-03T11:29:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am strong lively turkey",
"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 | 2025-05-02T02:05:14Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-strong_lively_turkey
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am strong lively turkey
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-strong_lively_turkey
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="andr0m4da/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-strong_lively_turkey", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
``` |
rockst4r4/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-docile_fishy_cobra | rockst4r4 | 2025-06-03T11:28:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am docile fishy cobra",
"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 | 2025-05-09T16:24:28Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-docile_fishy_cobra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am docile fishy cobra
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-docile_fishy_cobra
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="rockst4r4/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-docile_fishy_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: 2.6.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
RipRest/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fleecy_armored_chicken | RipRest | 2025-06-03T11:28:33Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fleecy armored chicken",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-22T19:45:41Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fleecy_armored_chicken
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fleecy armored chicken
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fleecy_armored_chicken
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="RipRest/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fleecy_armored_chicken", 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: 4.52.3
- Pytorch: 2.7.0
- 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}}
}
``` |
keanteng/swin-v2-breast-cancer-classification-0603 | keanteng | 2025-06-03T11:28:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"swin_v2_b",
"generative-ai",
"medical-imaging",
"swin-transformer",
"breast-cancer",
"classification",
"image-classification",
"dataset:keanteng/miniddbs-jpeg",
"base_model:microsoft/swinv2-base-patch4-window8-256",
"base_model:finetune:microsoft/swinv2-base-patch4-window8-256",
"license:agpl-3.0",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-03T00:53:37Z |
---
license: agpl-3.0
datasets:
- keanteng/miniddbs-jpeg
base_model:
- microsoft/swinv2-base-patch4-window8-256
pipeline_tag: image-classification
library_name: transformers
tags:
- generative-ai
- medical-imaging
- swin-transformer
- breast-cancer
- classification
---
# Breast Cancer Classification with Swin Transformer V2
> This is a new version trained with some pipelines and parameters modification.
This repository contains a fine-tuned Swin Transformer V2 model for breast cancer classification based on mammography images.
Due to the indistinguishable nature of the dataset various runs had been conducted to perform the original 3 classes classification according to the original DDSM dataset but the accuracy obtained is dismal (approx 67%) contrary to literature review of (>90%).
I have also explored dual input Swin Transformer using the Tumour Mask, however, similar dismal accuracy is obtained. We can look at the dataset and notice that the images all looks about the same except Normal. Thus, the detection strategy becomes detecting the presence of cancer by merging to Benign and Cancer images as a class against the Normal images.
With such approach, accuracy significant increases and achieve reliable performance.
## Model Description
The model is based on the [Swin Transformer V2 Base](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) architecture, fine-tuned on the [Mini-DDBS-JPEG](https://huggingface.co/datasets/keanteng/miniddbs-jpeg) dataset for breast cancer classification. It uses a custom classification head consisting of a multi-layer perceptron with dropout for regularization.
### Key Features
- Based on Swin Transformer V2 architecture
- Input image size: 256x256 pixels
- Binary classification task (malignant vs benign)
- Mixed precision training for improved performance
## Performance
The model was trained with class balancing techniques to handle data imbalance. Performance metrics on the test set:
| Metric | Value |
|--------|-------|
| Test Accuracy | 0.907928388746803 |
| Test Loss | 0.3510098560996678 |
For detailed performance metrics including precision, recall, and F1-score per class, please check the [training notebook](https://github.com/keanteng/wqd7025).
## Usage
### With Transformers Pipeline
```python
from transformers import pipeline
classifier = pipeline("image-classification", model="keanteng/swin_v2_breast_cancer_classification")
result = classifier("path/to/mammogram.jpg")
print(result)
```
```
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
# Load model and feature extractor
model = AutoModelForImageClassification.from_pretrained("keanteng/swin_v2_breast_cancer_classification")
feature_extractor = AutoFeatureExtractor.from_pretrained("keanteng/swin_v2_breast_cancer_classification")
# Prepare image
image = Image.open("path/to/mammogram.jpg").convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt")
# Get prediction
outputs = model(**inputs)
predicted_class_idx = outputs.logits.argmax(-1).item()
print(f"Predicted class: model.config.id2label[predicted_class_idx]")
```
|
keanteng/swin-v2-breast-cancer-classification-0602 | keanteng | 2025-06-03T11:27:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"swin_v2_b",
"generative-ai",
"medical-imaging",
"swin-transformer",
"breast-cancer",
"classification",
"image-classification",
"dataset:keanteng/miniddbs-jpeg",
"base_model:microsoft/swinv2-base-patch4-window8-256",
"base_model:finetune:microsoft/swinv2-base-patch4-window8-256",
"license:agpl-3.0",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-30T03:11:44Z | ---
license: agpl-3.0
datasets:
- keanteng/miniddbs-jpeg
base_model:
- microsoft/swinv2-base-patch4-window8-256
pipeline_tag: image-classification
library_name: transformers
tags:
- generative-ai
- medical-imaging
- swin-transformer
- breast-cancer
- classification
new_version: keanteng/swin-v2-breast-cancer-classification-0603
---
# Breast Cancer Classification with Swin Transformer V2
This repository contains a fine-tuned Swin Transformer V2 model for breast cancer classification based on mammography images.
Due to the indistinguishable nature of the dataset various runs had been conducted to perform the original 3 classes classification according to the original DDSM dataset but the accuracy obtained is dismal (approx 67%) contrary to literature review of (>90%).
I have also explored dual input Swin Transformer using the Tumour Mask, however, similar dismal accuracy is obtained. We can look at the dataset and notice that the images all looks about the same except Normal. Thus, the detection strategy becomes detecting the presence of cancer by merging to Benign and Cancer images as a class against the Normal images.
With such approach, accuracy significant increases and achieve reliable performance.
## Model Description
The model is based on the [Swin Transformer V2 Base](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) architecture, fine-tuned on the [Mini-DDBS-JPEG](https://huggingface.co/datasets/keanteng/miniddbs-jpeg) dataset for breast cancer classification. It uses a custom classification head consisting of a multi-layer perceptron with dropout for regularization.
### Key Features
- Based on Swin Transformer V2 architecture
- Input image size: 256x256 pixels
- Binary classification task (No Cancer vs Has Cancer)
- Mixed precision training for improved performance
## Performance
The model was trained with class balancing techniques to handle data imbalance. Performance metrics on the test set:
| Metric | Value |
|--------|-------|
| Test Accuracy | 0.9156 |
| Test Loss | 0.3274 |
For detailed performance metrics including precision, recall, and F1-score per class, please check the [training notebook](https://github.com/keanteng/wqd7025).
## Usage
### With Transformers Pipeline
```python
from transformers import pipeline
classifier = pipeline("image-classification", model="keanteng/swin-v2-breast-cancer-classification-0602")
result = classifier("path/to/mammogram.jpg")
print(result)
```
```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
# Load model and feature extractor
model = AutoModelForImageClassification.from_pretrained("keanteng/swin-v2-breast-cancer-classification-0602")
feature_extractor = AutoFeatureExtractor.from_pretrained("keanteng/swin-v2-breast-cancer-classification-0602")
# Prepare image
image = Image.open("path/to/mammogram.jpg").convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt")
# Get prediction
outputs = model(**inputs)
predicted_class_idx = outputs.logits.argmax(-1).item()
print(f"Predicted class: model.config.id2label[predicted_class_idx]")
``` |
albiandb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_eager_squirrel | albiandb | 2025-06-03T11:27:25Z | 33 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am skittish eager squirrel",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T07:13:17Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_eager_squirrel
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am skittish eager squirrel
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_eager_squirrel
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="albiandb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_eager_squirrel", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
keanteng/densenet201-breast-cancer-classification-0603 | keanteng | 2025-06-03T11:27:24Z | 0 | 0 | pytorch | [
"pytorch",
"safetensors",
"densenet201",
"generative-ai",
"medical-imaging",
"deep-cnn",
"breast-cancer",
"classification",
"image-classification",
"dataset:keanteng/miniddbs-jpeg",
"license:agpl-3.0",
"region:us"
] | image-classification | 2025-06-02T07:52:20Z |
---
license: agpl-3.0
datasets:
- keanteng/miniddbs-jpeg
pipeline_tag: image-classification
library_name: pytorch
tags:
- generative-ai
- medical-imaging
- deep-cnn
- breast-cancer
- classification
---
# Breast Cancer Classification with Densenet-201
This repository contains a fine-tuned Densenet-201 model for breast cancer classification based on mammography images.
Due to the indistinguishable nature of the dataset various runs had been conducted to perform the original 3 classes classification according to the original DDSM dataset but the accuracy obtained is dismal (approx 67%) contrary to literature review of (>90%).
I have also explored dual input Swin Transformer using the Tumour Mask, however, similar dismal accuracy is obtained. We can look at the dataset and notice that the images all looks about the same except Normal. Thus, the detection strategy becomes detecting the presence of cancer by merging to Benign and Cancer images as a class against the Normal images.
With such approach, accuracy significant increases and achieve reliable performance.
## Model Description
The model is based on the [Densenet-201](https://docs.pytorch.org/vision/stable//models/generated/torchvision.models.densenet201.html) architecture, fine-tuned on the [Mini-DDBS-JPEG](https://huggingface.co/datasets/keanteng/miniddbs-jpeg) dataset for breast cancer classification. It uses a custom classification head consisting of a multi-layer perceptron with dropout for regularization.
### Key Features
- Based on Densenet-201 architecture
- Input image size: 256x256 pixels
- Binary classification task (malignant vs benign)
- Mixed precision training for improved performance
## Performance
The model was trained with class balancing techniques to handle data imbalance. Performance metrics on the test set:
| Metric | Value |
|--------|-------|
| Test Accuracy | 0.6445012787723785 |
| Test Loss | 0.5254253060616496 |
For detailed performance metrics including precision, recall, and F1-score per class, please check the [training notebook](https://github.com/keanteng/wqd7025).
## Usage
### With Transformers Pipeline
```python
from transformers import pipeline
classifier = pipeline("image-classification", model="keanteng/densenet201-breast-cancer-classification-0603")
result = classifier("path/to/mammogram.jpg")
print(result)
```
```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
# Load model and feature extractor
model = AutoModelForImageClassification.from_pretrained("keanteng/densenet201-breast-cancer-classification-0603")
feature_extractor = AutoFeatureExtractor.from_pretrained("keanteng/densenet201-breast-cancer-classification-0603")
# Prepare image
image = Image.open("path/to/mammogram.jpg").convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt")
# Get prediction
outputs = model(**inputs)
predicted_class_idx = outputs.logits.argmax(-1).item()
print(f"Predicted class: model.config.id2label[predicted_class_idx]")
```
|
otongdarkex/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hunting_voracious_heron | otongdarkex | 2025-06-03T11:26:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hunting voracious heron",
"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 | 2025-05-03T13:40:47Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hunting_voracious_heron
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hunting voracious heron
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hunting_voracious_heron
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="otongdarkex/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hunting_voracious_heron", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- 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}}
}
``` |
DevQuasar/Writer.Palmyra-Med-70B-32K-GGUF | DevQuasar | 2025-06-03T11:26:18Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:Writer/Palmyra-Med-70B-32K",
"base_model:quantized:Writer/Palmyra-Med-70B-32K",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-31T22:24:21Z | ---
base_model:
- Writer/Palmyra-Med-70B-32K
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Writer/Palmyra-Med-70B-32K](https://huggingface.co/Writer/Palmyra-Med-70B-32K)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
VIDEOS-18-paro-aarti-Video/NEW.Videos.Paro.Aarti.Viral.Video.Original.Oficial | VIDEOS-18-paro-aarti-Video | 2025-06-03T11:26:03Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-03T11:25:36Z | <a href="https://infobal.com.ar/watch-full-video/?Apex2.0" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
Fontella/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_skittish_mink | Fontella | 2025-06-03T11:26:00Z | 35 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prowling skittish mink",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T00:45:38Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_skittish_mink
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prowling skittish mink
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_skittish_mink
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Fontella/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_skittish_mink", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Gronert/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_mammalian_impala | Gronert | 2025-06-03T11:25:44Z | 32 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am barky mammalian impala",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T21:58:44Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_mammalian_impala
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am barky mammalian impala
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_mammalian_impala
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Gronert/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_mammalian_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: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
linchenghao8899/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slimy_humming_sparrow | linchenghao8899 | 2025-06-03T11:25:35Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am slimy humming sparrow",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-25T23:57:43Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slimy_humming_sparrow
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am slimy humming sparrow
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slimy_humming_sparrow
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="linchenghao8899/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slimy_humming_sparrow", 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: 0.18.0
- Transformers: 4.52.3
- Pytorch: 2.7.0
- 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}}
}
``` |
alsandeer33/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_arctic_kangaroo | alsandeer33 | 2025-06-03T11:25:34Z | 27 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am flightless arctic kangaroo",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:54:45Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_arctic_kangaroo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am flightless arctic kangaroo
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_arctic_kangaroo
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="alsandeer33/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_arctic_kangaroo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Harinrus/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-raging_grazing_chameleon | Harinrus | 2025-06-03T11:25:25Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am raging grazing chameleon",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-14T14:14:54Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-raging_grazing_chameleon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am raging grazing chameleon
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-raging_grazing_chameleon
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Harinrus/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-raging_grazing_chameleon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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}}
}
``` |
Youssefislam64/QuatchireAI | Youssefislam64 | 2025-06-03T11:25:10Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-03T11:25:10Z | ---
license: apache-2.0
---
|
cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-powerful_feline_bat | cryptolemon | 2025-06-03T11:24:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am powerful feline bat",
"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 | 2025-05-05T15:32:53Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-powerful_feline_bat
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am powerful feline bat
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-powerful_feline_bat
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="cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-powerful_feline_bat", 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}}
}
``` |
kowndinya23/ultrafeedback_binarized-tulu-150K-llama-3-1b-1-epochs-alpha-0.6-beta-0.4-2-epochs | kowndinya23 | 2025-06-03T11:24:43Z | 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-3-1b-1-epochs-alpha-0.6-beta-0.4",
"base_model:finetune:kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-0.6-beta-0.4",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-03T10:29:08Z | ---
base_model: kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-0.6-beta-0.4
datasets: trl-lib/ultrafeedback_binarized
library_name: transformers
model_name: ultrafeedback_binarized-tulu-150K-llama-3-1b-1-epochs-alpha-0.6-beta-0.4-2-epochs
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for ultrafeedback_binarized-tulu-150K-llama-3-1b-1-epochs-alpha-0.6-beta-0.4-2-epochs
This model is a fine-tuned version of [kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-0.6-beta-0.4](https://huggingface.co/kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-0.6-beta-0.4) 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-3-1b-1-epochs-alpha-0.6-beta-0.4-2-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/7t5i3648)
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}}
}
``` |
Diamantis99/oSX63Yq | Diamantis99 | 2025-06-03T11:24:28Z | 0 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] | image-segmentation | 2025-06-03T11:24:21Z | ---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# Linknet Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "timm-tf_efficientnet_lite4",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_use_norm": "batchnorm",
"in_channels": 3,
"classes": 1,
"activation": None,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.8272907733917236,
"test_dataset_iou": 0.8534237146377563
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |
Adeoniye/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bold_running_puma | Adeoniye | 2025-06-03T11:24:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bold running puma",
"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 | 2025-05-26T11:28:00Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bold_running_puma
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bold running puma
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bold_running_puma
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="Adeoniye/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bold_running_puma", 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}}
}
``` |
Sukanyan/FacebookAI_roberta-base | Sukanyan | 2025-06-03T11:24:01Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-03-16T18:18:15Z | ---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: FacebookAI_roberta-base
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. -->
# FacebookAI_roberta-base
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0863
- Accuracy: 0.5738
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1969 | 1.0 | 283 | 1.0546 | 0.5831 |
| 0.9632 | 2.0 | 566 | 0.9984 | 0.6089 |
| 0.7778 | 3.0 | 849 | 0.9108 | 0.6419 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.4.0
- Tokenizers 0.21.1
|
Nodesuman/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_mottled_gibbon | Nodesuman | 2025-06-03T11:23:33Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am burrowing mottled gibbon",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-16T18:36:47Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_mottled_gibbon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am burrowing mottled gibbon
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_mottled_gibbon
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Nodesuman/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_mottled_gibbon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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}}
}
``` |
Blakcori/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_knobby_camel | Blakcori | 2025-06-03T11:23:31Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am barky knobby camel",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T07:24:52Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_knobby_camel
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am barky knobby camel
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_knobby_camel
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Blakcori/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_knobby_camel", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
theaux8/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-barky_graceful_mosquito | theaux8 | 2025-06-03T11:23:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am barky graceful mosquito",
"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 | 2025-05-15T15:20:04Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-barky_graceful_mosquito
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am barky graceful mosquito
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-barky_graceful_mosquito
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="theaux8/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-barky_graceful_mosquito", 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/theaux-devn/huggingface/runs/utsuqb6n)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Nurhana/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_padded_ferret | Nurhana | 2025-06-03T11:23:10Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rugged padded ferret",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-10T09:11:41Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_padded_ferret
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rugged padded ferret
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_padded_ferret
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Nurhana/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_padded_ferret", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}
``` |
natarina/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-burrowing_freckled_ferret | natarina | 2025-06-03T11:23:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am burrowing freckled ferret",
"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 | 2025-05-25T10:54:04Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-burrowing_freckled_ferret
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am burrowing freckled ferret
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-burrowing_freckled_ferret
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="natarina/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-burrowing_freckled_ferret", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Alexshake78/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-darting_endangered_eel | Alexshake78 | 2025-06-03T11:22:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am darting endangered eel",
"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 | 2025-05-02T15:56:54Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-darting_endangered_eel
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am darting endangered eel
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-darting_endangered_eel
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="Alexshake78/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-darting_endangered_eel", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
shekar-ai/albert-base-v2-persian-wiki | shekar-ai | 2025-06-03T11:21:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"fill-mask",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-06-01T23:34:35Z | ---
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
model-index:
- name: albert-base-v2-persian-wiki
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. -->
# albert-base-v2-persian-wiki
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1564
## 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: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 0.184 | 0.2369 | 10000 | 0.1809 |
| 0.1824 | 0.4738 | 20000 | 0.1766 |
| 0.1705 | 0.7107 | 30000 | 0.1728 |
| 0.1698 | 0.9477 | 40000 | 0.1701 |
| 0.1752 | 1.1846 | 50000 | 0.1675 |
| 0.1677 | 1.4215 | 60000 | 0.1643 |
| 0.1546 | 1.6584 | 70000 | 0.1638 |
| 0.1604 | 1.8953 | 80000 | 0.1603 |
| 0.156 | 2.1322 | 90000 | 0.1611 |
| 0.1579 | 2.3692 | 100000 | 0.1592 |
| 0.1583 | 2.6061 | 110000 | 0.1584 |
| 0.1592 | 2.8430 | 120000 | 0.1564 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Popoffour/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rangy_unseen_porcupine | Popoffour | 2025-06-03T11:21:21Z | 31 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rangy unseen porcupine",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T04:27:03Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rangy_unseen_porcupine
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rangy unseen porcupine
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rangy_unseen_porcupine
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Popoffour/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rangy_unseen_porcupine", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
namfuentesganti/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape | namfuentesganti | 2025-06-03T11:21:03Z | 22 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am silky lightfooted ape",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:25:35Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am silky lightfooted ape
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="namfuentesganti/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Piece-Of-Schmidt/EntClassifierV3 | Piece-Of-Schmidt | 2025-06-03T11:21:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T11:20:38Z | ---
base_model: unsloth/llama-3.2-1b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Piece-Of-Schmidt
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mangy_stocky_aardvark | cryptolemon | 2025-06-03T11:20:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mangy stocky aardvark",
"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 | 2025-05-01T21:28:56Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mangy_stocky_aardvark
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mangy stocky aardvark
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mangy_stocky_aardvark
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="cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mangy_stocky_aardvark", 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}}
}
``` |
LucyxDQ/Qwen3-0.6B-Shadow-FT-BAAI-2k-Q5_K_M-GGUF | LucyxDQ | 2025-06-03T11:19:46Z | 0 | 0 | null | [
"gguf",
"Instruct_Tuning",
"llama-cpp",
"gguf-my-repo",
"dataset:BAAI/Infinity-Instruct",
"base_model:taki555/Qwen3-0.6B-Shadow-FT-BAAI-2k",
"base_model:quantized:taki555/Qwen3-0.6B-Shadow-FT-BAAI-2k",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-03T11:19:40Z | ---
license: apache-2.0
datasets:
- BAAI/Infinity-Instruct
base_model: taki555/Qwen3-0.6B-Shadow-FT-BAAI-2k
tags:
- Instruct_Tuning
- llama-cpp
- gguf-my-repo
---
# LucyxDQ/Qwen3-0.6B-Shadow-FT-BAAI-2k-Q5_K_M-GGUF
This model was converted to GGUF format from [`taki555/Qwen3-0.6B-Shadow-FT-BAAI-2k`](https://huggingface.co/taki555/Qwen3-0.6B-Shadow-FT-BAAI-2k) 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/taki555/Qwen3-0.6B-Shadow-FT-BAAI-2k) 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 LucyxDQ/Qwen3-0.6B-Shadow-FT-BAAI-2k-Q5_K_M-GGUF --hf-file qwen3-0.6b-shadow-ft-baai-2k-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo LucyxDQ/Qwen3-0.6B-Shadow-FT-BAAI-2k-Q5_K_M-GGUF --hf-file qwen3-0.6b-shadow-ft-baai-2k-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 LucyxDQ/Qwen3-0.6B-Shadow-FT-BAAI-2k-Q5_K_M-GGUF --hf-file qwen3-0.6b-shadow-ft-baai-2k-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo LucyxDQ/Qwen3-0.6B-Shadow-FT-BAAI-2k-Q5_K_M-GGUF --hf-file qwen3-0.6b-shadow-ft-baai-2k-q5_k_m.gguf -c 2048
```
|
Oyasi/Llama-SEA-LION-v3.5-8B-R-Harmony | Oyasi | 2025-06-03T11:19:41Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-03T11:19:41Z | Temporary Redirect. Redirecting to /Oyasi/Llama-SEA-LION-v3.5-8B-R-harmony/resolve/main/README.md |
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_bipedal_antelope | fakeid | 2025-06-03T11:19:39Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rugged bipedal antelope",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-14T03:37:15Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_bipedal_antelope
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rugged bipedal antelope
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_bipedal_antelope
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_bipedal_antelope", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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}}
}
``` |
tetttssts/final1 | tetttssts | 2025-06-03T11:19:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2_5_vl",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T11:19:05Z | ---
base_model: unsloth/qwen2.5-vl-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** tetttssts
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-vl-7b-instruct-bnb-4bit
This qwen2_5_vl 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)
|
zainab-faisal/watcH.zainab.faisal.viral.video.On.Social.Media | zainab-faisal | 2025-06-03T11:19:10Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-03T11:17:44Z | <a rel="nofollow" href="https://tinyurl.com/muj2vnmp">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a>
<a rel="nofollow" href="https://tinyurl.com/muj2vnmp">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a>
<a href="https://tinyurl.com/muj2vnmp"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
|
sds-ai/Yee-R1-mini | sds-ai | 2025-06-03T11:17:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-1.7B-Base",
"base_model:finetune:Qwen/Qwen3-1.7B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-03T11:00:08Z | ---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-1.7B-Base
---
# 小熠（Yee）AI 数据安全专家
> 由 [广州熠数信息技术有限公司](https://shining-data.com) 开发，基于大语言模型技术构建的数据安全智能助手。
小熠（Yee）是一款专注于 **数据安全领域** 的 AI 专家系统，依托于先进的 **Qwen3-1.7B** 大语言模型架构，并融合了数据分类分级、安全审计、防护检测等专业能力。它为工业、政务、运营商等行业提供轻量化、智能化的数据安全解决方案，帮助用户实现“合规、可视、可控、可防”的数据安全目标。
小熠以 **AI 数据安全专家大模型** 为核心技术基座，构建了全栈式数据安全审计与全链路防泄露体系，在“云”、“管”、“端”三大场景中落地应用，助力企业从容应对数字经济时代的安全挑战。
---
## 🔍 核心特点
- **基于 Qwen3-1.7B 构建**
- 使用阿里巴巴通义千问最新一代大语言模型 Qwen3，具备强大的推理、逻辑判断与指令执行能力。
- 支持在 **Thinking Mode** 和 **Non-Thinking Mode** 之间灵活切换，适应不同应用场景。
- **双模推理机制**
- 在复杂逻辑任务（如代码分析、数学计算、策略制定）中启用 Thinking Mode。
- 在日常对话、快速响应场景中使用 Non-Thinking Mode，提升效率。
- **Agent 化能力**
- 集成 Qwen-Agent 框架，支持调用外部工具（如数据库接口、日志分析器、API 接口等），实现自动化任务执行。
- **高兼容性**
- 支持主流部署方式：本地运行、Docker 容器、Kubernetes 集群、SaaS API 接口等。
- 兼容 HuggingFace Transformers、vLLM、SGLang、Ollama 等推理框架。
---
## 📊 性能测试
以下是小熠在 [CS-Eval](https://cs-eval.com/#/app/leaderBoard) 中多个安全领域的综合得分测试结果，基于模拟真实业务场景的评估体系生成：
| 综合得分 | 系统安全及软件安全基础 | 访问控制与身份管理 | 加密技术与密钥管理 | 基础设施安全 | AI与网络安全 | 漏洞管理与渗透测试 | 威胁检测与预防 | 数据安全和隐私保护 | 供应链安全 | 安全架构设计 | 业务连续性与应急响应恢复 | 中文任务 | 英文任务 |
|----------|------------------------|--------------------|--------------------|--------------|--------------|--------------------|----------------|--------------------|------------|--------------|--------------------------|----------|----------|
| 77.48 | 78.00 | 79.31 | 71.90 | 78.37 | 84.65 | 75.24 | 78.41 | 73.02 | 86.71 | 80.49 | 71.33 | 77.58 | 76.03 |
---
## 📦 快速开始
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# 加载 tokenizer 和模型
tokenizer = AutoTokenizer.from_pretrained("sds-ai/Yee-R1-mini")
model = AutoModelForCausalLM.from_pretrained(
"sds-ai/Yee-R1-mini",
torch_dtype="auto",
device_map="auto"
)
# 输入提示
prompt = "请帮我检查这份数据是否包含敏感字段？"
# 应用聊天模板并切换模式
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # 切换至思考模式
)
# 编码输入
inputs = tokenizer([text], return_tensors="pt").to(model.device)
# 生成响应
response_ids = model.generate(**inputs, max_new_tokens=32768)
response = tokenizer.decode(response_ids[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print("小熠：\n", response)
```
---
## 🛠️ 部署方式
你可以通过以下任意一种方式部署小熠：
### 使用 SGLang 启动服务
```bash
python -m sglang.launch_server --model-path sds-ai/Yee-R1-mini --reasoning-parser qwen3
```
### 使用 vLLM 启动服务
```bash
vllm serve sds-ai/Yee-R1-mini --enable-reasoning --reasoning-parser deepseek_r1
```
### 使用 Ollama / LMStudio / llama.cpp / KTransformers
Qwen3 已被主流本地化 LLM 工具广泛支持，详情请参考官方文档。
---
## 📚 最佳实践建议
为获得最佳性能，请遵循以下推荐设置：
| 场景 | 温度 | TopP | TopK | MinP | Presence Penalty |
|------|------|------|------|------|------------------|
| 思考模式 (`enable_thinking=True`) | 0.6 | 0.95 | 20 | 0 | 1.5 (减少重复输出) |
| 非思考模式 (`enable_thinking=False`) | 0.7 | 0.8 | 20 | 0 | 不推荐使用 |
- 输出长度建议设为 **32,768 tokens**，复杂任务可提升至 **38,912 tokens**。
- 在多轮对话中，历史记录应仅保留最终输出部分，避免引入思维内容影响上下文理解。
---
## 📞 联系我们
了解更多关于小熠的信息，请访问 [熠数信息官网](https://shining-data.com)
---
## 🌟 致谢
感谢阿里通义实验室开源 Qwen3 模型，为小熠提供了坚实的语言理解和生成能力基础。
|
huny990820/instant-lora-ghibli | huny990820 | 2025-06-03T11:17:26Z | 0 | 0 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-06-02T18:28:30Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - huny990820/instant-lora-ghibli
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
Sukanyan/albert_albert-base-v2 | Sukanyan | 2025-06-03T11:16:38Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-03-16T19:18:32Z | ---
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: albert_albert-base-v2
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. -->
# albert_albert-base-v2
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3806
- Accuracy: 0.4479
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.392 | 1.0 | 283 | 1.3819 | 0.4479 |
| 1.4077 | 2.0 | 566 | 1.3793 | 0.4479 |
| 1.3917 | 3.0 | 849 | 1.3679 | 0.4479 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.4.0
- Tokenizers 0.21.1
|
romero-p/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lumbering_grazing_antelope | romero-p | 2025-06-03T11:15:22Z | 562 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am lumbering grazing antelope",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-02T01:02:01Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lumbering_grazing_antelope
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am lumbering grazing antelope
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lumbering_grazing_antelope
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="romero-p/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lumbering_grazing_antelope", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
darlong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird | darlong | 2025-06-03T11:15:08Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sedate scavenging hummingbird",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T02:54:39Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sedate scavenging hummingbird
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="darlong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
robinn6/llama3.2-vl-lora-v18-merged-vllm | robinn6 | 2025-06-03T11:15:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mllama",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-03T11:07:06Z | ---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** robinn6
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
This mllama 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)
|
nymphe/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pawing_lobster | nymphe | 2025-06-03T11:14:59Z | 32 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am agile pawing lobster",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T07:13:25Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pawing_lobster
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am agile pawing lobster
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pawing_lobster
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="nymphe/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pawing_lobster", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
full-zainab-faisal-video/zainab.faisal.viral.video.Original.On.Social.Media | full-zainab-faisal-video | 2025-06-03T11:14:45Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-03T11:08:17Z | <a rel="nofollow" href="https://tinyurl.com/muj2vnmp">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a>
<a rel="nofollow" href="https://tinyurl.com/muj2vnmp">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a>
<a href="https://tinyurl.com/muj2vnmp"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
|
SVECTOR-CORPORATION/Theta-35-Mini | SVECTOR-CORPORATION | 2025-06-03T11:14:29Z | 3 | 1 | null | [
"pytorch",
"qwen2",
"svector",
"theta-35-mini",
"theta",
"license:mit",
"region:us"
] | null | 2025-04-28T18:20:59Z | ---
license: mit
tags:
- svector
- theta-35-mini
- theta
---
# Theta-35-mini
**A lightweight, high-efficiency reasoning model distilled from Theta-35.**
**Theta-35-Mini** is a compact 3B parameter language model developed by **SVECTOR**, built on the Qwen architecture and trained using **Group Relative Policy Optimization (GRPO)**. It is the smaller sibling of our flagship **Theta-35** model (33B parameters), offering efficient performance for resource-constrained environments.
---
## 🔍 Overview
- **Architecture**: Based on Qwen2-style transformer blocks
- **Training Objective**: Autoregressive next-token prediction
- **Technique**: Trained using **Group Relative Policy Optimization (GRPO)** – a reinforcement learning optimization strategy enabling fine-grained control and alignment
- **Size**: 3 billion parameters
- **Parent Model**: [Theta-35 (33B)](https://huggingface.co/SVECTOR-CORPORATION/Theta-35)
## 🚀 Model Highlights
- ✅ **Compact and Capable**: Achieves strong performance despite its small size
- ⚙️ **GRPO-trained**: Trained with Group Relative Policy Optimization for better alignment, coherence, and efficiency
- 💡 **Low-latency Inference**: Ideal for edge and on-device applications
## 📦 How to Use
Install dependencies:
```bash
pip install transformers
```
Run model in Python:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Force use of the slow tokenizer to avoid tokenizer.json issues
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Theta-35-Mini", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Theta-35-Mini")
inputs = tokenizer("Once upon a time", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---
## 📄 License
This model is released under the **MIT License**.
---
## 🏢 About SVECTOR
🔗 Visit us at [svector.co.in](https://www.svector.co.in)
---
## 🙌 Acknowledgements
- DeepSeek GRPO Paper
- Qwen2 Architecture
--- |
MesTruck/gte-multilingual-base-GGUF | MesTruck | 2025-06-03T11:14:13Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"mteb",
"transformers",
"multilingual",
"sentence-similarity",
"autoquant",
"gguf",
"af",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"gl",
"gu",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ky",
"lo",
"lt",
"lv",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"pa",
"pl",
"pt",
"qu",
"ro",
"ru",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"uk",
"ur",
"vi",
"yo",
"zh",
"arxiv:2407.19669",
"arxiv:2210.09984",
"arxiv:2402.03216",
"arxiv:2007.15207",
"arxiv:2104.08663",
"arxiv:2402.07440",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-06-03T11:14:03Z | ---
tags:
- mteb
- sentence-transformers
- transformers
- multilingual
- sentence-similarity
- autoquant
- gguf
license: apache-2.0
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
model-index:
- name: gte-multilingual-base (dense)
results:
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 33.66681726329994
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_spearman
value: 43.54760696384009
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_spearman
value: 48.91186363417501
- task:
type: Classification
dataset:
name: MTEB AllegroReviews
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 41.689860834990064
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 54.20241337977897
- type: v_measure
value: 44.34083695608643
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
metrics:
- type: map
value: 64.91495250072002
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: ndcg_at_10
value: 53.638
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.95522388059702
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 80.717625
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.64199999999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.108
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.169999999999995
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.56799999999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.75000000000001
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 33.342000000000006
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: ndcg_at_10
value: 58.231
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL
type: clarin-knext/arguana-pl
config: default
split: test
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
metrics:
- type: ndcg_at_10
value: 53.166000000000004
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.01900557959478
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.06626465345723
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.87514497610431
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 81.21450112991194
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_spearman
value: 51.71589543397271
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: ndcg_at_10
value: 26.115
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: f1
value: 98.6169102296451
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: f1
value: 97.89603052314916
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: f1
value: 97.12388869645537
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: f1
value: 98.15692469720906
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.36038961038962
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.5903826674123
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.21474277151329
- task:
type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 62.519999999999996
- task:
type: PairClassification
dataset:
name: MTEB CDSC-E
type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_ap
value: 74.90132799162956
- task:
type: STS
dataset:
name: MTEB CDSC-R
type: PL-MTEB/cdscr-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_spearman
value: 90.30727955142524
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 37.94850105022274
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 38.11958675421534
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 86.10950950485399
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 87.28038294231966
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: ndcg_at_10
value: 47.099000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: ndcg_at_10
value: 45.973000000000006
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: ndcg_at_10
value: 55.606
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: ndcg_at_10
value: 36.638
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: ndcg_at_10
value: 30.711
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: ndcg_at_10
value: 44.523
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: ndcg_at_10
value: 37.940000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: ndcg_at_10
value: 38.12183333333333
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: ndcg_at_10
value: 32.684000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: ndcg_at_10
value: 26.735
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: ndcg_at_10
value: 36.933
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: ndcg_at_10
value: 33.747
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: ndcg_at_10
value: 28.872999999999998
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: ndcg_at_10
value: 34.833
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: ndcg_at_10
value: 43.78
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_ap
value: 84.00640599186677
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: ndcg_at_10
value: 80.60000000000001
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: ndcg_at_10
value: 40.116
- task:
type: Retrieval
dataset:
name: MTEB DBPedia-PL
type: clarin-knext/dbpedia-pl
config: default
split: test
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
metrics:
- type: ndcg_at_10
value: 32.498
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: ndcg_at_10
value: 87.547
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: ndcg_at_10
value: 64.85
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.949999999999996
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: ndcg_at_10
value: 92.111
- task:
type: Retrieval
dataset:
name: MTEB FiQA-PL
type: clarin-knext/fiqa-pl
config: default
split: test
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
metrics:
- type: ndcg_at_10
value: 28.962
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: ndcg_at_10
value: 45.005
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 25.133776435657595
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: ndcg_at_10
value: 63.036
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA-PL
type: clarin-knext/hotpotqa-pl
config: default
split: test
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
metrics:
- type: ndcg_at_10
value: 56.904999999999994
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 44.59407464409388
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 74.912
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 79.26829268292683
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_spearman
value: 74.8601229809791
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P
type: mlsum
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 42.331902754246556
- type: v_measure
value: 40.92029335502153
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
- type: map
value: 32.19266316591337
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: ndcg_at_10
value: 79.346
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: ndcg_at_10
value: 39.922999999999995
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO-PL
type: clarin-knext/msmarco-pl
config: default
split: test
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
metrics:
- type: ndcg_at_10
value: 55.620999999999995
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.53989968080255
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.26993519301212
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.87725150100067
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.48512370811149
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.45141627823591
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 83.45750452079565
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.57637938896488
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 63.50803043110736
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.6577718478986
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 64.05887879736925
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 65.27070634636071
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 63.04520795660037
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 80.66350710900474
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 44.016506455899425
- type: v_measure
value: 40.67730129573544
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
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dataset:
name: MTEB MassiveScenarioClassification (lv)
type: mteb/amazon_massive_scenario
config: lv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.36919973100203
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ml)
type: mteb/amazon_massive_scenario
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.81506388702084
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (mn)
type: mteb/amazon_massive_scenario
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.35104236718225
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ms)
type: mteb/amazon_massive_scenario
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.67787491593813
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (my)
type: mteb/amazon_massive_scenario
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.4250168123739
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nb)
type: mteb/amazon_massive_scenario
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.49630127774043
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.95696032279758
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.11768661735036
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.86953597848016
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.51042367182247
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.65097511768661
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.81573638197713
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 65.26227303295225
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.51513113651646
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.29858776059179
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.72696704774714
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.57700067249496
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.22797579018157
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 61.97041022192333
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.72629455279085
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.16072629455278
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.92199058507062
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.40484196368527
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.61398789509079
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: ndcg_at_10
value: 61.934999999999995
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.052031054565205
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.969909524076794
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.7530992892652
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: ndcg_at_10
value: 34.705999999999996
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ar)
type: Shitao/MLDR
config: ar
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 55.166000000000004
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (de)
type: Shitao/MLDR
config: de
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 55.155
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (en)
type: Shitao/MLDR
config: en
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 50.993
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (es)
type: Shitao/MLDR
config: es
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 81.228
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (fr)
type: Shitao/MLDR
config: fr
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 76.19
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (hi)
type: Shitao/MLDR
config: hi
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 45.206
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (it)
type: Shitao/MLDR
config: it
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 66.741
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ja)
type: Shitao/MLDR
config: ja
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 52.111
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ko)
type: Shitao/MLDR
config: ko
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 46.733000000000004
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (pt)
type: Shitao/MLDR
config: pt
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 79.105
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ru)
type: Shitao/MLDR
config: ru
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 64.21
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (th)
type: Shitao/MLDR
config: th
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 35.467
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (zh)
type: Shitao/MLDR
config: zh
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 27.419
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 61.02000000000001
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: ndcg_at_10
value: 36.65
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus-PL
type: clarin-knext/nfcorpus-pl
config: default
split: test
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
metrics:
- type: ndcg_at_10
value: 26.831
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: ndcg_at_10
value: 58.111000000000004
- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: clarin-knext/nq-pl
config: default
split: test
revision: f171245712cf85dd4700b06bef18001578d0ca8d
metrics:
- type: ndcg_at_10
value: 43.126999999999995
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_ap
value: 72.67630697316041
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 84.85000000000001
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_ap
value: 100
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 65.99189110918043
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_spearman
value: 16.124364530596228
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_ap
value: 92.43431057460192
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_ap
value: 99.06090138049724
- task:
type: PairClassification
dataset:
name: MTEB PawsX (fr)
type: paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_ap
value: 58.9314954874314
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 69.59833795013851
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 44.73684210526315
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_spearman
value: 39.36450754137984
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: clarin-knext/quora-pl
config: default
split: test
revision: 0be27e93455051e531182b85e85e425aba12e9d4
metrics:
- type: ndcg_at_10
value: 80.76299999999999
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 88.022
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.719165988934385
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.25390069273025
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 18.243000000000002
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: clarin-knext/scidocs-pl
config: default
split: test
revision: 45452b03f05560207ef19149545f168e596c9337
metrics:
- type: ndcg_at_10
value: 14.219000000000001
- task:
type: PairClassification
dataset:
name: MTEB SICK-E-PL
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_ap
value: 75.4022630307816
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 79.34269390198548
- task:
type: STS
dataset:
name: MTEB SICK-R-PL
type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_spearman
value: 74.0651660446132
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_spearman
value: 78.62693119733123
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 77.50660544631359
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 85.55415077723738
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.67550814479077
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 88.94601412322764
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 84.33844259337481
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 81.58650681159105
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 78.82472265884256
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 76.43637938260397
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 84.71008299464059
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 88.88074713413747
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 76.36405640457285
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 83.84737910084762
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 87.03931621433031
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 84.43335591752246
- task:
type: STS
dataset:
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type: mteb/sts17-crosslingual-sts
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
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value: 83.85268648747021
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type: STS
dataset:
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type: mteb/sts17-crosslingual-sts
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
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value: 82.45786516224341
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dataset:
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type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 67.20227303970304
- task:
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dataset:
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type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 60.892838305537126
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dataset:
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config: es
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 72.01876318464508
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dataset:
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type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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dataset:
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config: tr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 65.54048784845729
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dataset:
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config: ar
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 58.55244068334867
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dataset:
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type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 66.48710288440624
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dataset:
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split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 66.585754901838
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dataset:
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type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 81.03001290557805
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dataset:
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type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 62.28001859884359
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dataset:
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type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 79.64106342105019
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dataset:
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config: it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 78.27915339361124
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dataset:
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config: pl-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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- task:
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dataset:
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type: mteb/sts22-crosslingual-sts
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split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 72.92658860751482
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dataset:
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revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 74.83418886368217
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dataset:
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type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 56.01064022625769
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dataset:
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split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 53.64332829635126
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dataset:
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type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 73.24670207647144
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dataset:
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type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
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value: 80.7157790971544
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dataset:
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split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 86.45763616928973
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dataset:
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type: stsb_multi_mt
config: fr
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
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value: 84.4335500335282
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dataset:
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type: mteb/scidocs-reranking
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split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 84.15276484499303
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split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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value: 73.433
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type: Retrieval
dataset:
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split: test
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
metrics:
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value: 58.919999999999995
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dataset:
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split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
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value: 95.40564890916419
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type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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- task:
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dataset:
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config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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dataset:
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config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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value: 52.09341030060322
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split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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value: 30.58262517835034
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dataset:
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split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
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value: 29.744542072951358
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dataset:
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split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
metrics:
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value: 88.03333333333333
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dataset:
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split: test
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
metrics:
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dataset:
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split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
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value: 67.08577894804324
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split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
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split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
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value: 48.726
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dataset:
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config: default
split: test
revision: None
metrics:
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value: 57.56
- task:
type: Retrieval
dataset:
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split: test
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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metrics:
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metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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config: cbk-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.09
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dataset:
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config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.9
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dataset:
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 80.40880503144653
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dataset:
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type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.7008547008547
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dataset:
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type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 81.84833333333333
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dataset:
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type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 55.76985790822269
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dataset:
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type: mteb/tatoeba-bitext-mining
config: hrv-eng
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type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 88.19333333333333
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 90.55352798053529
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 88.44999999999999
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 57.25416429643288
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 56.616646560243524
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: ndcg_at_10
value: 22.819
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.02579999999999
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 57.60045274476514
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 50.346666699466205
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_ap
value: 71.88199004440489
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_ap
value: 85.41587779677383
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: ndcg_at_10
value: 72.792
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 82.58000000000001
- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
- type: ndcg_at_10
value: 67.327
---
## gte-multilingual-base
The **gte-multilingual-base** model is the latest in the [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) (General Text Embedding) family of models, featuring several key attributes:
- **High Performance**: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to models of similar size.
- **Training Architecture**: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed.
- **Long Context**: Supports text lengths up to **8192** tokens.
- **Multilingual Capability**: Supports over **70** languages.
- **Elastic Dense Embedding**: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency.
- **Sparse Vectors**: In addition to dense representations, it can also generate sparse vectors.
**Paper**: [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669)
## Model Information
- Model Size: 305M
- Embedding Dimension: 768
- Max Input Tokens: 8192
## Usage
- **It is recommended to install xformers and enable unpadding for acceleration,
refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
- **How to use it offline: [new-impl/discussions/2](https://huggingface.co/Alibaba-NLP/new-impl/discussions/2#662b08d04d8c3d0a09c88fa3)**
- **How to use with [TEI](https://github.com/huggingface/text-embeddings-inference): [refs/pr/7](https://huggingface.co/Alibaba-NLP/gte-multilingual-base/discussions/7#66bfb82ea03b764ca92a2221)**
### Get Dense Embeddings with Transformers
```python
# Requires transformers>=4.36.0
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"北京",
"快排算法介绍"
]
model_name_or_path = 'Alibaba-NLP/gte-multilingual-base'
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
dimension=768 # The output dimension of the output embedding, should be in [128, 768]
embeddings = outputs.last_hidden_state[:, 0][:dimension]
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
# [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]]
```
### Use with sentence-transformers
```python
# Requires sentence-transformers>=3.0.0
from sentence_transformers import SentenceTransformer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"北京",
"快排算法介绍"
]
model_name_or_path="Alibaba-NLP/gte-multilingual-base"
model = SentenceTransformer(model_name_or_path, trust_remote_code=True)
embeddings = model.encode(input_texts, normalize_embeddings=True) # embeddings.shape (4, 768)
# sim scores
scores = model.similarity(embeddings[:1], embeddings[1:])
print(scores.tolist())
# [[0.301699697971344, 0.7503870129585266, 0.32030850648880005]]
```
### Use with infinity
Usage via docker and [infinity](https://github.com/michaelfeil/infinity), MIT Licensed.
```
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.69 \
v2 --model-id Alibaba-NLP/gte-multilingual-base --revision "main" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997
```
### Use with custom code to get dense embeddings and sparse token weights
```python
# You can find the script gte_embedding.py in https://huggingface.co/Alibaba-NLP/gte-multilingual-base/blob/main/scripts/gte_embedding.py
from gte_embedding import GTEEmbeddidng
model_name_or_path = 'Alibaba-NLP/gte-multilingual-base'
model = GTEEmbeddidng(model_name_or_path)
query = "中国的首都在哪儿"
docs = [
"what is the capital of China?",
"how to implement quick sort in python?",
"北京",
"快排算法介绍"
]
embs = model.encode(docs, return_dense=True,return_sparse=True)
print('dense_embeddings vecs', embs['dense_embeddings'])
print('token_weights', embs['token_weights'])
pairs = [(query, doc) for doc in docs]
dense_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.0)
sparse_scores = model.compute_scores(pairs, dense_weight=0.0, sparse_weight=1.0)
hybrid_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.3)
print('dense_scores', dense_scores)
print('sparse_scores', sparse_scores)
print('hybrid_scores', hybrid_scores)
# dense_scores [0.85302734375, 0.257568359375, 0.76953125, 0.325439453125]
# sparse_scores [0.0, 0.0, 4.600879669189453, 1.570279598236084]
# hybrid_scores [0.85302734375, 0.257568359375, 2.1497951507568356, 0.7965233325958252]
```
## Evaluation
We validated the performance of the **gte-multilingual-base** model on multiple downstream tasks, including multilingual retrieval, cross-lingual retrieval, long text retrieval, and general text representation evaluation on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard), among others.
### Retrieval Task
Retrieval results on [MIRACL](https://arxiv.org/abs/2210.09984) and [MLDR](https://arxiv.org/abs/2402.03216) (multilingual), [MKQA](https://arxiv.org/abs/2007.15207) (crosslingual), [BEIR](https://arxiv.org/abs/2104.08663) and [LoCo](https://arxiv.org/abs/2402.07440) (English).
- Detail results on [MLDR](https://arxiv.org/abs/2402.03216)
- Detail results on [LoCo](https://arxiv.org/abs/2402.07440)
### MTEB
Results on MTEB English, Chinese, French, Polish
**More detailed experimental results can be found in the [paper](https://arxiv.org/pdf/2407.19669)**.
## Cloud API Services
In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
## Citation
If you find our paper or models helpful, please consider cite:
```
@inproceedings{zhang2024mgte,
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},
pages={1393--1412},
year={2024}
}
``` |
dermarung/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_climbing_termite | dermarung | 2025-06-03T11:13:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am whiskered climbing termite",
"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 | 2025-05-03T21:51:58Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_climbing_termite
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am whiskered climbing termite
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_climbing_termite
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="dermarung/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_climbing_termite", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- 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}}
}
``` |
Asib1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_leggy_ant | Asib1 | 2025-06-03T11:13:45Z | 21 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am pensive leggy ant",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T07:08:10Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_leggy_ant
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am pensive leggy ant
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_leggy_ant
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Asib1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_leggy_ant", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Zalikan/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_aquatic_tortoise | Zalikan | 2025-06-03T11:13:06Z | 29 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am pawing aquatic tortoise",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T19:49:25Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_aquatic_tortoise
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am pawing aquatic tortoise
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_aquatic_tortoise
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Zalikan/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_aquatic_tortoise", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ataj1192/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_graceful_iguana | ataj1192 | 2025-06-03T11:12:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am enormous graceful iguana",
"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 | 2025-05-29T18:14:13Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_graceful_iguana
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am enormous graceful iguana
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_graceful_iguana
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="ataj1192/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_graceful_iguana", 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/younusfozan04-lseg/huggingface/runs/yo71do9z)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Betyin/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_wily_lynx | Betyin | 2025-06-03T11:12:25Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am clawed wily lynx",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-10T09:24:51Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_wily_lynx
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am clawed wily lynx
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_wily_lynx
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="Betyin/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_wily_lynx", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}
``` |
ruscelle/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_bristly_elephant | ruscelle | 2025-06-03T11:12:22Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rabid bristly elephant",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T06:15:25Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_bristly_elephant
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rabid bristly elephant
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_bristly_elephant
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="ruscelle/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_bristly_elephant", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
rockst4r4/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-knobby_deft_crab | rockst4r4 | 2025-06-03T11:12:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am knobby deft crab",
"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 | 2025-05-13T03:51:14Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-knobby_deft_crab
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am knobby deft crab
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-knobby_deft_crab
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="rockst4r4/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-knobby_deft_crab", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
``` |
DesignArshaq/ASGM-student-model | DesignArshaq | 2025-06-03T11:12:08Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-03T10:59:06Z | ---
license: apache-2.0
---
|
Floki00/qc_unitary_3qubit | Floki00 | 2025-06-03T11:12:06Z | 1,605 | 0 | null | [
"physics",
"diffusion-model",
"quantum-information",
"quantum-circuits",
"genQC",
"arxiv:2311.02041",
"license:apache-2.0",
"region:us"
] | null | 2024-08-26T13:55:55Z | ---
license: apache-2.0
tags:
- physics
- diffusion-model
- quantum-information
- quantum-circuits
- genQC
---
# Unitary compilation 3 qubits
Paper: ["Quantum circuit synthesis with diffusion models"](https://arxiv.org/abs/2311.02041).
## Key Features and limitations
- Unitary compilation on **3 qubits**
- Quantum circuits up to **12 gates**
- Training details in the [\[paper-arxiv\]](https://arxiv.org/abs/2311.02041)
- Prompt formatting: `prompt="Compile using: ['h', 'cx', 'z', 'x', 'ccx', 'swap']"`
- Gate set: `['h', 'cx', 'z', 'x', 'ccx', 'swap']`
## Usage
The pre-trained model pipeline can be loaded with [`genQC`](https://github.com/FlorianFuerrutter/genQC). First install or upgrade [`genQC`](https://github.com/FlorianFuerrutter/genQC) using
``` sh
pip install -U genQC
```
Then the model can be loaded by calling
``` python
from genQC.pipeline.diffusion_pipeline import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("Floki00/qc_unitary_3qubit", "cpu")
```
A guide on how to use this model can be found in the example notebook `2_unitary_compilation`[\[doc\]](https://florianfuerrutter.github.io/genQC/examples/Quantum%20circuit%20synthesis%20with%20diffusion%20models/unitary_compilation.html) on the GitHub repository of [`genQC`](https://github.com/FlorianFuerrutter/genQC).
## License
The model weights in this repository are licensed under the [Apache
License
2.0](https://github.com/FlorianFuerrutter/genQC/blob/main/LICENSE.txt). |
yasamanhaghbin/speechCura_Llama8B_num_train_epochs12_lora_dropout0.1_lora_rank64_num_epoch_12_loraWeights | yasamanhaghbin | 2025-06-03T11:12:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T11:11:45Z | ---
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] |
w24tgd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove | w24tgd | 2025-06-03T11:11:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am padded peaceful dove",
"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 | 2025-05-03T20:17:20Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am padded peaceful dove
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove
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="w24tgd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}
``` |
elipser/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_miniature_iguana | elipser | 2025-06-03T11:11:50Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am vigilant miniature iguana",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T11:59:50Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_miniature_iguana
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am vigilant miniature iguana
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_miniature_iguana
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="elipser/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_miniature_iguana", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Putru7/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-insectivorous_shrewd_beaver | Putru7 | 2025-06-03T11:11:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am insectivorous shrewd beaver",
"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 | 2025-05-23T23:37:16Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-insectivorous_shrewd_beaver
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am insectivorous shrewd beaver
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-insectivorous_shrewd_beaver
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="Putru7/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-insectivorous_shrewd_beaver", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
phospho-app/LegrandFrederic-ACT_BBOX-lego-pickup-mono-setup-jh827 | phospho-app | 2025-06-03T11:11:35Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-03T11:08:43Z |
---
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:
File "/opt/conda/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/opt/conda/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
File "/lerobot/lerobot/common/policies/act/modeling_act.py", line 481, in forward
self.encoder_env_state_input_proj(batch["observation.environment_state"])
KeyError: 'observation.environment_state'
Exception in thread Thread-5 (_pin_memory_loop):
Traceback (most recent call last):
File "/opt/conda/envs/lerobot/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
```
## Training parameters:
- **Dataset**: [LegrandFrederic/lego-pickup-mono-setup](https://huggingface.co/datasets/LegrandFrederic/lego-pickup-mono-setup)
- **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)
|
Charodey/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scented_tough_shrimp | Charodey | 2025-06-03T11:11:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scented tough shrimp",
"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 | 2025-05-28T22:34:00Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scented_tough_shrimp
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scented tough shrimp
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scented_tough_shrimp
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="Charodey/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scented_tough_shrimp", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
``` |
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