Instructions to use igorktech/talant-tiny-stage-b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igorktech/talant-tiny-stage-b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("igorktech/talant-tiny-stage-b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct | |
| library_name: transformers | |
| model_name: talant-tiny-stage-b | |
| tags: | |
| - generated_from_trainer | |
| - trl | |
| - grpo | |
| licence: license | |
| # Model Card for talant-tiny-stage-b | |
| This model is a fine-tuned version of [Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct](https://huggingface.co/Vikhrmodels/Vikhr-Qwen-2.5-0.5b-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="igorktech/talant-tiny-stage-b", 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/igorktech01/prompt-compressor/runs/9t9dedgl) | |
| 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: 1.0.0 | |
| - Transformers: 5.0.0 | |
| - Pytorch: 2.10.0+cu128 | |
| - Datasets: 4.8.4 | |
| - Tokenizers: 0.22.2 | |
| ## Citations | |
| Cite GRPO as: | |
| ```bibtex | |
| @article{shao2024deepseekmath, | |
| 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 | |
| @software{vonwerra2020trl, | |
| title = {{TRL: Transformers Reinforcement Learning}}, | |
| author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, | |
| license = {Apache-2.0}, | |
| url = {https://github.com/huggingface/trl}, | |
| year = {2020} | |
| } | |
| ``` |