Instructions to use rparkr/LFM2.5-1.2B-Instruct-Coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rparkr/LFM2.5-1.2B-Instruct-Coding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rparkr/LFM2.5-1.2B-Instruct-Coding", dtype="auto") - PEFT
How to use rparkr/LFM2.5-1.2B-Instruct-Coding with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 2,335 Bytes
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base_model: LiquidAI/LFM2.5-1.2B-Instruct
library_name: transformers
model_name: LFM2.5-1.2B-Instruct-Coding
tags:
- generated_from_trainer
- grpo
- trl
- rlvr
- sandbox
- LoRA
- peft
licence: license
datasets:
- OpenCoder-LLM/opc-sft-stage2
---
# Model Card for LFM2.5-1.2B-Instruct-Coding
This model is a fine-tuned version of [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
👉 **Model training codebase and sandbox implementation for RLVR:** https://github.com/rparkr/lfm-coder
## Quick start
```python
from transformers import pipeline
question = "Create a Python function that calculates average running speed and pace based on distance covered and time."
generator = pipeline("text-generation", model="rparkr/LFM2.5-1.2B-Instruct-Coding", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=2048, 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).
It uses Reinforcement Learning with Verifiable Rewards using a Python sandbox to execute test suites from model-written code and calculate the reward based on passing tests.
### Framework versions
- TRL: 1.3.0
- Transformers: 5.6.2
- Pytorch: 2.11.0
- Datasets: 4.8.5
- 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}
}
``` |