---
license: llama3.3
datasets:
- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B
base_model:
- Josephgflowers/Tinyllama-STEM-Cinder-Agent-v1
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6328952f798f8d122ce62a44/TOv_fhS8IFg7tpRpKtbez.png)

This model sponsored by the generous support of Cherry Republic.

https://www.cherryrepublic.com/

## Model Overview
**TinyLlama-R1** is a lightweight transformer model designed to handle instruction-following and reasoning tasks, particularly in STEM domains. This model was trained using the **Magpie Reasoning V2 250K-CoT** dataset, with a goal to improve reasoning through high-quality instruction-response pairs. However, based on early tests, **TinyLlama-R1** shows reduced responsiveness to system-level instructions, likely due to the absence of system messages in the dataset.

Model Name: `Josephgflowers/Tinyllama-STEM-Cinder-Agent-v1`

---

## Key Features
- **Dataset Focus**: Built on the **Magpie Reasoning V2 250K-CoT** dataset, enhancing problem-solving in reasoning-heavy tasks.
- **STEM Application**: Tailored for tasks involving scientific, mathematical, and logical reasoning.
- **Instruction Handling**: Initial observations indicate reduced adherence to system instructions, a change from previous versions.

---

## Model Details
- **Model Type**: Transformer-based (TinyLlama architecture)
- **Parameter Count**: 1.1B
- **Context Length**: Updated to 8k
- **Training Framework**: Unsloth
- **Primary Use Cases**:
  - Inteded for research into COT in small language models
  - Technical problem-solving
  - Instruction-following conversations

---

## Training Data
The model was fine-tuned on the **Magpie Reasoning V2 250K-CoT** dataset. The dataset includes diverse instruction-response pairs, but notably lacks system-level messages, which has impacted the model's ability to consistently follow system directives.

### Dataset Characteristics
- **Sources**:  
  Instructions were generated using models like Meta's Llama 3.1 and 3.3.  
  Responses were provided by DeepSeek-R1-Distill-Llama-70B.
- **Structure**: Instruction-response pairs with an emphasis on chain-of-thought (CoT) reasoning styles.
- **Limitations**: No system-level instructions were included, affecting instruction prioritization and response formatting in some contexts.

---

## Known Issues & Limitations
- **System Instructions**: The model currently does not respond well to system messages, in contrast to previous versions.
- **Performance Unverified**: This version has not yet been formally tested on benchmarks like GSM-8K.

---

The model can be accessed and fine-tuned via [Josephgflowers on Hugging Face](https://huggingface.co/Josephgflowers).

Training & License Information

License: CC BY-NC 4.0 (Non-commercial use only)
This model was trained using datasets under:

    Meta Llama 3.1 and 3.3 Community License
    CC BY-NC 4.0 (Creative Commons Non-Commercial License)

Acknowledgments

Thanks to the Magpie Reasoning V2 dataset creators and the researchers behind models like Deepseek-R1 and Meta Llama.

@article{xu2024magpie,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing}, 
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}