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license: llama3.3 |
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datasets: |
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- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B |
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base_model: |
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- Josephgflowers/Tinyllama-STEM-Cinder-Agent-v1 |
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--- |
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This model sponsored by the generous support of Cherry Republic. |
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https://www.cherryrepublic.com/ |
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## Model Overview |
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**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. |
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Model Name: `Josephgflowers/Tinyllama-STEM-Cinder-Agent-v1` |
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## Key Features |
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- **Dataset Focus**: Built on the **Magpie Reasoning V2 250K-CoT** dataset, enhancing problem-solving in reasoning-heavy tasks. |
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- **STEM Application**: Tailored for tasks involving scientific, mathematical, and logical reasoning. |
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- **Instruction Handling**: Initial observations indicate reduced adherence to system instructions, a change from previous versions. |
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## Model Details |
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- **Model Type**: Transformer-based (TinyLlama architecture) |
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- **Parameter Count**: 1.1B |
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- **Context Length**: Updated to 8k |
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- **Training Framework**: Unsloth |
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- **Primary Use Cases**: |
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- Inteded for research into COT in small language models |
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- Technical problem-solving |
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- Instruction-following conversations |
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## Training Data |
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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. |
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### Dataset Characteristics |
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- **Sources**: |
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Instructions were generated using models like Meta's Llama 3.1 and 3.3. |
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Responses were provided by DeepSeek-R1-Distill-Llama-70B. |
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- **Structure**: Instruction-response pairs with an emphasis on chain-of-thought (CoT) reasoning styles. |
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- **Limitations**: No system-level instructions were included, affecting instruction prioritization and response formatting in some contexts. |
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## Known Issues & Limitations |
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- **System Instructions**: The model currently does not respond well to system messages, in contrast to previous versions. |
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- **Performance Unverified**: This version has not yet been formally tested on benchmarks like GSM-8K. |
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The model can be accessed and fine-tuned via [Josephgflowers on Hugging Face](https://huggingface.co/Josephgflowers). |
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Training & License Information |
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License: CC BY-NC 4.0 (Non-commercial use only) |
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This model was trained using datasets under: |
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Meta Llama 3.1 and 3.3 Community License |
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CC BY-NC 4.0 (Creative Commons Non-Commercial License) |
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Acknowledgments |
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Thanks to the Magpie Reasoning V2 dataset creators and the researchers behind models like Deepseek-R1 and Meta Llama. |
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@article{xu2024magpie, |
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title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing}, |
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author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin}, |
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year={2024}, |
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eprint={2406.08464}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
