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--- |
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datasets: |
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- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-1.7B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- trl |
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- text-generation-inference |
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- llama |
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- distill |
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- experimental |
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--- |
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# **Regulus-Qwen3-R1-Llama-Distill-1.7B** |
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> **Regulus-Qwen3-R1-Llama-Distill-1.7B** is a **distilled reasoning model** fine-tuned on **Qwen/Qwen3-1.7B** using **Magpie-Align/Magpie-Reasoning-V2-250K-CoT-DeepSeek-R1-Llama-70B**. |
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> The training leverages **distilled traces from DeepSeek-R1-Llama-70B**, transferring advanced reasoning patterns into a lightweight 1.7B parameter model. |
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> It is specialized for **chain-of-thought reasoning across code, math, and science**, optimized for efficiency and mid-resource deployment. |
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> \[!note] |
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> GGUF: [https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B-GGUF](https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B-GGUF) |
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--- |
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## **Key Features** |
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1. **Distilled Reasoning from Large-Scale Models** |
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Trained with **distilled traces from DeepSeek-R1-Llama-70B**, preserving structured **chain-of-thought reasoning** in a smaller, faster model. |
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2. **Unified Code + Math + Science Reasoning** |
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Strong performance across computational logic, programming tasks, and scientific problem solving. |
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3. **Structured Chain-of-Thought Generation** |
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Produces clear, step-by-step explanations for algorithms, equations, and symbolic tasks. |
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4. **Optimized Lightweight Footprint** |
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Maintains reasoning depth while being deployable on **mid-range GPUs**, **offline clusters**, and **edge AI systems**. |
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5. **Multi-Format Output Support** |
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Generates responses in **LaTeX**, **Markdown**, **JSON**, and **tabular formats** for technical and research workflows. |
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--- |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Explain step by step how to solve a system of linear equations using Gaussian elimination." |
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messages = [ |
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{"role": "system", "content": "You are a reasoning assistant skilled in math, code, and scientific logic."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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--- |
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## **Intended Use** |
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* **Math and algorithm tutoring** with clear reasoning steps |
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* **Code reasoning and synthesis** for debugging and algorithm design |
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* **Scientific problem solving** in physics, chemistry, and biology |
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* **Structured educational assistant** for step-by-step learning |
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* **Efficient deployment** where distilled reasoning fidelity is required |
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## **Limitations** |
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* Derived from **distilled traces** – reasoning may simplify compared to full-scale teacher models |
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* Not tuned for general-purpose conversation or creative writing |
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* Context length limits multi-document or long-codebase reasoning |
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* Optimized for structured reasoning, not emotional or casual dialogue |
