107 lines
3.6 KiB
Markdown
107 lines
3.6 KiB
Markdown
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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
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