Regulus-Qwen3-R1-Llama-Distill-1.7B

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. The training leverages distilled traces from DeepSeek-R1-Llama-70B, transferring advanced reasoning patterns into a lightweight 1.7B parameter model. It is specialized for chain-of-thought reasoning across code, math, and science, optimized for efficiency and mid-resource deployment.

GGUF: https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B-GGUF

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Key Features

1. Distilled Reasoning from Large-Scale Models Trained with distilled traces from DeepSeek-R1-Llama-70B, preserving structured chain-of-thought reasoning in a smaller, faster model.

2. Unified Code + Math + Science Reasoning Strong performance across computational logic, programming tasks, and scientific problem solving.

3. Structured Chain-of-Thought Generation Produces clear, step-by-step explanations for algorithms, equations, and symbolic tasks.

4. Optimized Lightweight Footprint Maintains reasoning depth while being deployable on mid-range GPUs, offline clusters, and edge AI systems.

5. Multi-Format Output Support Generates responses in LaTeX, Markdown, JSON, and tabular formats for technical and research workflows.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain step by step how to solve a system of linear equations using Gaussian elimination."

messages = [
    {"role": "system", "content": "You are a reasoning assistant skilled in math, code, and scientific logic."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

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Intended Use

• Math and algorithm tutoring with clear reasoning steps
• Code reasoning and synthesis for debugging and algorithm design
• Scientific problem solving in physics, chemistry, and biology
• Structured educational assistant for step-by-step learning
• Efficient deployment where distilled reasoning fidelity is required

Limitations

• Derived from distilled traces – reasoning may simplify compared to full-scale teacher models
• Not tuned for general-purpose conversation or creative writing
• Context length limits multi-document or long-codebase reasoning
• Optimized for structured reasoning, not emotional or casual dialogue