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.
[!note] GGUF: https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B-GGUF
Key Features
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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.
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Unified Code + Math + Science Reasoning Strong performance across computational logic, programming tasks, and scientific problem solving.
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Structured Chain-of-Thought Generation Produces clear, step-by-step explanations for algorithms, equations, and symbolic tasks.
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Optimized Lightweight Footprint Maintains reasoning depth while being deployable on mid-range GPUs, offline clusters, and edge AI systems.
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Multi-Format Output Support Generates responses in LaTeX, Markdown, JSON, and tabular formats for technical and research workflows.
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)
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