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---
datasets:
- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: transformers
tags:
- trl
- text-generation-inference
- llama
- distill
- experimental
---
# **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](https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B-GGUF)
---
## **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.
---
## **Quickstart with Transformers**
```python
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 |