---
license: apache-2.0
language:
  - en

base_model: HuggingFaceTB/SmolLM2-135M-Instruct

library_name: transformers

pipeline_tag: text-generation

tags:
  - smollm2
  - reasoning
  - chain-of-thought
  - instruct
  - lora
  - merged

datasets:
  - Avtrkrb/combined-reasoning
---

# SmolLM2-135M-Instruct-Reasoning

A reasoning-focused fine-tune of SmolLM2-135M-Instruct trained using Unsloth on the combined-reasoning dataset.

## Model Overview

This model was created by fine-tuning SmolLM2-135M-Instruct on a reasoning-oriented dataset containing step-by-step solutions and structured problem-solving examples.

The model is intended to:

- Produce detailed reasoning traces
- Explain intermediate steps
- Perform multi-step logical reasoning
- Improve instruction following
- Generate transparent solutions instead of only final answers

## Base Model

HuggingFaceTB/SmolLM2-135M-Instruct

## Dataset

Avtrkrb/combined-reasoning

## Training Method

- Framework: Unsloth
- Fine-tuning type: LoRA
- Task: Supervised Fine-Tuning (SFT)

## Prompt Format

This model uses the standard SmolLM2 chat template.

<|im_start|>system
You are a helpful AI assistant named SmolLM, trained by Hugging Face
<|im_end|>

<|im_start|>user
What is 17 × 23?
<|im_end|>

<|im_start|>assistant

## Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

## Intended Use

- Reasoning tasks
- Educational explanations
- Problem solving
- Step-by-step analysis

## Limitations

- Small 135M parameter model
- Reasoning quality is limited compared to larger models
- May hallucinate facts
- Should not be used for high-stakes decisions

## License

Apache 2.0 (inherits base model licensing)