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)
