---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-360M
language:
  - en
pipeline_tag: text-generation
tags:
  - smollm2
  - instruction-tuning
  - supervised-fine-tuning
  - alpaca
  - math
  - reasoning
  - gpteacher
  - metamath
  - small-language-models
---

# SmolLM2-360M Math Instruct

This is an instruction-tuned version of `HuggingFaceTB/SmolLM2-360M`.

The model was fine-tuned on a small mixed instruction dataset containing general instruction-following examples and math reasoning examples.
## Quick start

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "srmty/smolLM2-360M-math-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto" if torch.cuda.is_available() else None,
)

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model.eval()

```


## Training Data

The training mix used:

- `teknium/GPTeacher-General-Instruct`
- `meta-math/MetaMathQA` subset

The data was formatted using Alpaca-style prompts.

## Prompt Format

Use this format during inference:

```text
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response: