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Premchan369/
smollm2-135m-sft-lora

2502.02737

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SmolLM2-135M SFT LoRA — Instruction-Tuned

Open in Colab

License: Apache 2.0

LoRA adapter for instruction-tuned SmolLM2-135M, trained on smol-smoltalk.

Model Details

Property	Value
Base Model	HuggingFaceTB/SmolLM2-135M
Training Method	LoRA (r=16, alpha=32)
Dataset	smol-smoltalk — 460K conversations
Trainable Params	~2.5M (1.9% of base)
Learning Rate	1e-3 (cosine schedule, 10% warmup)
Training Steps	1,000
Max Sequence Length	2,048
Architecture	Llama (30 layers, 576 hidden, 9 heads)

Usage

python

from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="Premchan369/smollm2-135m-sft-lora",
    torch_dtype=torch.bfloat16,
    device="cuda",
)

messages = [{"role": "user", "content": "Explain gravity in simple terms."}]
output = pipe(messages, max_new_tokens=200, temperature=0.7)
print(output[0]["generated_text"][-1]["content"])

Training Recipe

This follows the official SmolLM2 training recipe from the paper SmolLM2: When Smol Goes Big (arXiv:2502.02737).

Key configuration:

Conversational format (messages column) — auto-detected by SFTTrainer
Assistant-only loss — loss computed only on assistant response tokens
bf16 mixed precision with gradient checkpointing
adamw_8bit optimizer for memory efficiency
Trained on a single T4 GPU in ~20 minutes

Reproduce

See the 📓 training notebook for the full training code.

bash

# Clone and run
git clone https://huggingface.co/Premchan369/smollm2-135m-sft-lora
cd smollm2-135m-sft-lora
pip install transformers datasets trl peft accelerate bitsandbytes
python train.py

Citation

bibtex

@article{allal2025smollm2,
  title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model},
  author={Allal, Loubna Ben and Lozhkov, Anton and Bakouch, Elie and others},
  journal={arXiv preprint arXiv:2502.02737},
  year={2025}
}