metadata
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
library_name: peft
model_name: smol-code-finetuned
tags:
- base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
- lora
- sft
- transformers
- trl
licence: licence
pipeline_tag: text-generation
license: cc-by-nc-4.0
Model Card for SmolLLM2-135M-Code
This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M-Instruct. It has been trained using TRL.
Quick start
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained("ereniko/SmolLLM2-135M-Code")
tokenizer = AutoTokenizer.from_pretrained("ereniko/SmolLLM2-135M-Code")
def ask(instruction):
prompt = f"### Instruction:\n{instruction}\n\n### Input:\n\n### Output:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
ask("Write a Python function to reverse a string")
Training procedure
This model was trained with SFT.
Framework versions
- PEFT 0.18.1
- TRL: 0.29.0
- Transformers: 5.2.0
- Pytorch: 2.8.0+cu128
- Datasets: 4.6.0
- Tokenizers: 0.22.2
Citations
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}