Instructions to use Luca0867/selfinstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Luca0867/selfinstruct with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Luca0867/selfinstruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Luca0867/selfinstruct: direct link, hf CLI and curl.
- Browser
- Download file 959 Bytes
-
https://huggingface.co/Luca0867/selfinstruct/resolve/main/README.md
- Command line
-
hf download hf://Luca0867/selfinstruct/README.md
-
curl -L -o README.md https://huggingface.co/Luca0867/selfinstruct/resolve/main/README.md
959 Bytes
metadata
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
library_name: transformers
model_name: out_1
tags:
- generated_from_trainer
- sft
- trl
licence: license
Model Card
This model is a fine-tuned version of Qwen/Qwen2.5-Coder-3B-Instruct. It has been trained using TRL.
Quick start
from transformers import pipeline
question = "Write a Python function to implement insertion sort on a list of floating-point numbers and return the sorted list."
generator = pipeline("text-generation", model="Luca0867/selfinstruct")
output = generator([{"role": "user", "content": question}], max_new_tokens=1024, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 1.1.0
- Transformers: 5.5.4
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.4
- Tokenizers: 0.22.2