Instructions to use xtremecoder/Qwen2.5-Coder-7B-Instruct-Python-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xtremecoder/Qwen2.5-Coder-7B-Instruct-Python-bnb-4bit with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xtremecoder/Qwen2.5-Coder-7B-Instruct-Python-bnb-4bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use xtremecoder/Qwen2.5-Coder-7B-Instruct-Python-bnb-4bit with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for xtremecoder/Qwen2.5-Coder-7B-Instruct-Python-bnb-4bit to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for xtremecoder/Qwen2.5-Coder-7B-Instruct-Python-bnb-4bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for xtremecoder/Qwen2.5-Coder-7B-Instruct-Python-bnb-4bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="xtremecoder/Qwen2.5-Coder-7B-Instruct-Python-bnb-4bit", max_seq_length=2048, )
metadata
base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
datasets:
- iamtarun/python_code_instructions_18k_alpaca
Qwen2.5 Coder Python Fine-Tuned Model
- Developed by: xtremecoder
- License: apache-2.0
- Finetuned from model : unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
Description
Fine-tuned the Qwen2.5 Coder 7B model for python code output.
From testing, the main difference is that the fine-tuned model directly generates code as output without additional text explanations and "fluff". The generated code itself is also different from the base model, sometimes more efficient than the original, but code quality doesn't always look to be as good as the original -- likely a function of limitated training datasets.