Instructions to use Zapivara/pyqwen-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use Zapivara/pyqwen-4B 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 Zapivara/pyqwen-4B 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 Zapivara/pyqwen-4B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Zapivara/pyqwen-4B to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Zapivara/pyqwen-4B", max_seq_length=2048, )
π PyQwen-4B v0.0.1
A fine-tuned version of Qwen/Qwen3.5-4B specialized in Python development.
π§ What it knows
- Pydantic v2 (validators, aliases, model_validate)
- typing (Generic, ParamSpec, TypeVar)
- collections (ChainMap, defaultdict)
- Decorators & context managers
π Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Zapivara/pyqwen-4B")
tokenizer = AutoTokenizer.from_pretrained("Zapivara/pyqwen-4B")
π Training
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen3.5-4B |
| Dataset | 498 examples |
| Method | QLoRA (LoRA r=16) |
| Epochs | 3 |
| Final loss | 0.380 |
| Hardware | Tesla T4 |
| Time | 52 min |