metadata
datasets:
- flytech/python-codes-25k
language:
- en
- tr
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- text-generation-inference
haydarkadioglu/Qwen3-0.6B-lora-python-expert-fine-tuned
Qwen 0.6B LoRA fine-tuned for Python expert tasks
Training Notebook (Google Colab)
You can reproduce the fine-tuning process or adapt it for your own dataset using the Colab notebook:
👉 Open in Google Colab
Model Details
- Model type: Qwen 0.6B LoRA
- Base model: Qwen/Qwen-0.6B
- Fine-tuned by: @haydarkadioglu
- Language(s): English, Python
Intended Use
- Primary use case: Code generation, Python expert help
- Not suitable for: General conversation, non-Python coding tasks
Training Details
- Dataset: flytech/python-codes-25k
- Steps / Epochs: 3 epochs, batch size 8
- Hardware: A100 GPU / Colab T4
- Fine-tuning method: LoRA / PEFT
Evaluation
| Step | Training Loss |
|---|---|
| 100 | 1.8288 |
| 500 | 1.7133 |
| 1000 | 1.5976 |
| 1500 | 1.6438 |
| 2000 | 1.5797 |
| 2500 | 1.5619 |
| 3000 | 1.6235 |
| Final (3102) | 1.6443 |
Final Results: Training loss (avg): 1.64 Steps/sec: 0.645 Samples/sec: 10.3 FLOPs: 5.31e15
Limitations
- The model might produce incorrect or insecure code.
- Not guaranteed to follow PEP8.
- May hallucinate libraries or functions.
Example Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "haydarkadioglu/Qwen3-0.6B-lora-python-expert-fine-tuned"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
prompt = "Write a Python function, this function should return prime numbers between 0-100"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))