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))