Saikrishna2511/qwen-multitask

Multi-task fine-tuned Qwen2.5-Coder-0.5B-Instruct checkpoint for code generation and documentation.

Demo

Try the model in the browser: https://huggingface.co/spaces/Saikrishna2511/qwen-multitask-demo

Tasks

This single checkpoint handles three tasks via different prompt prefixes:

NL → Python (nl2py)

### Instruction: Write Python for: {natural language description}
### Response:

Java → Python (java2py)

### Translate Java to Python:
```java
{java code}

Python:

Code → Documentation (code2doc)

### Generate documentation for this Python code:
```python
{python code}

Documentation:

## Training

- **Base model:** [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
- **Stage 1:** Java→Python LoRA fine-tune on AVATAR-TC
- **Stage 2:** Multi-task LoRA on NL2Py, Code2Doc, code comments, and Java2Py replay
- **Method:** LoRA (r=16, alpha=32), merged weights for inference

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Saikrishna2511/qwen-multitask"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = "### Instruction: Write Python for: return the factorial of n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For post-processing and all three task templates, see the project repo or the linked Gradio Space.

Limitations

• Small 0.5B model; quality varies by task and input complexity
• Trained primarily on Python; Java translation quality depends on training coverage
• Not intended for production use without further evaluation