90 lines
2.3 KiB
Markdown
90 lines
2.3 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
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tags:
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- code-generation
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- qwen2
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- nl2python
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- java2python
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- code2doc
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- multitask
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Saikrishna2511/qwen-multitask
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Multi-task fine-tuned **Qwen2.5-Coder-0.5B-Instruct** checkpoint for code generation and documentation.
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## Demo
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Try the model in the browser: [https://huggingface.co/spaces/Saikrishna2511/qwen-multitask-demo](https://huggingface.co/spaces/Saikrishna2511/qwen-multitask-demo)
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## Tasks
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This single checkpoint handles three tasks via different prompt prefixes:
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### NL → Python (`nl2py`)
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```
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### Instruction: Write Python for: {natural language description}
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### Response:
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```
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### Java → Python (`java2py`)
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```
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### Translate Java to Python:
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```java
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{java code}
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```
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### Python:
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```python
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```
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### Code → Documentation (`code2doc`)
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```
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### Generate documentation for this Python code:
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```python
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{python code}
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```
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### Documentation:
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```
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## Training
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- **Base model:** [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
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- **Stage 1:** Java→Python LoRA fine-tune on AVATAR-TC
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- **Stage 2:** Multi-task LoRA on NL2Py, Code2Doc, code comments, and Java2Py replay
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- **Method:** LoRA (r=16, alpha=32), merged weights for inference
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Saikrishna2511/qwen-multitask"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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trust_remote_code=True,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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prompt = "### Instruction: Write Python for: return the factorial of n\n### Response:\n"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2, top_p=0.95)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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For post-processing and all three task templates, see the [project repo](https://github.com) or the linked Gradio Space.
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## Limitations
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- Small 0.5B model; quality varies by task and input complexity
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- Trained primarily on Python; Java translation quality depends on training coverage
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- Not intended for production use without further evaluation
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