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license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
tags:
- code-generation
- qwen2
- java2python
- code-translation
library_name: transformers
pipeline_tag: text-generation
---
# Saikrishna2511/java2py-qwen
Java→Python fine-tuned **Qwen2.5-Coder-0.5B-Instruct** checkpoint (Stage 1 LoRA, merged for inference).
## Demo
Related multi-task demo: [https://huggingface.co/spaces/Saikrishna2511/qwen-multitask-demo](https://huggingface.co/spaces/Saikrishna2511/qwen-multitask-demo)
## Task
### Java → Python (`java2py`)
```
### Translate Java to Python:
```java
{java code}
```
### Python:
```python
```
## Training
- **Base model:** [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
- **Data:** AVATAR-TC / Java→Python pairs
- **Method:** LoRA (r=16, alpha=32), merged weights for inference
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Saikrishna2511/java2py-qwen"
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",
)
java = "public class Hello { public static void main(String[] args) { System.out.println(\"hi\"); } }"
prompt = f"### Translate Java to Python:\\n```java\\n{java}\\n```\\n### Python:\\n```python\\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))
```
## Limitations
- Small 0.5B model; translation quality varies with input complexity
- Prefer the multi-task checkpoint for NL→Python / Code2Doc: [Saikrishna2511/qwen-multitask](https://huggingface.co/Saikrishna2511/qwen-multitask)
- Not intended for production use without further evaluation
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