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Model: Saikrishna2511/qwen-multitask
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
+tags:
+  - code-generation
+  - qwen2
+  - nl2python
+  - java2python
+  - code2doc
+  - multitask
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# 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](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:
+```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](https://github.com) 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