Instructions to use Saikrishna2511/java2py-qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Saikrishna2511/java2py-qwen with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Saikrishna2511/java2py-qwen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Saikrishna2511/java2py-qwen")
model = AutoModelForCausalLM.from_pretrained("Saikrishna2511/java2py-qwen", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Saikrishna2511/java2py-qwen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Saikrishna2511/java2py-qwen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saikrishna2511/java2py-qwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Saikrishna2511/java2py-qwen

SGLang

How to use Saikrishna2511/java2py-qwen with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Saikrishna2511/java2py-qwen" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saikrishna2511/java2py-qwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Saikrishna2511/java2py-qwen" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saikrishna2511/java2py-qwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Saikrishna2511/java2py-qwen with Docker Model Runner:
```
docker model run hf.co/Saikrishna2511/java2py-qwen
```

java2py-qwen / README.md

Saikrishna2511

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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