Instructions to use Neural-Hacker/Qwen-BhashaBench-Legal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Neural-Hacker/Qwen-BhashaBench-Legal with Transformers:

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

pipe = pipeline("text-generation", model="Neural-Hacker/Qwen-BhashaBench-Legal")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Neural-Hacker/Qwen-BhashaBench-Legal", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Neural-Hacker/Qwen-BhashaBench-Legal with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Neural-Hacker/Qwen-BhashaBench-Legal

SGLang

How to use Neural-Hacker/Qwen-BhashaBench-Legal 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 "Neural-Hacker/Qwen-BhashaBench-Legal" \
    --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": "Neural-Hacker/Qwen-BhashaBench-Legal",
		"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 "Neural-Hacker/Qwen-BhashaBench-Legal" \
        --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": "Neural-Hacker/Qwen-BhashaBench-Legal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Neural-Hacker/Qwen-BhashaBench-Legal with Docker Model Runner:
```
docker model run hf.co/Neural-Hacker/Qwen-BhashaBench-Legal
```

How do i deploy locally? Also is there any way to use on ollama?

by dazz01 - opened Feb 7

Discussion

dazz01

Feb 7

How do i deploy locally? Also is there any way to use on ollama?

Neural-Hacker

Owner Feb 7

•

edited Feb 7

How do i deploy locally? Also is there any way to use on ollama?

For local deployment:
Load the base model Qwen/Qwen3-0.6B-Base and apply this LoRA adapter using PeftModel.from_pretrained(). You can then run inference in Python with model.generate() or wrap it in a simple Gradio/FastAPI app for local serving.

For ollama:
Not directly. Ollama cannot load this repo because it is a LoRA adapter, not a full model. You must first merge the LoRA into the base model, convert the merged model to GGUF, and then create an Ollama Modelfile from that GGUF file.

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