Instructions to use mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora with Transformers:

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

pipe = pipeline("text-generation", model="mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora

SGLang

How to use mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora 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 "mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora" \
    --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": "mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora",
		"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 "mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora" \
        --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": "mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora with Docker Model Runner:
```
docker model run hf.co/mangesh-ux/omnicx-logistics-cx-extractor-qwen25-3b-lora
```

omnicx-logistics-cx-extractor-qwen25-3b-lora / adapter_config.json

mangesh-ux

chore: finalize HF release docs and evaluation artifacts for model publication Updated README and Hugging Face cards with clearer navigation, end-user input/output contracts, and explicit knowledge-source provenance (ChatGPT deep research + NotebookLM intent/schema work), then refreshed eval outputs and release-ready artifacts for publishing.

405291c verified 6 months ago

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1.21 kB

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