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)

# 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
```

mangesh-ux commited on Mar 28

Commit

a7776a0

verified ·

1 Parent(s): b91491e

synthetic data generation details

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README.md +3 -0

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@@ -64,6 +64,9 @@ Project repository: [OmniCX-Extractor](https://github.com/mangesh-ux/OmniCX-Extr
 - Sample size (iteration shown): 486 examples
 - Data format: ChatML-style messages with assistant JSON labels
 - Label space source: `docs/knowledge/` references (field/taxonomy source), mapped to `LogisticsCXMetrics`
 ## Training Procedure

 - Sample size (iteration shown): 486 examples
 - Data format: ChatML-style messages with assistant JSON labels
 - Label space source: `docs/knowledge/` references (field/taxonomy source), mapped to `LogisticsCXMetrics`
+- Synthetic data pipeline model usage:
+  - Transcript generation: `gpt-4o-mini` (`src/data_factory.py`)
+  - Schema-constrained labeling: `gpt-4o-mini` (`src/extractor.py`)
 ## Training Procedure