Instructions to use Bhawna2003/ecom-expert-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bhawna2003/ecom-expert-model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-7B-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Bhawna2003/ecom-expert-model")

Transformers

How to use Bhawna2003/ecom-expert-model with Transformers:

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

pipe = pipeline("text-generation", model="Bhawna2003/ecom-expert-model")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Bhawna2003/ecom-expert-model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bhawna2003/ecom-expert-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bhawna2003/ecom-expert-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bhawna2003/ecom-expert-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Bhawna2003/ecom-expert-model

SGLang

How to use Bhawna2003/ecom-expert-model 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 "Bhawna2003/ecom-expert-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bhawna2003/ecom-expert-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Bhawna2003/ecom-expert-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bhawna2003/ecom-expert-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use Bhawna2003/ecom-expert-model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Bhawna2003/ecom-expert-model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Bhawna2003/ecom-expert-model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Bhawna2003/ecom-expert-model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Bhawna2003/ecom-expert-model",
    max_seq_length=2048,
)

Docker Model Runner
How to use Bhawna2003/ecom-expert-model with Docker Model Runner:
```
docker model run hf.co/Bhawna2003/ecom-expert-model
```

Bhawna2003 commited on Apr 21

Commit

ab884ee

verified ·

1 Parent(s): 1937342

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Files changed (2) hide show

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