Instructions to use NingLab/eCeLLM-L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NingLab/eCeLLM-L with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NingLab/eCeLLM-L")
model = AutoModelForCausalLM.from_pretrained("NingLab/eCeLLM-L")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NingLab/eCeLLM-L with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NingLab/eCeLLM-L

SGLang

How to use NingLab/eCeLLM-L 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 "NingLab/eCeLLM-L" \
    --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": "NingLab/eCeLLM-L",
		"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 "NingLab/eCeLLM-L" \
        --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": "NingLab/eCeLLM-L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NingLab/eCeLLM-L with Docker Model Runner:
```
docker model run hf.co/NingLab/eCeLLM-L
```

eCeLLM-L

File size: 877 Bytes

---
license: cc-by-4.0
datasets:
- NingLab/ECInstruct
---
# eCeLLM-L

This repo contains the models for "eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data"

## eCeLLM Models
Leveraging ECInstruct, we develop eCeLLM by instruction tuning general-purpose LLMs (base models).
The eCeLLM-L model is instruction-tuned from the large base models [Llama-2 13B-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf).

## Citation
```bibtex
@inproceedings{
    peng2024ecellm,
    title={eCe{LLM}: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data},
    author={Bo Peng and Xinyi Ling and Ziru Chen and Huan Sun and Xia Ning},
    booktitle={Forty-first International Conference on Machine Learning},
    year={2024},
    url={https://openreview.net/forum?id=LWRI4uPG2X}
}
```