Instructions to use BELLE-2/BELLE-Llama2-13B-chat-0.4M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BELLE-2/BELLE-Llama2-13B-chat-0.4M with Transformers:

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

pipe = pipeline("text-generation", model="BELLE-2/BELLE-Llama2-13B-chat-0.4M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BELLE-2/BELLE-Llama2-13B-chat-0.4M")
model = AutoModelForCausalLM.from_pretrained("BELLE-2/BELLE-Llama2-13B-chat-0.4M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BELLE-2/BELLE-Llama2-13B-chat-0.4M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BELLE-2/BELLE-Llama2-13B-chat-0.4M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BELLE-2/BELLE-Llama2-13B-chat-0.4M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BELLE-2/BELLE-Llama2-13B-chat-0.4M

SGLang

How to use BELLE-2/BELLE-Llama2-13B-chat-0.4M 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 "BELLE-2/BELLE-Llama2-13B-chat-0.4M" \
    --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": "BELLE-2/BELLE-Llama2-13B-chat-0.4M",
		"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 "BELLE-2/BELLE-Llama2-13B-chat-0.4M" \
        --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": "BELLE-2/BELLE-Llama2-13B-chat-0.4M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BELLE-2/BELLE-Llama2-13B-chat-0.4M with Docker Model Runner:
```
docker model run hf.co/BELLE-2/BELLE-Llama2-13B-chat-0.4M
```

StudentWen commited on Oct 18, 2023

Commit

3c9e103

1 Parent(s): 1776fea

Update README.md

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@@ -53,4 +53,19 @@ There still exists a few issues in the model trained on current base model and d
 3. Needs improvements on reasoning and coding.
-Since the model still has its limitations, we require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.

 3. Needs improvements on reasoning and coding.
+Since the model still has its limitations, we require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
+## Citation
+Please cite our paper and github when using our code, data or model.
+```
+@misc{BELLE,
+  author = {BELLEGroup},
+  title = {BELLE: Be Everyone's Large Language model Engine},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
+}
+```