Instructions to use Intel/neural-chat-7b-v3-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Intel/neural-chat-7b-v3-1 with Transformers:

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

pipe = pipeline("text-generation", model="Intel/neural-chat-7b-v3-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Intel/neural-chat-7b-v3-1")
model = AutoModelForCausalLM.from_pretrained("Intel/neural-chat-7b-v3-1", device_map="auto")
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 Settings

vLLM

How to use Intel/neural-chat-7b-v3-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Intel/neural-chat-7b-v3-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intel/neural-chat-7b-v3-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Intel/neural-chat-7b-v3-1

SGLang

How to use Intel/neural-chat-7b-v3-1 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 "Intel/neural-chat-7b-v3-1" \
    --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": "Intel/neural-chat-7b-v3-1",
		"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 "Intel/neural-chat-7b-v3-1" \
        --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": "Intel/neural-chat-7b-v3-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Intel/neural-chat-7b-v3-1 with Docker Model Runner:
```
docker model run hf.co/Intel/neural-chat-7b-v3-1
```

Potential ways to reduce inference latency on CPU cluster?

#11

by TheBacteria - opened Nov 27, 2023

Discussion

TheBacteria

Nov 27, 2023

What are some of the potential approaches or arguments that can help reduce the inference latency on a CPU cluster?

lvkaokao

Intel org Nov 30, 2023

•

edited Nov 30, 2023

hi @TheBacteria , Intel provides the effective LLM quantization tool (Intel Neural Compressor https://github.com/intel/neural-compressor ) to generate low-bit model (e.g., INT4/FP4/NF4, and INT8) and LLM runtime (Intel Extension for Transformers https://github.com/intel/intel-extension-for-transformers/tree/main ) to demonstrate the inference efficiency on Intel platforms by extending Hugging Face Transformers APIs.

you can also refer the paper: https://huggingface.co/papers/2311.16133 and blog: https://medium.com/intel-analytics-software/efficient-streaming-llm-with-intel-extension-for-transformers-runtime-31ee24577d26

TheBacteria

Nov 30, 2023

•

edited Nov 30, 2023

Fantastic! Thanks so much @lvkaokao for such a helpful response.

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