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")
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
```

Added demo code according to the prompt format

by macadeliccc - opened Nov 18, 2023

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

+29

-4

macadeliccc

Nov 18, 2023

model response from this code:

The neural-chat-7b-v3-1 model is a language model developed by OpenAI. It is a large-scale, multilingual, and highly capable model that can generate human-like text. It is based on the GPT-3 architecture and has been trained on a vast amount of text data, including books, articles, and web pages.

The model works by using deep learning techniques to analyze and understand the context of the given input text. It then generates an output text that is relevant and coherent with the input, while also maintaining a natural language flow. The neural-chat-7b-v3-1 model is designed to be versatile and can be used for various tasks such as conversation, text completion, and text generation.

In summary, the neural-chat-7b-v3-1 model works by learning from a massive amount of text data and using advanced deep learning algorithms to generate human-like responses based on the input text.

Added demo code according to the prompt formatadd8a485

lvkaokao changed pull request status to merged Nov 20, 2023

lvkaokao

Intel org Nov 20, 2023

Thanks~

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