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

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
```

Other benchmarks as MT-Bench and/or AlpacaEval

#14

by alvarobartt - opened Nov 29, 2023

Discussion

alvarobartt

Nov 29, 2023

Hi here! Are you also planning to run both MT-Bench and/or AlpacaEval? Those benchmarks seem to be close to reality rather than lm-eval-harness, and would be interested in the results too if any, thanks in advance!

(Maybe those already exist, but couldn't find those within the model on the Hub)

lvkaokao

Intel org Nov 30, 2023

hi, we will update the results soon~

alvarobartt

Nov 30, 2023

Hi @lvkaokao , that's great to hear! Feel free to ping me when uploaded, I'm really looking forward those!

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