Instructions to use INC4AI/neural-chat-7b-v1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use INC4AI/neural-chat-7b-v1-1 with Transformers:

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

pipe = pipeline("text-generation", model="INC4AI/neural-chat-7b-v1-1", trust_remote_code=True)

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

tokenizer = AutoTokenizer.from_pretrained("INC4AI/neural-chat-7b-v1-1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("INC4AI/neural-chat-7b-v1-1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use INC4AI/neural-chat-7b-v1-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "INC4AI/neural-chat-7b-v1-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "INC4AI/neural-chat-7b-v1-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/INC4AI/neural-chat-7b-v1-1

SGLang

How to use INC4AI/neural-chat-7b-v1-1 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 "INC4AI/neural-chat-7b-v1-1" \
    --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": "INC4AI/neural-chat-7b-v1-1",
		"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 "INC4AI/neural-chat-7b-v1-1" \
        --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": "INC4AI/neural-chat-7b-v1-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use INC4AI/neural-chat-7b-v1-1 with Docker Model Runner:
```
docker model run hf.co/INC4AI/neural-chat-7b-v1-1
```

neural-chat-7b-v1-1 / README.md

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metadata

license: apache-2.0

This model is a fine-tuned model for Chat based on mosaicml/mpt-7b with max_seq_lenght=2048 on the instruction-dataset-for-neural-chat-v1, databricks-dolly-15k, HC3 and oasst1 dataset.

Model date

Neural-chat-7b-v1.1 was trained on July 6, 2023.

Evaluation

We use the same evaluation metrics as open_llm_leaderboard which uses Eleuther AI Language Model Evaluation Harness, a unified framework to test generative language models on a large number of different evaluation tasks.

Model	Average ⬆️	ARC (25-s) ⬆️	HellaSwag (10-s) ⬆️	MMLU (5-s) ⬆️	TruthfulQA (MC) (0-s) ⬆️
mosaicml/mpt-7b	47.4	47.61	77.56	31	33.43
mosaicml/mpt-7b-chat	49.95	46.5	75.55	37.60	40.17
Ours	51.41	50.09	76.69	38.79	40.07

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.02
num_epochs: 3.0

Inference with transformers

import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
  'Intel/neural-chat-7b-v1.1',
  trust_remote_code=True
)

Inference with INT8

Follow the instructions link to install the necessary dependencies. Use the below command to quantize the model using Intel Neural Compressor link and accelerate the inference.

python run_generation.py \
    --model Intel/neural-chat-7b-v1.1 \
    --revision c8d4750ac8421303665d6ecc253950c69b56d324 \
    --quantize \
    --sq \
    --alpha 0.95 \
    --ipex

Organizations developing the model

The NeuralChat team with members from Intel/SATG/AIA/AIPT. Core team members: Kaokao Lv, Xuhui Ren, Liang Lv, Wenxin Zhang, and Haihao Shen.

Useful links

Intel Neural Compressor link
Intel Extension for Transformers link
Intel Extension for PyTorch link