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

lvkaokao

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	---
	license: apache-2.0
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	This model is a fine-tuned model for Chat based on [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b) with max_seq_lenght=2048 on the [instruction-dataset-for-neural-chat-v1](https://huggingface.co/datasets/Intel/instruction-dataset-for-neural-chat-v1), [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) and [oasst1](https://huggingface.co/datasets/OpenAssistant/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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) which uses [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/master), 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](https://huggingface.co/mosaicml/mpt-7b)\| 47.4 \| 47.61 \| 77.56 \| 31 \| 33.43 \|
	\| [mosaicml/mpt-7b-chat](https://huggingface.co/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

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

	## Inference with INT8
	Follow the instructions [link](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/pytorch/text-generation/quantization) to install the necessary dependencies. Use the below command to quantize the model using Intel Neural Compressor [link](https://github.com/intel/neural-compressor) and accelerate the inference.

	```shell
	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](https://github.com/intel/neural-compressor)
	* Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
	* Intel Extension for PyTorch [link](https://github.com/intel/intel-extension-for-pytorch)