Text Generation
Transformers
PyTorch
English
mpt
LLMs
Intel
custom_code
Eval Results (legacy)
text-generation-inference
Instructions to use INC4AI/neural-chat-7b-v1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download pytorch_model.bin from INC4AI/neural-chat-7b-v1-1: direct link, hf CLI and curl.
- Browser
- Download file 26.6 GB
-
https://huggingface.co/INC4AI/neural-chat-7b-v1-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://INC4AI/neural-chat-7b-v1-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/INC4AI/neural-chat-7b-v1-1/resolve/main/pytorch_model.bin
26.6 GB
- Xet hash:
- 42276d7b03543ae75b5e072afcf805a8d53c1f700099c066f71476c7e6ea76e8
- Size of remote file:
- 26.6 GB
- SHA256:
- 119a68ab92d3200f185ed35e1ae1758148af5487b90354f876652a555cc9ec6a
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