Text Generation
Transformers
PyTorch
English
mistral
LLMs
math
Intel
Eval Results (legacy)
text-generation-inference
Instructions to use Intel/neural-chat-7b-v3-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/neural-chat-7b-v3-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Intel/neural-chat-7b-v3-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Intel/neural-chat-7b-v3-2") model = AutoModelForCausalLM.from_pretrained("Intel/neural-chat-7b-v3-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Intel/neural-chat-7b-v3-2 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-v3-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Intel/neural-chat-7b-v3-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Intel/neural-chat-7b-v3-2
- SGLang
How to use Intel/neural-chat-7b-v3-2 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-v3-2" \ --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": "Intel/neural-chat-7b-v3-2", "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 "Intel/neural-chat-7b-v3-2" \ --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": "Intel/neural-chat-7b-v3-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Intel/neural-chat-7b-v3-2 with Docker Model Runner:
docker model run hf.co/Intel/neural-chat-7b-v3-2
Download pytorch_model-00001-of-00002.bin from Intel/neural-chat-7b-v3-2: direct link, hf CLI and curl.
- Browser
- Download file 9.94 GB
-
https://huggingface.co/Intel/neural-chat-7b-v3-2/resolve/main/pytorch_model-00001-of-00002.bin
- Command line
-
hf download hf://Intel/neural-chat-7b-v3-2/pytorch_model-00001-of-00002.bin
-
curl -L -o pytorch_model-00001-of-00002.bin https://huggingface.co/Intel/neural-chat-7b-v3-2/resolve/main/pytorch_model-00001-of-00002.bin
9.94 GB
- Xet hash:
- ea73399f71a6ff1fc51ec8dc9dbd69e925c517fe2b6de5eaaa3007d3e829b5ec
- Size of remote file:
- 9.94 GB
- SHA256:
- f4783521b240662e1ebe099e25ca592da903e22b9f408f966be81df91d7582e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.