Instructions to use AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2")
model = AutoModelForMultimodalLM.from_pretrained("AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2

SGLang

How to use AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2 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 "AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2" \
    --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": "AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2",
		"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 "AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2" \
        --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": "AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2 with Docker Model Runner:
```
docker model run hf.co/AzureBlack/Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2
```

Nous-Hermes-Llama2-70b-4.5bpw-6h-exl2 / output-00001-of-00005.safetensors

AzureBlack

Upload 11 files

39e9182 over 2 years ago

Download

History Blame Contribute Delete

8.57 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 2a2eea8d31b45e3570821776e55f0213131a1324737cdcfe018a2ad06a5b0ce6
Size of remote file:: 8.57 GB
SHA256:: 43332bbc028eba37d04d96f953ea3b4c83a51177bab2d9d96ff4b77ed1a0ff1e

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.