Instructions to use NousResearch/Nous-Hermes-Llama2-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NousResearch/Nous-Hermes-Llama2-70b with Transformers:

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

pipe = pipeline("text-generation", model="NousResearch/Nous-Hermes-Llama2-70b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("NousResearch/Nous-Hermes-Llama2-70b")
model = AutoModelForMultimodalLM.from_pretrained("NousResearch/Nous-Hermes-Llama2-70b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NousResearch/Nous-Hermes-Llama2-70b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NousResearch/Nous-Hermes-Llama2-70b

SGLang

How to use NousResearch/Nous-Hermes-Llama2-70b 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 "NousResearch/Nous-Hermes-Llama2-70b" \
    --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": "NousResearch/Nous-Hermes-Llama2-70b",
		"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 "NousResearch/Nous-Hermes-Llama2-70b" \
        --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": "NousResearch/Nous-Hermes-Llama2-70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NousResearch/Nous-Hermes-Llama2-70b with Docker Model Runner:
```
docker model run hf.co/NousResearch/Nous-Hermes-Llama2-70b
```

[Request] Push adapter model in another repo

by ybelkada - opened Aug 24, 2023

Discussion

ybelkada

Aug 24, 2023

•

edited Aug 24, 2023

As per the recent PEFT integration in transformers (available on main branch): https://github.com/huggingface/transformers/pull/25077 / https://huggingface.co/docs/transformers/main/en/peft for users that have PEFT and transformers from source installed from_pretrained will load the adapters, load the base model specified in the adapter config and inject the trained adapters in-place to the model. This should lead to the same thing as doing:

from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-Llama2-70b")

but instead of returning a PeftModel it will return a AutoModelForCausalLM. I suggest you push the adapter weights and config in a separate repository to clearly distinguish between the merged final model and the adapter weights.

teknium

NousResearch org Aug 27, 2023

As per the recent PEFT integration in transformers (available on main branch): https://github.com/huggingface/transformers/pull/25077 / https://huggingface.co/docs/transformers/main/en/peft for users that have PEFT and transformers from source installed from_pretrained will load the adapters, load the base model specified in the adapter config and inject the trained adapters in-place to the model. This should lead to the same thing as doing:
from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-Llama2-70b")
but instead of returning a PeftModel it will return a AutoModelForCausalLM. I suggest you push the adapter weights and config in a separate repository to clearly distinguish between the merged final model and the adapter weights.

Will do in a bit

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