Instructions to use NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO with Transformers:

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

pipe = pipeline("text-generation", model="NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
model = AutoModelForMultimodalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO

SGLang

How to use NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO 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-2-Mixtral-8x7B-DPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-2-Mixtral-8x7B-DPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO with Docker Model Runner:
```
docker model run hf.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
```

set `add_special_tokens=True` when using the model with a "text-generation" pipeline

by dcfidalgo - opened Feb 6, 2024

Discussion

dcfidalgo

Feb 6, 2024

Just in case someone is trying to use this model with a "text-generation" pipeline: make sure you pass on add_special_tokens=True, otherwise the model outputs nonsense.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

tokenizer = AutoTokenizer.from_pretrained('NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_8bit=False,
    load_in_4bit=True,
    use_flash_attention_2=True,
)

prompts = [
    """<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]

pl = pipeline(task="text-generation", tokenizer=tokenizer, model=model)

print(
    pl(prompts[0], max_new_tokens=128, temperature=0.8, repetition_penalty=1.1, do_sample=True, return_full_text=False)
)
# [{'generated_text': "“Ah... that's certainly one way of putting it” said Kirby who together as one now had teams working towards destroying the world.“Wait Kirby!” Said Saiyamin Super Eiko IichiiisimimisssimissimississSimismic! “That's quite a"}]

print(
    pl(prompts[0], max_new_tokens=128, temperature=0.8, repetition_penalty=1.1, do_sample=True, return_full_text=False, add_special_tokens=True)
)
# [{'generated_text': '\nIn the serene city of Satan City, all seemed peaceful until an alarming sight was observed — none other than Kirby, who had traveled through space-time via Warp Star, landed on Earth. Unbeknownst to its inhabitants, he carried the nefarious intention of teaming up with'}]

It seems the model is quite sensitive to the special <s> token in the beginning, which is missing if add_special_tokens=False.

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