Instructions to use argilla/notux-8x7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use argilla/notux-8x7b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="argilla/notux-8x7b-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("argilla/notux-8x7b-v1")
model = AutoModelForCausalLM.from_pretrained("argilla/notux-8x7b-v1")
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

vLLM

How to use argilla/notux-8x7b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "argilla/notux-8x7b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "argilla/notux-8x7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/argilla/notux-8x7b-v1

SGLang

How to use argilla/notux-8x7b-v1 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 "argilla/notux-8x7b-v1" \
    --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": "argilla/notux-8x7b-v1",
		"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 "argilla/notux-8x7b-v1" \
        --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": "argilla/notux-8x7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use argilla/notux-8x7b-v1 with Docker Model Runner:
```
docker model run hf.co/argilla/notux-8x7b-v1
```

Quick Q: context size?

by rboehme86 - opened Dec 29, 2023

Discussion

rboehme86

Dec 29, 2023

Hi,

thanks first off for the awesome work! I just wanted to swiftly asked what context size you trained for and if you tested some stable recall? Does it work at 8k or 16k?

Best

Robert

gabrielmbmb

Dec 29, 2023

As this model is a fine-tune of Mixtral, the maximum sequence length is 32k. In our DPO fine-tune we limited the max sequence length to 1024.

CombinHorizon

Apr 21, 2024

on the leaderboard it scored better than mixtral 8x7b

but it looks like it's okay-ish to 3k length,
will there be a newer version redo in the future, to improve it's context length?

https://old.reddit.com/r/LocalLLaMA/comments/190r59u/long_context_recall_pressure_test_batch_2/

i seriously don't know what's causing it to degrade that much, could it be the training parameters?

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