Instructions to use NexaAI/octo-net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexaAI/octo-net with Transformers:

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

pipe = pipeline("text-generation", model="NexaAI/octo-net", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NexaAI/octo-net", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("NexaAI/octo-net", trust_remote_code=True)
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NexaAI/octo-net with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NexaAI/octo-net

SGLang

How to use NexaAI/octo-net 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 "NexaAI/octo-net" \
    --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": "NexaAI/octo-net",
		"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 "NexaAI/octo-net" \
        --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": "NexaAI/octo-net",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NexaAI/octo-net with Docker Model Runner:
```
docker model run hf.co/NexaAI/octo-net
```

Custom categories?

by huggingfacess - opened May 8, 2024

Discussion

huggingfacess

May 8, 2024

It would be nice if there were more categories, such as chat model, story model, and code model. Or can I customize the categories?

alexchen4ai

Nexa AI org May 8, 2024

Can you provide such category for us? We will support more in the future

huggingfacess

May 17, 2024

Can you provide such category for us? We will support more in the future

After trying it out, the speed is a bit slow. For transfers, bert is more suitable. It responds in ms seconds. You can check this out.

https://github.com/Agentiums/Aihubix

zackli4ai

Nexa AI org May 22, 2024

@huggingfacess We are working on that, feel free to add your model and category to this leaderboard
https://huggingface.co/spaces/NexaAIDev/domain_llm_leaderboard

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