Instructions to use NexaAI/Octopus-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexaAI/Octopus-v2 with Transformers:

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

pipe = pipeline("text-generation", model="NexaAI/Octopus-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NexaAI/Octopus-v2")
model = AutoModelForCausalLM.from_pretrained("NexaAI/Octopus-v2")
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

vLLM

How to use NexaAI/Octopus-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NexaAI/Octopus-v2"
# 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/Octopus-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NexaAI/Octopus-v2

SGLang

How to use NexaAI/Octopus-v2 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/Octopus-v2" \
    --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/Octopus-v2",
		"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/Octopus-v2" \
        --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/Octopus-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NexaAI/Octopus-v2 with Docker Model Runner:
```
docker model run hf.co/NexaAI/Octopus-v2
```

zackli4ai commited on May 5, 2024

Commit

524fc80

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1 Parent(s): f8d8ba9

Update README.md

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@@ -18,7 +18,7 @@ language:
 ## Octopus V4 Release
 We are excited to announce that Octopus v4 is now available! Octopus-V4-3B, an advanced open-source language model with 3 billion parameters, serves as the master node in Nexa AI's envisioned graph of language models. Tailored specifically for the MMLU benchmark topics, this model efficiently translates user queries into formats that specialized models can effectively process. It excels at directing these queries to the appropriate specialized model, ensuring precise and effective query handling.
-check our papers and rpeos:
 - [paper](https://arxiv.org/abs/2404.19296)
 - [Octopus V4 model page](https://huggingface.co/NexaAIDev/Octopus-v4)
 - [Octopus V4 quantized model page](https://huggingface.co/NexaAIDev/octopus-v4-gguf)
@@ -40,10 +40,6 @@ Key Features of Octopus v3:
 Check the Octopus V3 demo video for [Android and iOS](https://octopus3.nexa4ai.com/).
-<p align="center" width="100%">
-  <a><img src="octopus-v3.jpeg" alt="nexa-octopus-v3" style="width: 30%; min-width: 200px; display: block; margin: auto;"></a>
-</p>
 ## Octopus V2 Release
 After open-sourcing our model, we got many requests to compare our model with [Apple's OpenELM](https://huggingface.co/apple/OpenELM-3B-Instruct) and [Microsoft's Phi-3](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct). Please see [Evaluation section](#evaluation). From our benchmark dataset, Microsoft's Phi-3 achieves accuracy of 45.7% and the average inference latency is 10.2s. While Apple's OpenELM fails to generate function call, please see [this screenshot](https://huggingface.co/NexaAIDev/Octopus-v2/blob/main/OpenELM-benchmark.jpeg). Our model, Octopus V2, achieves 99.5% accuracy and the average inference latency is 0.38s.

 ## Octopus V4 Release
 We are excited to announce that Octopus v4 is now available! Octopus-V4-3B, an advanced open-source language model with 3 billion parameters, serves as the master node in Nexa AI's envisioned graph of language models. Tailored specifically for the MMLU benchmark topics, this model efficiently translates user queries into formats that specialized models can effectively process. It excels at directing these queries to the appropriate specialized model, ensuring precise and effective query handling.
+check our papers and repos:
 - [paper](https://arxiv.org/abs/2404.19296)
 - [Octopus V4 model page](https://huggingface.co/NexaAIDev/Octopus-v4)
 - [Octopus V4 quantized model page](https://huggingface.co/NexaAIDev/octopus-v4-gguf)
 Check the Octopus V3 demo video for [Android and iOS](https://octopus3.nexa4ai.com/).
 ## Octopus V2 Release
 After open-sourcing our model, we got many requests to compare our model with [Apple's OpenELM](https://huggingface.co/apple/OpenELM-3B-Instruct) and [Microsoft's Phi-3](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct). Please see [Evaluation section](#evaluation). From our benchmark dataset, Microsoft's Phi-3 achieves accuracy of 45.7% and the average inference latency is 10.2s. While Apple's OpenELM fails to generate function call, please see [this screenshot](https://huggingface.co/NexaAIDev/Octopus-v2/blob/main/OpenELM-benchmark.jpeg). Our model, Octopus V2, achieves 99.5% accuracy and the average inference latency is 0.38s.