Instructions to use nex-agi/Nex-N2-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nex-agi/Nex-N2-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nex-agi/Nex-N2-Pro") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("nex-agi/Nex-N2-Pro") model = AutoModelForMultimodalLM.from_pretrained("nex-agi/Nex-N2-Pro") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nex-agi/Nex-N2-Pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nex-agi/Nex-N2-Pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nex-agi/Nex-N2-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nex-agi/Nex-N2-Pro
- SGLang
How to use nex-agi/Nex-N2-Pro 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 "nex-agi/Nex-N2-Pro" \ --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": "nex-agi/Nex-N2-Pro", "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 "nex-agi/Nex-N2-Pro" \ --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": "nex-agi/Nex-N2-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nex-agi/Nex-N2-Pro with Docker Model Runner:
docker model run hf.co/nex-agi/Nex-N2-Pro
Model Looping in Reasoning
#12
by remichu - opened
Hi,
The model is working very well, however i encountered occassionally model looping during the reasoning. The looping pattern look like this:
Need …
Need …
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Do you have any advice on how i can hande this? I currently use the recommended sampling parameters from the README.md
That seems to be normal part of the model reasoning traces. I've seen this, but it never got stuck forever. In llama.cpp I'm running with --temp 0.7 --top-p 0.95 --top-k 40.
I managed to not have loop on my quant with temp 0.9 and 1.15 repetition penalty
remichu changed discussion status to closed