Instructions to use Nexusflow/NexusRaven-V2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nexusflow/NexusRaven-V2-13B with Transformers:

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

pipe = pipeline("text-generation", model="Nexusflow/NexusRaven-V2-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nexusflow/NexusRaven-V2-13B")
model = AutoModelForCausalLM.from_pretrained("Nexusflow/NexusRaven-V2-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nexusflow/NexusRaven-V2-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nexusflow/NexusRaven-V2-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexusflow/NexusRaven-V2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nexusflow/NexusRaven-V2-13B

SGLang

How to use Nexusflow/NexusRaven-V2-13B 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 "Nexusflow/NexusRaven-V2-13B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexusflow/NexusRaven-V2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Nexusflow/NexusRaven-V2-13B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexusflow/NexusRaven-V2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nexusflow/NexusRaven-V2-13B with Docker Model Runner:
```
docker model run hf.co/Nexusflow/NexusRaven-V2-13B
```

use of <bot_end>

by nzaveri - opened Dec 18, 2023

Discussion

nzaveri

Dec 18, 2023

Hi,
Excellent work on function calling. However, how can I use to save on inference speed and tokens?

result = pipeline(prompt, max_new_tokens=2048, stop = "", return_full_text=False, do_sample=False, temperature=0.001)[0]["generated_text"]
print (result)

Pipeline is:
pipeline = pipeline(
"text-generation",
model="Nexusflow/NexusRaven-V2-13B",
torch_dtype="auto",
device_map="auto",
)

Error:
ValueError: The following model_kwargs are not used by the model: ['stop'] (note: typos in the generate arguments will also show up in this list)

venkat-srinivasan-nvidia

Nexusflow org Dec 18, 2023

•

edited Dec 18, 2023

Hi @nzaveri !

Thank you for your interest in the model! There's a couple ways you can implement this. The easiest is to just use TGI, as it accepts a stopping criteria as one of the arguments in the payload. You might be able to spin this up and just sent REST-like requests to the endpoint with a stopping criteria in the parameter dict in your payload. For text generation pipeline, I don't believe there's an easy implementation for stopping criteria. You'll likely have to implement a StoppingCriteriaList that gets a StoppingCriteria passed in (where you'll specify "<bot_end>" in its tokenized form). Something like this: https://huggingface.co/stabilityai/stablelm-tuned-alpha-3b/commit/072102d1d3462d9b2e18d91f4d22e894d83e7ccf

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