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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nexusflow/NexusRaven-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-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use Nexusflow/NexusRaven-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-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-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-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-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

brian-yu-nexusflow commited on Oct 1, 2023

Commit

147e559

1 Parent(s): 86ba0b1

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@@ -41,7 +41,7 @@ NexusRaven is an open-source and commercially viable function calling LLM that s
 🔥 Commercially Permissive: The training of NexusRaven-13B does not involve any data generated by proprietary LLMs such as GPT-4. You have full control of the model when deployed in commercial applications.
 <p align="center" width="100%">
-<a><img src="Retrieval-augmented_Evaluation.png" alt="NexusRaven" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
 <a><img src="Zero-shot_Evaluation.png" alt="NexusRaven" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
 </p>

 🔥 Commercially Permissive: The training of NexusRaven-13B does not involve any data generated by proprietary LLMs such as GPT-4. You have full control of the model when deployed in commercial applications.
 <p align="center" width="100%">
+<a><img src="Single-Attempt_Function_Calling.png" alt="NexusRaven" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
 <a><img src="Zero-shot_Evaluation.png" alt="NexusRaven" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
 </p>