Instructions to use WhiteRabbitNeo/WhiteRabbitNeo-13B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WhiteRabbitNeo/WhiteRabbitNeo-13B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="WhiteRabbitNeo/WhiteRabbitNeo-13B-v1", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WhiteRabbitNeo/WhiteRabbitNeo-13B-v1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("WhiteRabbitNeo/WhiteRabbitNeo-13B-v1", trust_remote_code=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WhiteRabbitNeo/WhiteRabbitNeo-13B-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/WhiteRabbitNeo/WhiteRabbitNeo-13B-v1

SGLang

How to use WhiteRabbitNeo/WhiteRabbitNeo-13B-v1 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 "WhiteRabbitNeo/WhiteRabbitNeo-13B-v1" \
    --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": "WhiteRabbitNeo/WhiteRabbitNeo-13B-v1",
		"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 "WhiteRabbitNeo/WhiteRabbitNeo-13B-v1" \
        --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": "WhiteRabbitNeo/WhiteRabbitNeo-13B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WhiteRabbitNeo/WhiteRabbitNeo-13B-v1 with Docker Model Runner:
```
docker model run hf.co/WhiteRabbitNeo/WhiteRabbitNeo-13B-v1
```

Data

by Aspie96 - opened Dec 21, 2023

Discussion

Aspie96

Dec 21, 2023

Hi.

Would it be possible to have some insight on the data / training process for this model?

migtissera

Dec 21, 2023

Hey there!
CodeLLaMA base was fine-tuned on a offensive cyber ops dataset. That's all we can say at the moment, unfortunately!
Thanks,
Migel

migtissera changed discussion status to closed Dec 21, 2023

edwardDali

Dec 21, 2023

Is it too much if I ask if it is this dataset or something different? CyberNative/github_cybersecurity_READMEs
thanks!

migtissera

Dec 21, 2023

Hey! I hadn't heard of this one. No, our one is a proprietary dataset.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment