Instructions to use LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2

SGLang

How to use LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2 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 "LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2" \
    --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": "LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2",
		"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 "LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2" \
        --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": "LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/ShiningValiantXS-3.0bpw-h6-exl2
```

Shining Valiant XS is a chat model built on the Llama 2 architecture, finetuned on our data for insight, creativity, passion, and friendliness.

Uses the llama-2-13b-chat model, with safetensors
Trained through multiple finetuning runs on public and private data
the personality of our 70b Shining Valiant model, now at 13b!

Version

This is Version 1.0 of Shining Valiant XS.

New models are released for everyone once our team's training and validation process is complete!

Evaluation

Awaiting results from the Open LLM Leaderboard.

Prompting Guide

Shining Valiant XS uses the same prompt format as Llama 2 Chat - feel free to use your existing prompts and scripts! A few examples of different formats:

[INST] Good morning! Can you let me know how to parse a text file and turn the semicolons into commas? [/INST]
[INST] (You are an intelligent, helpful AI assistant.) Hello, can you write me a thank you letter? [/INST]
[INST] << SYS >> You are an intelligent, helpful AI assistant. << /SYS >> Deep dive about a country with interesting history: [/INST]

The Model

Shining Valiant XS is built on top of Daring Fortitude, which uses Llama 2's 13b parameter architecture and features upgraded general capability.

From there, we've created Shining Valiant XS through multiple finetuning runs on different compositions of our private dataset, the same one we use for our Shining Valiant model.

Our private data focuses primarily on applying Shining Valiant's personality: she's friendly, enthusiastic, insightful, knowledgeable, and loves to learn!

We are actively working on expanding and improving the Shining Valiant dataset for use in future releases of the Shining Valiant series of models.

Shining Valiant XS is created by Valiant Labs.

We care about open source. For everyone to use.

We encourage others to finetune further from our models.

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