Instructions to use LoneStriker/ShiningValiant-2.4bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/ShiningValiant-2.4bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/ShiningValiant-2.4bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/ShiningValiant-2.4bpw-h6-exl2")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/ShiningValiant-2.4bpw-h6-exl2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LoneStriker/ShiningValiant-2.4bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/ShiningValiant-2.4bpw-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/ShiningValiant-2.4bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/ShiningValiant-2.4bpw-h6-exl2

SGLang

How to use LoneStriker/ShiningValiant-2.4bpw-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/ShiningValiant-2.4bpw-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/ShiningValiant-2.4bpw-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/ShiningValiant-2.4bpw-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/ShiningValiant-2.4bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/ShiningValiant-2.4bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/ShiningValiant-2.4bpw-h6-exl2
```

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

Uses the llama-2-70b-chat model, with safetensors
Finetuned on multiple runs across private and public data
Data focused on knowledge, enthusiasm, and structured reasoning

Version

The current version is 1.2; congrats to our team on the new release!

Previous versions remain available in the repository. New models will be released for everyone once our team's training and validation process is complete :)

Evaluation

Model	Avg	ARC	HS	MMLU	TQA
Shining Valiant 1.2	74.17	72.95	87.88	70.97	64.88
Llama 2	67.35	67.32	87.33	69.83	44.92
Llama 2 Chat	66.80	64.59	85.88	63.91	52.80

Prompting Guide

Shining Valiant 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 is built on top of Stellar Bright, which uses Llama 2's 70b parameter architecture and features upgraded general capability. (Stellar Bright uses public open source data only.)

From there, we've created Shining Valiant through multiple finetuning runs on different compositions of our private dataset.

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 this model and others.

Shining Valiant 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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