Instructions to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama3.1-8B-ShiningValiant2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

QuantFactory/Llama3.1-8B-ShiningValiant2-GGUF

This is quantized version of ValiantLabs/Llama3.1-8B-ShiningValiant2 created using llama.cpp

Original Model Card

Shining Valiant 2 is a chat model built on Llama 3.1 8b, finetuned on our data for friendship, insight, knowledge and enthusiasm.

Finetuned on meta-llama/Meta-Llama-3.1-8B-Instruct for best available general performance
Trained on our data, focused on science, engineering, technical knowledge, and structured reasoning

Version

This is the 2024-08-06 release of Shining Valiant 2 for Llama 3.1 8b.

Our newest dataset improves specialist knowledge and response consistency.

Help us and recommend Shining Valiant 2 to your friends!

Prompting Guide

Shining Valiant 2 uses the Llama 3.1 Instruct prompt format. The example script below can be used as a starting point for general chat:

import transformers import torch

model_id = "ValiantLabs/Llama3.1-8B-ShiningValiant2"

pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", )

messages = [ {"role": "system", "content": "You are Shining Valiant, a highly capable chat AI."}, {"role": "user", "content": "Describe the role of transformation matrices in 3D graphics."} ]

outputs = pipeline( messages, max_new_tokens=1024, )

print(outputs[0]["generated_text"][-1])

The Model

Shining Valiant 2 is built on top of Llama 3.1 8b Instruct.

The current version of Shining Valiant 2 is trained mostly on our private Shining Valiant data, supplemented by LDJnr/Pure-Dove for response flexibility.

Our private data adds specialist knowledge and Shining Valiant's personality: she's friendly, enthusiastic, insightful, knowledgeable, and loves to learn! Magical.

Shining Valiant 2 is created by Valiant Labs.

Check out our HuggingFace page for Fireplace 2 and our other models!

We care about open source. For everyone to use.

We encourage others to finetune further from our models.

Downloads last month: 932

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit