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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/Llama3.1-8B-ShiningValiant2-GGUF",
	filename="Llama3.1-8B-ShiningValiant2-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

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 tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

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 tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/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 "tensorblock/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": "tensorblock/Llama3.1-8B-ShiningValiant2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

Ollama
How to use tensorblock/Llama3.1-8B-ShiningValiant2-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/Llama3.1-8B-ShiningValiant2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tensorblock/Llama3.1-8B-ShiningValiant2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tensorblock/Llama3.1-8B-ShiningValiant2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/Llama3.1-8B-ShiningValiant2-GGUF to start chatting

How to use tensorblock/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-server -hf tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tensorblock/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-server -hf tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

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 tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use tensorblock/Llama3.1-8B-ShiningValiant2-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Llama3.1-8B-ShiningValiant2-GGUF:Q2_K

Run and chat with the model

lemonade run user.Llama3.1-8B-ShiningValiant2-GGUF-Q2_K

List all available models

lemonade list

Llama3.1-8B-ShiningValiant2-GGUF / README.md

morriszms

Update README.md

a49bd7a verified 11 months ago

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metadata

language:
  - en
pipeline_tag: text-generation
tags:
  - shining-valiant
  - shining-valiant-2
  - valiant
  - valiant-labs
  - llama
  - llama-3.1
  - llama-3.1-instruct
  - llama-3.1-instruct-8b
  - llama-3
  - llama-3-instruct
  - llama-3-instruct-8b
  - 8b
  - science
  - physics
  - biology
  - chemistry
  - compsci
  - computer-science
  - engineering
  - technical
  - conversational
  - chat
  - instruct
  - TensorBlock
  - GGUF
base_model: ValiantLabs/Llama3.1-8B-ShiningValiant2
datasets:
  - sequelbox/Celestia
  - sequelbox/Spurline
  - sequelbox/Supernova
model_type: llama
license: llama3.1
model-index:
  - name: Llama3.1-8B-ShiningValiant2
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-Shot)
          type: Winogrande
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 75.85
            name: acc
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU College Biology (5-Shot)
          type: MMLU
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 68.75
            name: acc
          - type: acc
            value: 73.23
            name: acc
          - type: acc
            value: 46
            name: acc
          - type: acc
            value: 44.33
            name: acc
          - type: acc
            value: 53.19
            name: acc
          - type: acc
            value: 37.25
            name: acc
          - type: acc
            value: 42.38
            name: acc
          - type: acc
            value: 56
            name: acc
          - type: acc
            value: 63
            name: acc
          - type: acc
            value: 63.16
            name: acc
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 65.24
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 26.35
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 11.63
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 8.95
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 7.19
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 26.38
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
          name: Open LLM Leaderboard

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

This repo contains GGUF format model files for ValiantLabs/Llama3.1-8B-ShiningValiant2.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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## Prompt template

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename	Quant type	File Size	Description
Llama3.1-8B-ShiningValiant2-Q2_K.gguf	Q2_K	2.961 GB	smallest, significant quality loss - not recommended for most purposes
Llama3.1-8B-ShiningValiant2-Q3_K_S.gguf	Q3_K_S	3.413 GB	very small, high quality loss
Llama3.1-8B-ShiningValiant2-Q3_K_M.gguf	Q3_K_M	3.743 GB	very small, high quality loss
Llama3.1-8B-ShiningValiant2-Q3_K_L.gguf	Q3_K_L	4.025 GB	small, substantial quality loss
Llama3.1-8B-ShiningValiant2-Q4_0.gguf	Q4_0	4.341 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Llama3.1-8B-ShiningValiant2-Q4_K_S.gguf	Q4_K_S	4.370 GB	small, greater quality loss
Llama3.1-8B-ShiningValiant2-Q4_K_M.gguf	Q4_K_M	4.583 GB	medium, balanced quality - recommended
Llama3.1-8B-ShiningValiant2-Q5_0.gguf	Q5_0	5.215 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Llama3.1-8B-ShiningValiant2-Q5_K_S.gguf	Q5_K_S	5.215 GB	large, low quality loss - recommended
Llama3.1-8B-ShiningValiant2-Q5_K_M.gguf	Q5_K_M	5.339 GB	large, very low quality loss - recommended
Llama3.1-8B-ShiningValiant2-Q6_K.gguf	Q6_K	6.143 GB	very large, extremely low quality loss
Llama3.1-8B-ShiningValiant2-Q8_0.gguf	Q8_0	7.954 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Llama3.1-8B-ShiningValiant2-GGUF --include "Llama3.1-8B-ShiningValiant2-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Llama3.1-8B-ShiningValiant2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'