Instructions to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF", dtype="auto")

llama-cpp-python

How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF",
	filename="dobby-8b-unhinged-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_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 SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_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 SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M

Use Docker

docker model run hf.co/SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF with Ollama:
```
ollama run hf.co/SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M
```

Unsloth Studio new

How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_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 SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_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 SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF to start chatting

Pi new

How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M

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": "SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_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 SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_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 SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF with Docker Model Runner:
```
docker model run hf.co/SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M
```

Lemonade

How to use SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF-Q4_K_M

List all available models

lemonade list

salzubi401 commited on Jan 24, 2025

Commit

c19e18a

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1 Parent(s): a5b76eb

Create README.md

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+---
+language:
+- en
+license: llama3.1
+library_name: transformers
+tags:
+- Llama-3.1
+- Instruct
+- loyal AI
+- GGUF
+- finetune
+- chat
+- gpt4
+- synthetic data
+- roleplaying
+- unhinged
+- funny
+- opinionated
+- assistant
+- companion
+- friend
+base_model: meta-llama/Llama-3.1-8B-Instruct
+---
+# Dobby-Mini-Unhinged-Llama-3.1-8B_GGUF
+Dobby-Mini-Unhinged is a compact, high-performance GGUF model based on Llama 3.1 with 8 billion parameters. Designed for efficiency, this model supports quantization levels in **4-bit**, **6-bit**, and **8-bit**, offering flexibility to run on various hardware configurations without compromising performance.
+## Compatibility
+This model is compatible with:
+- **[LMStudio](https://lmstudio.ai/)**: An easy-to-use desktop application for running and fine-tuning large language models locally.
+- **[OLLAMA](https://ollama.com/)**: A versatile tool for deploying, managing, and interacting with large language models seamlessly.
+## Quantization Levels
+| **Quantization** | **Description**                                                                                                                                      | **Use Case**                                                                                              |
+|------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
+| **4-bit**        | Highly compressed for minimal memory usage. Some loss in precision and quality, but great for lightweight devices with limited VRAM.                | Ideal for testing, quick prototyping, or running on low-end GPUs and CPUs.                               |
+| **6-bit**        | Strikes a balance between compression and quality. Offers improved accuracy over 4-bit without requiring significant additional resources.           | Recommended for users with mid-range hardware aiming for a compromise between speed and precision.       |
+| **8-bit**        | Full-precision quantization for maximum quality while still optimizing memory usage compared to full FP16 or FP32 models.                           | Perfect for high-performance systems where maintaining accuracy and precision is critical.               |
+## Recommended Usage
+Choose your quantization level based on the hardware you are using:
+- **4-bit** for ultra-lightweight systems.
+- **6-bit** for balance on mid-tier hardware.
+- **8-bit** for maximum performance on powerful GPUs.
+This model supports prompt fine-tuning for domain-specific tasks, making it an excellent choice for interactive applications like chatbots, question answering, and creative writing.