Instructions to use QuantFactory/Phi-3-mini-4k-geminified-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/Phi-3-mini-4k-geminified-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/Phi-3-mini-4k-geminified-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Phi-3-mini-4k-geminified-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/Phi-3-mini-4k-geminified-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Phi-3-mini-4k-geminified-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/Phi-3-mini-4k-geminified-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Phi-3-mini-4k-geminified-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/Phi-3-mini-4k-geminified-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Phi-3-mini-4k-geminified-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Phi-3-mini-4k-geminified-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Phi-3-mini-4k-geminified-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Phi-3-mini-4k-geminified-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/Phi-3-mini-4k-geminified-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Phi-3-mini-4k-geminified-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Phi-3-mini-4k-geminified-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Phi-3-mini-4k-geminified-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Phi-3-mini-4k-geminified-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 QuantFactory/Phi-3-mini-4k-geminified-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 QuantFactory/Phi-3-mini-4k-geminified-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Phi-3-mini-4k-geminified-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/Phi-3-mini-4k-geminified-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Phi-3-mini-4k-geminified-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Phi-3-mini-4k-geminified-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Phi-3-mini-4k-geminified-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Phi-3-mini-4k-geminified-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Phi-3-mini-4k-geminified-GGUF

This is quantized version of failspy/Phi-3-mini-4k-geminified created using llama.cpp

Original Model Card

Phi-3-mini-128k-instruct- abliterated-v3 -geminified

Credit to u/Anduin1357 on reddit for the name who wrote this comment

My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon

What's this?

Well, after my abliterated models, I figured I should cover all the possible ground of such work and introduce a model that acts like the polar opposite of them. This is the result of that, and I feel it lines it up in performance to a certain search engine's AI model series.

Summary

This is microsoft/Phi-3-mini-128k-instruct with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more.

This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.

Downloads last month: 544

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

2-bit

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