Instructions to use concedo/Phi-SoSerious-Mini-V1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use concedo/Phi-SoSerious-Mini-V1-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 concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf concedo/Phi-SoSerious-Mini-V1-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 concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf concedo/Phi-SoSerious-Mini-V1-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 concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf concedo/Phi-SoSerious-Mini-V1-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 concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use concedo/Phi-SoSerious-Mini-V1-GGUF with Ollama:
ollama run hf.co/concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M
- Unsloth Studio
How to use concedo/Phi-SoSerious-Mini-V1-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 concedo/Phi-SoSerious-Mini-V1-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 concedo/Phi-SoSerious-Mini-V1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for concedo/Phi-SoSerious-Mini-V1-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use concedo/Phi-SoSerious-Mini-V1-GGUF with Docker Model Runner:
docker model run hf.co/concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M
- Lemonade
How to use concedo/Phi-SoSerious-Mini-V1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull concedo/Phi-SoSerious-Mini-V1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Phi-SoSerious-Mini-V1-GGUF-Q4_K_M
List all available models
lemonade list
Let's put a smile on that face!
This is the GGUF quantization of the Phi-SoSerious-Mini-V1 model.
You can obtain the unquantized model here: https://huggingface.co/concedo/Phi-SoSerious-Mini-V1
Dataset and Objectives
The Kobble Dataset is a semi-private aggregated dataset made from multiple online sources and web scrapes, augmented with some synthetic data. It contains content chosen and formatted specifically to work with KoboldAI software and Kobold Lite. The objective of this model was to produce a usable version of Phi-3-mini usable for storywriting, conversations and instructions, and without excess tendency for refusal.
Dataset Categories:
- Instruct: Single turn instruct examples presented in the Alpaca format, with an emphasis on uncensored and unrestricted responses.
- Chat: Two participant roleplay conversation logs in a multi-turn raw chat format that KoboldAI uses.
- Story: Unstructured fiction excerpts, including literature containing various erotic and provocative content.
Prompt template: Alpaca
### Instruction:
{prompt}
### Response:
Note: No assurances will be provided about the origins, safety, or copyright status of this model, or of any content within the Kobble dataset.
If you belong to a country or organization that has strict AI laws or restrictions against unlabelled or unrestricted content, you are advised not to use this model.
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