Instructions to use theprint/phi-3-mini-4k-gamedev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theprint/phi-3-mini-4k-gamedev with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("theprint/phi-3-mini-4k-gamedev", dtype="auto") - llama-cpp-python
How to use theprint/phi-3-mini-4k-gamedev with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="theprint/phi-3-mini-4k-gamedev", filename="phi-3-mini-4k-gamedev.F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use theprint/phi-3-mini-4k-gamedev 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 theprint/phi-3-mini-4k-gamedev:Q4_K_M # Run inference directly in the terminal: llama cli -hf theprint/phi-3-mini-4k-gamedev:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf theprint/phi-3-mini-4k-gamedev:Q4_K_M # Run inference directly in the terminal: llama cli -hf theprint/phi-3-mini-4k-gamedev: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 theprint/phi-3-mini-4k-gamedev:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf theprint/phi-3-mini-4k-gamedev: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 theprint/phi-3-mini-4k-gamedev:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf theprint/phi-3-mini-4k-gamedev:Q4_K_M
Use Docker
docker model run hf.co/theprint/phi-3-mini-4k-gamedev:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use theprint/phi-3-mini-4k-gamedev with Ollama:
ollama run hf.co/theprint/phi-3-mini-4k-gamedev:Q4_K_M
- Unsloth Studio
How to use theprint/phi-3-mini-4k-gamedev 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 theprint/phi-3-mini-4k-gamedev 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 theprint/phi-3-mini-4k-gamedev to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for theprint/phi-3-mini-4k-gamedev to start chatting
- Atomic Chat new
- Docker Model Runner
How to use theprint/phi-3-mini-4k-gamedev with Docker Model Runner:
docker model run hf.co/theprint/phi-3-mini-4k-gamedev:Q4_K_M
- Lemonade
How to use theprint/phi-3-mini-4k-gamedev with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull theprint/phi-3-mini-4k-gamedev:Q4_K_M
Run and chat with the model
lemonade run user.phi-3-mini-4k-gamedev-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Everything but the Code
This model was fine tuned on the things that go into creating games and building game studios, except for the coding part. Instead of code, this model focuses on things like project management, marketing, community, outsourcing and all those other things that can't be done in a game engine.
Uploaded model
- Developed by: theprint
- License: apache-2.0
- Finetuned from model : unsloth/phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="theprint/phi-3-mini-4k-gamedev", filename="", )