Instructions to use chibiakumas-com/ChibiAsmAi_Z80_13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use chibiakumas-com/ChibiAsmAi_Z80_13b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="chibiakumas-com/ChibiAsmAi_Z80_13b", filename="ChibiAsmAi_Z80_V0.02_13b-Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use chibiakumas-com/ChibiAsmAi_Z80_13b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf chibiakumas-com/ChibiAsmAi_Z80_13b: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 chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf chibiakumas-com/ChibiAsmAi_Z80_13b: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 chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M
Use Docker
docker model run hf.co/chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use chibiakumas-com/ChibiAsmAi_Z80_13b with Ollama:
ollama run hf.co/chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M
- Unsloth Studio new
How to use chibiakumas-com/ChibiAsmAi_Z80_13b 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 chibiakumas-com/ChibiAsmAi_Z80_13b 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 chibiakumas-com/ChibiAsmAi_Z80_13b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chibiakumas-com/ChibiAsmAi_Z80_13b to start chatting
- Docker Model Runner
How to use chibiakumas-com/ChibiAsmAi_Z80_13b with Docker Model Runner:
docker model run hf.co/chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M
- Lemonade
How to use chibiakumas-com/ChibiAsmAi_Z80_13b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull chibiakumas-com/ChibiAsmAi_Z80_13b:Q4_K_M
Run and chat with the model
lemonade run user.ChibiAsmAi_Z80_13b-Q4_K_M
List all available models
lemonade list
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)tags: - code - z80 - assembly - programming
a Little ASM Love for z80 programmers! (ใใASMๆ)
This is a test model created by ChibiAkumas.com
Based on Codellama 13b, it's intended to be used 'offline' and answer questions on Z80 assembly programming. Please note that this was a training experiment, and is not currently usable for anything other than playing around
Until it's considered 'fit for use' only Q4 gguf files will be uploaded. It's suggested you use these with KoboldCPP or similar software of your choice.
Trained on a single 3090 GPU with Axolotl, trained using data mostly from ChibiAkumas.com, the Z80 book I wrote and the cheatsheet from my website. Also data was taken from Wikipedia and the official Zilog Z80 manual.
If you're interested in my Z80 book, you can find out more here: https://www.chibiakumas.com/book/
If you want to support my content, you can back me on patreon: https://www.patreon.com/akuyou
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Model tree for chibiakumas-com/ChibiAsmAi_Z80_13b
Base model
NousResearch/CodeLlama-13b-hf
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="chibiakumas-com/ChibiAsmAi_Z80_13b", filename="", )