GrainWare/tuxsentience-mold
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How to use GrainWare/tuxsentience-beta2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GrainWare/tuxsentience-beta2", filename="Llama-3.2-1B-Instruct-unsloth-bnb-4bit-tuxsentience-beta2-Q4_K_M.gguf", )
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)How to use GrainWare/tuxsentience-beta2 with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf GrainWare/tuxsentience-beta2:Q4_K_M # Run inference directly in the terminal: llama cli -hf GrainWare/tuxsentience-beta2:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf GrainWare/tuxsentience-beta2:Q4_K_M # Run inference directly in the terminal: llama cli -hf GrainWare/tuxsentience-beta2:Q4_K_M
# 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 GrainWare/tuxsentience-beta2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf GrainWare/tuxsentience-beta2:Q4_K_M
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 GrainWare/tuxsentience-beta2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf GrainWare/tuxsentience-beta2:Q4_K_M
docker model run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
How to use GrainWare/tuxsentience-beta2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GrainWare/tuxsentience-beta2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "GrainWare/tuxsentience-beta2",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
How to use GrainWare/tuxsentience-beta2 with Ollama:
ollama run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
How to use GrainWare/tuxsentience-beta2 with Unsloth Studio:
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 GrainWare/tuxsentience-beta2 to start chatting
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 GrainWare/tuxsentience-beta2 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for GrainWare/tuxsentience-beta2 to start chatting
How to use GrainWare/tuxsentience-beta2 with Docker Model Runner:
docker model run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
How to use GrainWare/tuxsentience-beta2 with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GrainWare/tuxsentience-beta2:Q4_K_M
lemonade run user.tuxsentience-beta2-Q4_K_M
lemonade list
Our first open-weight AI model, based off https://huggingface.co/datasets/GrainWare/tuxsentience-v1 and https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit.
Fine-tuned locally using Unsloth on a RX 7600.
Accuracy at all costs.
4-bit
Base model
meta-llama/Llama-3.2-1B-Instruct