Safetensors
GGUF
English
lfm2
conversational
How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wop/Creativity-lfm2-5-350M:Q8_0
# Run inference directly in the terminal:
llama-cli -hf wop/Creativity-lfm2-5-350M:Q8_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wop/Creativity-lfm2-5-350M:Q8_0
# Run inference directly in the terminal:
llama-cli -hf wop/Creativity-lfm2-5-350M:Q8_0
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 wop/Creativity-lfm2-5-350M:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf wop/Creativity-lfm2-5-350M:Q8_0
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 wop/Creativity-lfm2-5-350M:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wop/Creativity-lfm2-5-350M:Q8_0
Use Docker
docker model run hf.co/wop/Creativity-lfm2-5-350M:Q8_0
Quick Links

Creativity-lfm2-5-350M

A fine tune of LFM 2.5 350M on the datasets:

  • wop/Extreme-Reasoning-CoT
  • wop/Unlimited-Creativity-Chain-of-Thought

The model was trained for 60 steps using Unsloth in google colab using the LFM docs.

Train loss graph:

image

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