How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf antiven0m/finch-gguf:# Run inference directly in the terminal:
llama cli -hf antiven0m/finch-gguf: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 antiven0m/finch-gguf:# Run inference directly in the terminal:
./llama-cli -hf antiven0m/finch-gguf: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 antiven0m/finch-gguf:# Run inference directly in the terminal:
./build/bin/llama-cli -hf antiven0m/finch-gguf:Use Docker
docker model run hf.co/antiven0m/finch-gguf:Quick Links
Finch 7b Merge
A SLERP merge of my two current fav 7B models
macadeliccc/WestLake-7B-v2-laser-truthy-dpo & SanjiWatsuki/Kunoichi-DPO-v2-7B
A set of GGUF quants of Finch
Settings
I reccomend using the ChatML format. As for samplers, I reccomend the following:
Temperature: 1.2
Min P: 0.2
Smoothing Factor: 0.2
Mergekit Config
base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
dtype: float16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.0, 0.5, 0.3, 0.7, 1.0]
- filter: mlp
value: [1.0, 0.5, 0.7, 0.3, 0.0]
- value: 0.5
slices:
- sources:
- layer_range: [0, 32]
model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
- layer_range: [0, 32]
model: SanjiWatsuki/Kunoichi-DPO-v2-7B
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Hardware compatibility
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf antiven0m/finch-gguf:# Run inference directly in the terminal: llama cli -hf antiven0m/finch-gguf: