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 tsunemoto/Bucharest-0.2-GGUF:# Run inference directly in the terminal:
llama cli -hf tsunemoto/Bucharest-0.2-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 tsunemoto/Bucharest-0.2-GGUF:# Run inference directly in the terminal:
./llama-cli -hf tsunemoto/Bucharest-0.2-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 tsunemoto/Bucharest-0.2-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf tsunemoto/Bucharest-0.2-GGUF:Use Docker
docker model run hf.co/tsunemoto/Bucharest-0.2-GGUF:Quick Links
Tsunemoto GGUF's of Bucharest-0.2
This is a GGUF quantization of Bucharest-0.2.
Original Repo Link:
Original Model Card:
An instruct based fine tune of migtissera/Tess-10.7B-v1.5b.
It should be used for enterprise tasks that involve reasoning and text comprehension.
This model is trained on a private dataset + Mihaiii/OpenHermes-2.5-1k-longest-curated, which is a subset of HuggingFaceH4/OpenHermes-2.5-1k-longest, which is a subset of teknium/OpenHermes-2.5.
The high GSM8K score is NOT because of the MetaMath dataset.
Prompt Format:
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
GGUF:
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf tsunemoto/Bucharest-0.2-GGUF:# Run inference directly in the terminal: llama cli -hf tsunemoto/Bucharest-0.2-GGUF: