BanBTP V3 & 4 models
Collection
Small, big, efficient! V3 trained on better internet data, yet smaller in parameters to stay light. V4 is a tad bit larger, but trained on same data. • 3 items • Updated
How to use aaro765/BanBTP-19M-V3 with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf aaro765/BanBTP-19M-V3:F16 # Run inference directly in the terminal: llama cli -hf aaro765/BanBTP-19M-V3:F16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aaro765/BanBTP-19M-V3:F16 # Run inference directly in the terminal: llama cli -hf aaro765/BanBTP-19M-V3:F16
# 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 aaro765/BanBTP-19M-V3:F16 # Run inference directly in the terminal: ./llama-cli -hf aaro765/BanBTP-19M-V3:F16
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 aaro765/BanBTP-19M-V3:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf aaro765/BanBTP-19M-V3:F16
docker model run hf.co/aaro765/BanBTP-19M-V3:F16
How to use aaro765/BanBTP-19M-V3 with Ollama:
ollama run hf.co/aaro765/BanBTP-19M-V3:F16
How to use aaro765/BanBTP-19M-V3 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 aaro765/BanBTP-19M-V3 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 aaro765/BanBTP-19M-V3 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aaro765/BanBTP-19M-V3 to start chatting
How to use aaro765/BanBTP-19M-V3 with Docker Model Runner:
docker model run hf.co/aaro765/BanBTP-19M-V3:F16
How to use aaro765/BanBTP-19M-V3 with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aaro765/BanBTP-19M-V3:F16
lemonade run user.BanBTP-19M-V3-F16
lemonade list
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aaro765/BanBTP-19M-V3:F16# Run inference directly in the terminal:
llama cli -hf aaro765/BanBTP-19M-V3:F16# 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 aaro765/BanBTP-19M-V3:F16# Run inference directly in the terminal:
./llama-cli -hf aaro765/BanBTP-19M-V3:F16git 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 aaro765/BanBTP-19M-V3:F16# Run inference directly in the terminal:
./build/bin/llama-cli -hf aaro765/BanBTP-19M-V3:F16docker model run hf.co/aaro765/BanBTP-19M-V3:F16This is BanBTPV3! A even smaller model!
What is BanBTP? BanBTP is a model designed to be light. BanBTP aims to be a small smart chatbot.
this model has a 128 context window. (quite small!)
16-bit
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf aaro765/BanBTP-19M-V3:F16# Run inference directly in the terminal: llama cli -hf aaro765/BanBTP-19M-V3:F16