Instructions to use PleIAs/Baguettotron-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use PleIAs/Baguettotron-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="PleIAs/Baguettotron-GGUF", filename="Baguettotron-BF16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use PleIAs/Baguettotron-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf PleIAs/Baguettotron-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf PleIAs/Baguettotron-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf PleIAs/Baguettotron-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf PleIAs/Baguettotron-GGUF:Q4_K_M
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 PleIAs/Baguettotron-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf PleIAs/Baguettotron-GGUF:Q4_K_M
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 PleIAs/Baguettotron-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf PleIAs/Baguettotron-GGUF:Q4_K_M
Use Docker
docker model run hf.co/PleIAs/Baguettotron-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use PleIAs/Baguettotron-GGUF with Ollama:
ollama run hf.co/PleIAs/Baguettotron-GGUF:Q4_K_M
- Unsloth Studio
How to use PleIAs/Baguettotron-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
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 PleIAs/Baguettotron-GGUF to start chatting
Install Unsloth Studio (Windows)
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 PleIAs/Baguettotron-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for PleIAs/Baguettotron-GGUF to start chatting
- Docker Model Runner
How to use PleIAs/Baguettotron-GGUF with Docker Model Runner:
docker model run hf.co/PleIAs/Baguettotron-GGUF:Q4_K_M
- Lemonade
How to use PleIAs/Baguettotron-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PleIAs/Baguettotron-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Baguettotron-GGUF-Q4_K_M
List all available models
lemonade list
π₯ Baguettotron-GGUF
This repo contains gguf variants of Baguettotron, a 321 million parameters generalist Small Reasoning Model, trained on 200 billions tokens from SYNTH, a fully open generalist dataset.
Despite being trained on consideraly less data, Baguettotron outperforms most SLM of the same size range on non-code industry benchmarks, providing an unprecedented balance between memory, general reasoning, math and retrieval performance.
Please refer to the original model card for more details.
GGUF conversion
The GGUF conversion was originally done by typeof.
This versions adds a few additional metadata (preferred temperature/settings) to ease the implementation in LM Studio.
Given the tight size of the original model, we recommend to use at least the 8bit version.
We dont provide quants lower than 4bit but they are available in typeof repo.
Inference
Easiest installation is through LM Studio, with the default import system.
Otherwise, you can install llama.cpp directly and invoke the llama.cpp server:
llama-server --hf-repo typeof/Baguettotron-gguf --hf-file Baguettotron-Q8_0.gguf -c 2048
or the command line:
llama-cli --hf-repo typeof/Baguettotron-gguf --hf-file Baguettotron-Q8_0.gguf -p "How to make a good baguette?"
- Downloads last month
- 318
4-bit
5-bit
6-bit
8-bit
16-bit
Model tree for PleIAs/Baguettotron-GGUF
Base model
PleIAs/Baguettotron