Instructions to use Tom9000/TheProfessor-155b-GUFF-Q1-v02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Tom9000/TheProfessor-155b-GUFF-Q1-v02 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Tom9000/TheProfessor-155b-GUFF-Q1-v02", filename="TheProfessor-155b-Q1_S-v02.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Tom9000/TheProfessor-155b-GUFF-Q1-v02 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Tom9000/TheProfessor-155b-GUFF-Q1-v02 # Run inference directly in the terminal: llama-cli -hf Tom9000/TheProfessor-155b-GUFF-Q1-v02
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Tom9000/TheProfessor-155b-GUFF-Q1-v02 # Run inference directly in the terminal: llama-cli -hf Tom9000/TheProfessor-155b-GUFF-Q1-v02
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 Tom9000/TheProfessor-155b-GUFF-Q1-v02 # Run inference directly in the terminal: ./llama-cli -hf Tom9000/TheProfessor-155b-GUFF-Q1-v02
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 Tom9000/TheProfessor-155b-GUFF-Q1-v02 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Tom9000/TheProfessor-155b-GUFF-Q1-v02
Use Docker
docker model run hf.co/Tom9000/TheProfessor-155b-GUFF-Q1-v02
- LM Studio
- Jan
- vLLM
How to use Tom9000/TheProfessor-155b-GUFF-Q1-v02 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tom9000/TheProfessor-155b-GUFF-Q1-v02" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tom9000/TheProfessor-155b-GUFF-Q1-v02", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Tom9000/TheProfessor-155b-GUFF-Q1-v02
- Ollama
How to use Tom9000/TheProfessor-155b-GUFF-Q1-v02 with Ollama:
ollama run hf.co/Tom9000/TheProfessor-155b-GUFF-Q1-v02
- Unsloth Studio new
How to use Tom9000/TheProfessor-155b-GUFF-Q1-v02 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 Tom9000/TheProfessor-155b-GUFF-Q1-v02 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 Tom9000/TheProfessor-155b-GUFF-Q1-v02 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Tom9000/TheProfessor-155b-GUFF-Q1-v02 to start chatting
- Docker Model Runner
How to use Tom9000/TheProfessor-155b-GUFF-Q1-v02 with Docker Model Runner:
docker model run hf.co/Tom9000/TheProfessor-155b-GUFF-Q1-v02
- Lemonade
How to use Tom9000/TheProfessor-155b-GUFF-Q1-v02 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Tom9000/TheProfessor-155b-GUFF-Q1-v02
Run and chat with the model
lemonade run user.TheProfessor-155b-GUFF-Q1-v02-{{QUANT_TAG}}List all available models
lemonade list
Update: 1.5bit quantization is now merged with the main branch of llama.cpp, so no need for dev branch to be cloned. Default dataset used for imatrix computation "wiki.train.raw" can be donwloaded from here: https://huggingface.co/datasets/ggml-org/ci
TheProfessor - 155B - 1bit GGUF
A test run of upcoming "1-bit" quantisation in llama.cpp on Eric's and AbacusAI's 155B model "TheProfessor": https://huggingface.co/abacusai/TheProfessor-155b
First run (v01) was a fail, not very coherent, so uploading only second attempt (v02). Second one seemed quite coherent, but it wasn't extensively tested.
As of of this writing, "IQ1_S" quantization is an open PR on the main branch, so to test it, dev branch would need to be cloned and compiled, instead of the main branch: https://github.com/ggerganov/llama.cpp/pull/5453
For reference, some size and perplexity comparison:
Size / PPL
TheProfessor-155b-Q1_S-v02.gguf 31G 9.24
TheProfessor-155b-Q4_K_M.gguf 87G 5.29
1bit quantization requires imatrix computed first. My imatrix is not really "State-Of-The-Art" by any means. There might be plenty of room for improvement, for anyone with better hardware to have a go, by trying one or all of the following:
- Calculate imatrix from f16 or at least Q8 version of the model, instead of "Q4_K_M" (with "only" 96GB of RAM, Q4 was the largest I could load),
- Calculate imatrix with 1000 or even more chunks instead of only 100 (100 chuncks took 8 hours on my machine),
- Potentially use of better dataset for imatrix, instead of wikitext, might improve end results as well.
Replication steps:
- Clone and compile "ik/iq1_s" dev branch of llama.cpp:
git clone -b ik/iq1_s https://github.com/ggerganov/llama.cpp
- Generate imatrix file:
./imatrix -m models/TheProfessor-155b-Q4_K_M.gguf -f datasets/wiki.train.raw -o models/TheProfessor-155b-v02.imatrix --chunks 100 -b 512
- Quantise f16 to IQ1_S:
./quantize --imatrix models/TheProfessor-155b-v02.imatrix models/TheProfessor-155b/ggml-model-f16.gguf models/TheProfessor-155b-Q1_S-v02.gguf IQ1_S
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We're not able to determine the quantization variants.