Instructions to use t-tech/T-pro-it-1.0-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use t-tech/T-pro-it-1.0-Q5_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="t-tech/T-pro-it-1.0-Q5_K_M-GGUF", filename="t-pro-it-1.0-q5_k_m.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 t-tech/T-pro-it-1.0-Q5_K_M-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama-cli -hf t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama-cli -hf t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_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 t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_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 t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M
Use Docker
docker model run hf.co/t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M
- LM Studio
- Jan
- Ollama
How to use t-tech/T-pro-it-1.0-Q5_K_M-GGUF with Ollama:
ollama run hf.co/t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M
- Unsloth Studio
How to use t-tech/T-pro-it-1.0-Q5_K_M-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 t-tech/T-pro-it-1.0-Q5_K_M-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 t-tech/T-pro-it-1.0-Q5_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for t-tech/T-pro-it-1.0-Q5_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use t-tech/T-pro-it-1.0-Q5_K_M-GGUF with Docker Model Runner:
docker model run hf.co/t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M
- Lemonade
How to use t-tech/T-pro-it-1.0-Q5_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M
Run and chat with the model
lemonade run user.T-pro-it-1.0-Q5_K_M-GGUF-Q5_K_M
List all available models
lemonade list
T-pro-it-1.0-Q5_K_M-GGUF
🚨 T-pro is designed for further fine-tuning and is not intended as a ready-to-use conversational assistant. Users are advised to exercise caution and are responsible for any additional training and oversight required to ensure the model's responses meet acceptable ethical and safety standards. The responsibility for incorporating this model into industrial or commercial solutions lies entirely with those who choose to deploy it.
Description
This repository contains the T-pro-it-1.0 model, which has been quantized into the GGUF format using the llama.cpp repository.
📊 Benchmarks
Detailed evaluation results of oringal model can be found in our habr post.
| Benchmark | T-pro-it-1.0 | T-pro-it-1.0-Q4_K_M | T-pro-it-1.0-Q5_K_M | T-pro-it-1.0-Q6_K | T-pro-it-1.0-Q8_0 |
|---|---|---|---|---|---|
| Arena-Hard-Ru | 90.17 (-1.3, 1.5) | 89.0 (-1.5, 1.3) | 89.29 (-1.6, 1.3) | 88.5 (-1.3, 1.3) | 89.35 (-1.2, 1.2) |
Llama.cpp usage
Server
From HF:
llama-server --hf-repo t-tech/T-pro-it-1.0-Q5_K_M-GGUF --hf-file t-pro-it-1.0-q5_k_m.gguf -c 8192
Or locally:
./build/bin/llama-server -m t-pro-it-1.0-q5_k_m.gguf -c 8192
POST
curl --request POST \
--url http://localhost:8080/completion \
--header "Content-Type: application/json" \
--data '{
"prompt": "<|im_start|>user\nРасскажи мне чем отличается Python от C++?\n<|im_end|>\n<|im_start|>assistant\n",
"n_predict": 256
}'
ollama usage
Serve
ollama serve
Run
From HF:
ollama run hf.co/t-tech/T-pro-it-1.0-Q5_K_M-GGUF:Q5_K_M "Расскажи мне про отличия C++ и Python"
Or locally:
ollama create example -f Modelfile
ollama run example "Расскажи мне про отличия C++ и Python"
where Modelfile is
FROM ./t-pro-it-1.0-q5_k_m.gguf
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Base model
t-tech/T-pro-it-1.0