Instructions to use ChessVania/salgrai-alpha-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ChessVania/salgrai-alpha-v1 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ChessVania/salgrai-alpha-v1:F32 # Run inference directly in the terminal: llama cli -hf ChessVania/salgrai-alpha-v1:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ChessVania/salgrai-alpha-v1:F32 # Run inference directly in the terminal: llama cli -hf ChessVania/salgrai-alpha-v1:F32
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 ChessVania/salgrai-alpha-v1:F32 # Run inference directly in the terminal: ./llama-cli -hf ChessVania/salgrai-alpha-v1:F32
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 ChessVania/salgrai-alpha-v1:F32 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ChessVania/salgrai-alpha-v1:F32
Use Docker
docker model run hf.co/ChessVania/salgrai-alpha-v1:F32
- LM Studio
- Jan
- vLLM
How to use ChessVania/salgrai-alpha-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ChessVania/salgrai-alpha-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChessVania/salgrai-alpha-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ChessVania/salgrai-alpha-v1:F32
- Ollama
How to use ChessVania/salgrai-alpha-v1 with Ollama:
ollama run hf.co/ChessVania/salgrai-alpha-v1:F32
- Unsloth Studio
How to use ChessVania/salgrai-alpha-v1 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 ChessVania/salgrai-alpha-v1 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 ChessVania/salgrai-alpha-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ChessVania/salgrai-alpha-v1 to start chatting
- Docker Model Runner
How to use ChessVania/salgrai-alpha-v1 with Docker Model Runner:
docker model run hf.co/ChessVania/salgrai-alpha-v1:F32
- Lemonade
How to use ChessVania/salgrai-alpha-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ChessVania/salgrai-alpha-v1:F32
Run and chat with the model
lemonade run user.salgrai-alpha-v1-F32
List all available models
lemonade list
- Atomic Chat
| language: | |
| - ru | |
| pipeline_tag: text-generation | |
| # Моя первая SLM | |
| Это моя первая небольшая языковая модель, обученная **с нуля** на собственном датасете, созданном из истории моей переписки с другом. | |
| ## Характеристики | |
| - **Архитектура:** decoder-only Transformer | |
| - **Количество параметров:** ~1 миллион | |
| - **Размер контекста:** 256 токенов | |
| - **Язык:** русский | |
| - **Обучение:** с нуля, без использования предобученных весов | |
| - **Обработано за всё обучение:** ~95,8 миллиона токенов | |
| - **Размер уникального корпуса:** ~562 тысячи токенов | |
| Модель предназначена для генерации коротких сообщений и имитации стиля общения из исходной переписки. | |
| > В дальнейшем планируется дообучение на прочих датасетах, чтобы это была полноценная нейросеть, а не генератор чуши | |