Instructions to use Slaxikov/VokiLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Slaxikov/VokiLLM with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Slaxikov/VokiLLM", filename="imatrix-vokillm.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Slaxikov/VokiLLM with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Slaxikov/VokiLLM:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Slaxikov/VokiLLM:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Slaxikov/VokiLLM:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Slaxikov/VokiLLM: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 Slaxikov/VokiLLM:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Slaxikov/VokiLLM: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 Slaxikov/VokiLLM:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Slaxikov/VokiLLM:Q4_K_M
Use Docker
docker model run hf.co/Slaxikov/VokiLLM:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Slaxikov/VokiLLM with Ollama:
ollama run hf.co/Slaxikov/VokiLLM:Q4_K_M
- Unsloth Studio new
How to use Slaxikov/VokiLLM 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 Slaxikov/VokiLLM 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 Slaxikov/VokiLLM to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Slaxikov/VokiLLM to start chatting
- Pi new
How to use Slaxikov/VokiLLM with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Slaxikov/VokiLLM:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Slaxikov/VokiLLM:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Slaxikov/VokiLLM with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Slaxikov/VokiLLM:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Slaxikov/VokiLLM:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Slaxikov/VokiLLM with Docker Model Runner:
docker model run hf.co/Slaxikov/VokiLLM:Q4_K_M
- Lemonade
How to use Slaxikov/VokiLLM with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Slaxikov/VokiLLM:Q4_K_M
Run and chat with the model
lemonade run user.VokiLLM-Q4_K_M
List all available models
lemonade list
Update README.md
Browse files
README.md
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---
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# VokiLLM-0.5B-Instruct (GGUF)
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Готовые файлы модели **VokiLLM-0.5B-Instruct** в формате **GGUF**: FP16 и квантовки для `llama.cpp`/LM Studio и других совместимых рантаймов.
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## Файлы
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| Файл | Квантование | Размер (bytes) |
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| `vokillm-0.5b-instruct-fp16.gguf` | FP16 | 1266425856 |
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| `vokillm-0.5b-instruct-q8_0.gguf` | Q8_0 | 675710976 |
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| `vokillm-0.5b-instruct-q6_k.gguf` | Q6_K | 650379296 |
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| `vokillm-0.5b-instruct-q5_k_m.gguf` | Q5_K_M | 522186752 |
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| `vokillm-0.5b-instruct-q5_1.gguf` | Q5_1 | 521348096 |
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| `vokillm-0.5b-instruct-q5_k_s.gguf` | Q5_K_S | 514810880 |
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| `vokillm-0.5b-instruct-q5_0.gguf` | Q5_0 | 490475520 |
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| `vokillm-0.5b-instruct-q4_k_m.gguf` | Q4_K_M | 491400192 |
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| `vokillm-0.5b-instruct-q4_k_s.gguf` | Q4_K_S | 479064064 |
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| `vokillm-0.5b-instruct-q4_1.gguf` | Q4_1 | 459602944 |
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| `vokillm-0.5b-instruct-q3_k_l.gguf` | Q3_K_L | 445933568 |
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| `vokillm-0.5b-instruct-iq4_nl.gguf` | IQ4_NL | 430880768 |
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| `vokillm-0.5b-instruct-q4_0.gguf` | Q4_0 | 428730368 |
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| `vokillm-0.5b-instruct-iq4_xs.gguf` | IQ4_XS | 428020736 |
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| `vokillm-0.5b-instruct-q3_k_m.gguf` | Q3_K_M | 432041984 |
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| `vokillm-0.5b-instruct-iq3_xxs.gguf` | IQ3_XXS | 416919296 |
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| `vokillm-0.5b-instruct-iq3_s.gguf` | IQ3_S | 415182848 |
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| `vokillm-0.5b-instruct-q2_k.gguf` | Q2_K | 415182848 |
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| `vokillm-0.5b-instruct-q3_k_s.gguf` | Q3_K_S | 414838784 |
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| `vokillm-0.5b-instruct-iq2_s.gguf` | IQ2_S | 402312928 |
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| `vokillm-0.5b-instruct-iq2_xs.gguf` | IQ2_XS | 400985056 |
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| `vokillm-0.5b-instruct-iq2_xxs.gguf` | IQ2_XXS | 398125024 |
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| `vokillm-0.5b-instruct-iq1_s.gguf` | IQ1_S | 392404960 |
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## Как запустить
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## Про квантовки IQ2/IQ1
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Квантовки `IQ2_*` и `IQ1_S` сделаны с **importance matrix (imatrix)** (это повышает качество для “экстремальных” квантовок).
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- instruct
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---
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# VokiLLM-0.5B-Instruct (GGUF)
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Готовые файлы модели **VokiLLM-0.5B-Instruct** в формате **GGUF**: FP16 и квантовки для `llama.cpp`/LM Studio и других совместимых рантаймов.
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## Файлы
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Файлы лежат **в корне репозитория**. В таблице ниже названия — это **ссылки на скачивание**.
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| Файл | Квантование | Размер (bytes) |
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| [`vokillm-0.5b-instruct-fp16.gguf`](vokillm-0.5b-instruct-fp16.gguf?download=true) | FP16 | 1266425856 |
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| [`vokillm-0.5b-instruct-q8_0.gguf`](vokillm-0.5b-instruct-q8_0.gguf?download=true) | Q8_0 | 675710976 |
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| [`vokillm-0.5b-instruct-q6_k.gguf`](vokillm-0.5b-instruct-q6_k.gguf?download=true) | Q6_K | 650379296 |
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| [`vokillm-0.5b-instruct-q5_k_m.gguf`](vokillm-0.5b-instruct-q5_k_m.gguf?download=true) | Q5_K_M | 522186752 |
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| [`vokillm-0.5b-instruct-q5_1.gguf`](vokillm-0.5b-instruct-q5_1.gguf?download=true) | Q5_1 | 521348096 |
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| [`vokillm-0.5b-instruct-q5_k_s.gguf`](vokillm-0.5b-instruct-q5_k_s.gguf?download=true) | Q5_K_S | 514810880 |
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| [`vokillm-0.5b-instruct-q5_0.gguf`](vokillm-0.5b-instruct-q5_0.gguf?download=true) | Q5_0 | 490475520 |
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| [`vokillm-0.5b-instruct-q4_k_m.gguf`](vokillm-0.5b-instruct-q4_k_m.gguf?download=true) | Q4_K_M | 491400192 |
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| [`vokillm-0.5b-instruct-q4_k_s.gguf`](vokillm-0.5b-instruct-q4_k_s.gguf?download=true) | Q4_K_S | 479064064 |
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| [`vokillm-0.5b-instruct-q4_1.gguf`](vokillm-0.5b-instruct-q4_1.gguf?download=true) | Q4_1 | 459602944 |
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| [`vokillm-0.5b-instruct-q3_k_l.gguf`](vokillm-0.5b-instruct-q3_k_l.gguf?download=true) | Q3_K_L | 445933568 |
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| [`vokillm-0.5b-instruct-iq4_nl.gguf`](vokillm-0.5b-instruct-iq4_nl.gguf?download=true) | IQ4_NL | 430880768 |
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| [`vokillm-0.5b-instruct-q4_0.gguf`](vokillm-0.5b-instruct-q4_0.gguf?download=true) | Q4_0 | 428730368 |
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| [`vokillm-0.5b-instruct-iq4_xs.gguf`](vokillm-0.5b-instruct-iq4_xs.gguf?download=true) | IQ4_XS | 428020736 |
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| [`vokillm-0.5b-instruct-q3_k_m.gguf`](vokillm-0.5b-instruct-q3_k_m.gguf?download=true) | Q3_K_M | 432041984 |
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| [`vokillm-0.5b-instruct-iq3_xxs.gguf`](vokillm-0.5b-instruct-iq3_xxs.gguf?download=true) | IQ3_XXS | 416919296 |
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| [`vokillm-0.5b-instruct-iq3_s.gguf`](vokillm-0.5b-instruct-iq3_s.gguf?download=true) | IQ3_S | 415182848 |
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| [`vokillm-0.5b-instruct-q2_k.gguf`](vokillm-0.5b-instruct-q2_k.gguf?download=true) | Q2_K | 415182848 |
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| [`vokillm-0.5b-instruct-q3_k_s.gguf`](vokillm-0.5b-instruct-q3_k_s.gguf?download=true) | Q3_K_S | 414838784 |
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| [`vokillm-0.5b-instruct-iq2_s.gguf`](vokillm-0.5b-instruct-iq2_s.gguf?download=true) | IQ2_S | 402312928 |
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| [`vokillm-0.5b-instruct-iq2_xs.gguf`](vokillm-0.5b-instruct-iq2_xs.gguf?download=true) | IQ2_XS | 400985056 |
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| [`vokillm-0.5b-instruct-iq2_xxs.gguf`](vokillm-0.5b-instruct-iq2_xxs.gguf?download=true) | IQ2_XXS | 398125024 |
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| [`vokillm-0.5b-instruct-iq1_s.gguf`](vokillm-0.5b-instruct-iq1_s.gguf?download=true) | IQ1_S | 392404960 |
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## Как запустить
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## Про квантовки IQ2/IQ1
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Квантовки `IQ2_*` и `IQ1_S` сделаны с **importance matrix (imatrix)** (это повышает качество для “экстремальных” квантовок).
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