Text Generation
Safetensors
GGUF
Russian
English
mistral3
reasoning
r1
deepseek-r1
ministral
cot
chain-of-thought
russian
code
math
unsloth
llama.cpp
lmstudio
ollama
vllm
jan
openclaw
hermes-agent
conversational
Eval Results (legacy)
Instructions to use fwizzer1/Fwizzer-R1-3B-RU-v2 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 fwizzer1/Fwizzer-R1-3B-RU-v2 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 fwizzer1/Fwizzer-R1-3B-RU-v2 # Run inference directly in the terminal: llama cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2 # Run inference directly in the terminal: llama cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2
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 fwizzer1/Fwizzer-R1-3B-RU-v2 # Run inference directly in the terminal: ./llama-cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2
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 fwizzer1/Fwizzer-R1-3B-RU-v2 # Run inference directly in the terminal: ./build/bin/llama-cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2
Use Docker
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2
- LM Studio
- Jan
- vLLM
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fwizzer1/Fwizzer-R1-3B-RU-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fwizzer1/Fwizzer-R1-3B-RU-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2
- Ollama
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Ollama:
ollama run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2
- Unsloth Desktop
- Pi
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "fwizzer1/Fwizzer-R1-3B-RU-v2" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Docker Model Runner:
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2
- Lemonade
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fwizzer1/Fwizzer-R1-3B-RU-v2
Run and chat with the model
lemonade run user.Fwizzer-R1-3B-RU-v2-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2
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 fwizzer1/Fwizzer-R1-3B-RU-v2
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "fwizzer1/Fwizzer-R1-3B-RU-v2" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 1,950 Bytes
629b050 5512bd3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | {
"identifier": "@local:fwizzer-r1-3b-ru-v2",
"name": "Fwizzer-R1-3B-RU-v2 (Thinking)",
"changed": true,
"operation": {
"fields": [
{
"key": "llm.prediction.systemPrompt",
"value": "Ты русскоязычный ИИ-ассистент Fwizzer-R1-3B-RU. Твоя задача — рассуждать глубоко и логично внутри тегов <think>(цепочка мыслей) и </think>(ответ пользователю), решая задачи шаг за шагом. Всегда отвечай на русском языке (или на языке запроса пользователя)."
},
{
"key": "llm.prediction.temperature",
"value": 0.6
},
{
"key": "llm.prediction.topP",
"value": 0.95
},
{
"key": "llm.prediction.repeatPenalty",
"value": 1.15
},
{
"key": "llm.prediction.llama.cpuThreads",
"value": 6
},
{
"key": "llm.prediction.reasoning.enableThinking",
"value": true
},
{
"key": "llm.prediction.reasoning.parsing",
"value": {
"enabled": true,
"startString": "<think>",
"endString": "</think>"
}
},
{
"key": "llm.prediction.reasoningParsing",
"value": {
"type": "tag",
"startTag": "<think>",
"endTag": "</think>"
}
},
{
"key": "llm.prediction.stopStrings",
"value": [
"</s>",
"[INST]",
"[/INST]"
]
}
]
},
"load": {
"fields": [
{
"key": "llm.load.contextLength",
"value": 8192
},
{
"key": "llm.load.llama.gpuOffload",
"value": {
"type": "max"
}
},
{
"key": "llm.load.llama.flashAttention",
"value": true
}
]
}
} |