Text Generation
Safetensors
GGUF
Russian
English
mistral3
reasoning
deepseek-r1
ru-deepthink-11k
mistral
conversational
Instructions to use fwizzer1/Fwizzer-R1-3B-RU 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 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 # Run inference directly in the terminal: llama cli -hf fwizzer1/Fwizzer-R1-3B-RU
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 # Run inference directly in the terminal: llama cli -hf fwizzer1/Fwizzer-R1-3B-RU
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 # Run inference directly in the terminal: ./llama-cli -hf fwizzer1/Fwizzer-R1-3B-RU
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 # Run inference directly in the terminal: ./build/bin/llama-cli -hf fwizzer1/Fwizzer-R1-3B-RU
Use Docker
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU
- LM Studio
- Jan
- vLLM
How to use fwizzer1/Fwizzer-R1-3B-RU 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" # 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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU
- Ollama
How to use fwizzer1/Fwizzer-R1-3B-RU with Ollama:
ollama run hf.co/fwizzer1/Fwizzer-R1-3B-RU
- Unsloth Desktop
- Docker Model Runner
How to use fwizzer1/Fwizzer-R1-3B-RU with Docker Model Runner:
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU
- Lemonade
How to use fwizzer1/Fwizzer-R1-3B-RU with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fwizzer1/Fwizzer-R1-3B-RU
Run and chat with the model
lemonade run user.Fwizzer-R1-3B-RU-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 1,944 Bytes
cd0b25a e5d6131 cd0b25a e5d6131 cd0b25a e5d6131 cd0b25a e5d6131 | 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",
"name": "Fwizzer-R1-3B-RU (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
}
]
}
} |