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
Add official Fwizzer-R1 LM Studio preset
Browse files- Fwizzer-R1.preset.json +67 -0
Fwizzer-R1.preset.json
ADDED
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{
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"identifier": "@local:fwizzer-r1",
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"name": "Fwizzer-R1-3B-RU",
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"changed": true,
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"operation": {
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"fields": [
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{
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"key": "llm.prediction.systemPrompt",
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"value": "Ты думающая нейросеть а зовут тебя Fwizzer-R1-3B-RU. Весь ход мыслей и шаги пиши внутри тегов <think>(напиши сначала) и </think>(напиши по окончанию рассуждений), а итоговый ответ — обязательно после них."
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},
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{
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"key": "llm.prediction.temperature",
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"value": 0.6
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},
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{
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"key": "llm.prediction.topP",
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"value": 0.95
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},
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{
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"key": "llm.prediction.repeatPenalty",
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"value": 1.15
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},
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{
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"key": "llm.prediction.llama.cpuThreads",
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"value": 6
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},
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{
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"key": "llm.prediction.reasoning.enableThinking",
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"value": true
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},
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{
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"key": "llm.prediction.reasoning.parsing",
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"value": {
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"enabled": true,
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"startString": "<think>",
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"endString": "</think>"
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}
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},
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{
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"key": "llm.prediction.reasoningParsing",
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"value": {
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"type": "tag",
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"startTag": "<think>",
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"endTag": "</think>"
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}
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}
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]
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},
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"load": {
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"fields": [
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{
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"key": "llm.load.contextLength",
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"value": 8192
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},
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{
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"key": "llm.load.llama.gpuOffload",
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"value": {
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"type": "max"
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}
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},
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{
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"key": "llm.load.llama.flashAttention",
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"value": true
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}
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]
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}
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}
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