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
Commit History
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Update Fwizzer-R1-3B-Speed.gguf with embedded reasoning system prompt and enable_thinking
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Synchronize verified Fwizzer-R1-3B-Speed GGUF with embedded think prefill
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Add official Fwizzer-R1 LM Studio preset
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Update chat_template.jinja with Fwizzer identity and <think> prefill
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Update Fwizzer-R1-3B-Speed.gguf with clean explicit <think> generation
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Fix chat template so model outputs <think> explicitly for UI reasoning parsers
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Update README with Speed and Max filenames
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Update model.yaml with Speed and Max filenames
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Rename model files to Fwizzer-R1-3B-Max.gguf and Fwizzer-R1-3B-Speed.gguf
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Rename Q8_0 file to Fwizzer-R1-3B.Q8_0.gguf
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Inject baked Fwizzer-R1 system prompt directly into GGUF binary header
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Update README with full documentation and reasoning tags
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Add model.yaml with reasoning: true, thinking: true, deepseek format
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Bake exact Fwizzer-R1 system prompt into chat_template.jinja
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Clean up Fwizzer-R1-3B-Instruct-2512.Q4_K_M.gguf
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Update model.yaml: Standard UI mode for Fwizzer-R1-3B
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Update chat_template.jinja: Name Fwizzer-R1-3B without forced thinking
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Add clean Fwizzer-R1-3B.Q4_K_M.gguf (Name: Fwizzer-R1-3B, standard chat without forced thinking)
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Remove old Ministral-named Q4_K_M duplicate
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Add Fwizzer-R1-3B-Instruct-2512.Q4_K_M.gguf with baked metadata
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Bake Fwizzer-R1-3B-RU name and reasoning chat_template directly into GGUF binary
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Enable 1M Context (1,048,576 tokens) with YaRN RoPE scaling
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Enable 1M Context (1048576 tokens) in model.yaml
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Update README: generalized code generation description
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Publish professional English README with benchmarks, GGUF table and quickstart
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Set reasoning: true, thinking: true and deepseek format in model.yaml
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Add native deepseek reasoning token format to tokenizer_config.json
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Enable native reasoning and thinking capability flags in config.json
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Update README with official reasoning tags and description
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Add LM Studio model.yaml with reasoning: true
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Set default_system_prompt to Fwizzer-R1-3B-RU in tokenizer_config.json
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Bake Fwizzer-R1-3B-RU identity and system prompt
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Bake reasoning chat_template and system prompt into tokenizer_config.json
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Set built-in default reasoning system prompt and auto <think> generation
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Add README
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