Text Generation
Safetensors
GGUF
English
Russian
mistral3
reasoning
r1
deepseek-r1
ministral
cot
chain-of-thought
english
code
math
unsloth
llama.cpp
lmstudio
ollama
vllm
jan
openclaw
hermes-agent
conversational
Eval Results (legacy)
Instructions to use fwizzer1/Fwizzer-R1-3B-EN-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-EN-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-EN-v2 # Run inference directly in the terminal: llama cli -hf fwizzer1/Fwizzer-R1-3B-EN-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-EN-v2 # Run inference directly in the terminal: llama cli -hf fwizzer1/Fwizzer-R1-3B-EN-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-EN-v2 # Run inference directly in the terminal: ./llama-cli -hf fwizzer1/Fwizzer-R1-3B-EN-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-EN-v2 # Run inference directly in the terminal: ./build/bin/llama-cli -hf fwizzer1/Fwizzer-R1-3B-EN-v2
Use Docker
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-EN-v2
- LM Studio
- Jan
- vLLM
How to use fwizzer1/Fwizzer-R1-3B-EN-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-EN-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-EN-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-EN-v2
- Ollama
How to use fwizzer1/Fwizzer-R1-3B-EN-v2 with Ollama:
ollama run hf.co/fwizzer1/Fwizzer-R1-3B-EN-v2
- Unsloth Desktop
- Docker Model Runner
How to use fwizzer1/Fwizzer-R1-3B-EN-v2 with Docker Model Runner:
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-EN-v2
- Lemonade
How to use fwizzer1/Fwizzer-R1-3B-EN-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fwizzer1/Fwizzer-R1-3B-EN-v2
Run and chat with the model
lemonade run user.Fwizzer-R1-3B-EN-v2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Download tokenizer.json from fwizzer1/Fwizzer-R1-3B-EN-v2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/fwizzer1/Fwizzer-R1-3B-EN-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://fwizzer1/Fwizzer-R1-3B-EN-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/fwizzer1/Fwizzer-R1-3B-EN-v2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 187c8f6cc6fd217e11ecc3dfbc82048b314c6d4608b6b6eb42769e12728cd987
- Size of remote file:
- 17.1 MB
- SHA256:
- 577575622324b2e099e2648be26bdeb5e5815ffe66d7004e9e3ddbf421db6bf1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.