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

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

Fwizzer-R1-3B-EN-v2

File size: 697 Bytes

{
  "image_break_token": "[IMG_BREAK]",
  "image_end_token": "[IMG_END]",
  "image_processor": {
    "data_format": "channels_first",
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_rescale": true,
    "do_resize": true,
    "image_mean": [
      0.48145466,
      0.4578275,
      0.40821073
    ],
    "image_processor_type": "PixtralImageProcessor",
    "image_std": [
      0.26862954,
      0.26130258,
      0.27577711
    ],
    "patch_size": 14,
    "resample": 3,
    "rescale_factor": 0.00392156862745098,
    "size": {
      "longest_edge": 1540
    }
  },
  "image_token": "[IMG]",
  "patch_size": 14,
  "processor_class": "PixtralProcessor",
  "spatial_merge_size": 2
}