Instructions to use fwizzer1/Fwizzer-R1-3B-RU-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-RU-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-RU-v2
# Run inference directly in the terminal:
llama cli -hf fwizzer1/Fwizzer-R1-3B-RU-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-RU-v2
# Run inference directly in the terminal:
llama cli -hf fwizzer1/Fwizzer-R1-3B-RU-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-RU-v2
# Run inference directly in the terminal:
./llama-cli -hf fwizzer1/Fwizzer-R1-3B-RU-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-RU-v2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2

Use Docker

docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2

LM Studio
Jan

vLLM

How to use fwizzer1/Fwizzer-R1-3B-RU-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-RU-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-RU-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2

Ollama
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Ollama:
```
ollama run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2
```
Unsloth Desktop

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "fwizzer1/Fwizzer-R1-3B-RU-v2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Docker Model Runner:
```
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2
```

Lemonade

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fwizzer1/Fwizzer-R1-3B-RU-v2

Run and chat with the model

lemonade run user.Fwizzer-R1-3B-RU-v2-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default fwizzer1/Fwizzer-R1-3B-RU-v2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "fwizzer1/Fwizzer-R1-3B-RU-v2" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

fwizzer1 commited on Sep 7

Commit

eed2f21

verified ·

1 Parent(s): 0703405

Fix links in README to match root GGUF filenames

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -126,9 +126,9 @@ model-index:
 | Файл | Квантование | Размер | Требуемая VRAM / RAM | Назначение |
 | :--- | :--- | :--- | :--- | :--- |
-| [**`ru/Fwizzer-R1-3B-RU-v2Speed.gguf`**](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-RU-v2/resolve/main/ru/Fwizzer-R1-3B-RU-v2Speed.gguf) | `Q4_K_M` | 2.1 ГБ | ~2.5 ГБ | Максимальная скорость, слабые ПК, мобильные устройства |
-| [**`ru/Fwizzer-R1-3B-RU-v2Balanced.gguf`**](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-RU-v2/resolve/main/ru/Fwizzer-R1-3B-RU-v2Balanced.gguf) | `Q5_K_M` | 2.5 ГБ | ~3.0 ГБ | **Рекомендуемый выбор**: идеальный баланс качества и скорости |
-| [**`ru/Fwizzer-R1-3B-RU-v2Max.gguf`**](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-RU-v2/resolve/main/ru/Fwizzer-R1-3B-RU-v2Max.gguf) | `Q8_0` | 3.6 ГБ | ~4.2 ГБ | Максимальная точность математики и сложного кода |
 ---
@@ -168,11 +168,11 @@ model, tokenizer = FastLanguageModel.from_pretrained("fwizzer1/Fwizzer-R1-3B-RU-
 ### 3. 🦙 llama.cpp & llama-server
 #### Интерактивный CLI:
 ```bash
-llama-cli -m ru/Fwizzer-R1-3B-RU-v2Balanced.gguf -p "[INST] Привет! Кто ты? [/INST]" -c 8192 --temp 0.6 --top-p 0.95 --repeat-penalty 1.15
 ```
 #### Высокопроизводительный сервер:
 ```bash
-llama-server -m ru/Fwizzer-R1-3B-RU-v2Balanced.gguf --jinja -c 8192 --port 8080 --host 0.0.0.0
 ```
 ### 4. 🖥️ LM Studio
@@ -204,13 +204,13 @@ ollama run fwizzer-r1-3b-v2 "Напиши алгоритм Дейкстры на
 ### 9. 🥧 Pi (Raspberry Pi 5 / Одноплатники)
 На 8GB Raspberry Pi 5 модель Speed выдаёт отличную скорость:
 ```bash
-./llama-cli -m ru/Fwizzer-R1-3B-RU-v2Speed.gguf -t 4 -c 4096 -p "[INST] Привет! [/INST]"
 ```
 ### 10. 🐳 Docker Model Runner
 ```bash
 docker run --gpus all -v $(pwd):/models -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server \
-    -m /models/ru/Fwizzer-R1-3B-RU-v2Balanced.gguf -c 8192 --host 0.0.0.0 --port 8080
 ```
 ### 11. 🍋 Lemonade

 | Файл | Квантование | Размер | Требуемая VRAM / RAM | Назначение |
 | :--- | :--- | :--- | :--- | :--- |
+| [**`Fwizzer-R1-3B-RU-Speed.gguf`**](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-RU-v2/resolve/main/Fwizzer-R1-3B-RU-Speed.gguf) | `Q4_K_M` | 2.1 ГБ | ~2.5 ГБ | Максимальная скорость, слабые ПК, мобильные устройства |
+| [**`Fwizzer-R1-3B-RU-Balanced.gguf`**](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-RU-v2/resolve/main/Fwizzer-R1-3B-RU-Balanced.gguf) | `Q5_K_M` | 2.5 ГБ | ~3.0 ГБ | **Рекомендуемый выбор**: идеальный баланс качества и скорости |
+| [**`Fwizzer-R1-3B-RU-Max.gguf`**](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-RU-v2/resolve/main/Fwizzer-R1-3B-RU-Max.gguf) | `Q8_0` | 3.6 ГБ | ~4.2 ГБ | Максимальная точность математики и сложного кода |
 ---
 ### 3. 🦙 llama.cpp & llama-server
 #### Интерактивный CLI:
 ```bash
+llama-cli -m Fwizzer-R1-3B-RU-Balanced.gguf -p "[INST] Привет! Кто ты? [/INST]" -c 8192 --temp 0.6 --top-p 0.95 --repeat-penalty 1.15
 ```
 #### Высокопроизводительный сервер:
 ```bash
+llama-server -m Fwizzer-R1-3B-RU-Balanced.gguf --jinja -c 8192 --port 8080 --host 0.0.0.0
 ```
 ### 4. 🖥️ LM Studio
 ### 9. 🥧 Pi (Raspberry Pi 5 / Одноплатники)
 На 8GB Raspberry Pi 5 модель Speed выдаёт отличную скорость:
 ```bash
+./llama-cli -m Fwizzer-R1-3B-RU-Speed.gguf -t 4 -c 4096 -p "[INST] Привет! [/INST]"
 ```
 ### 10. 🐳 Docker Model Runner
 ```bash
 docker run --gpus all -v $(pwd):/models -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server \
+    -m /models/Fwizzer-R1-3B-RU-Balanced.gguf -c 8192 --host 0.0.0.0 --port 8080
 ```
 ### 11. 🍋 Lemonade