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

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

fwizzer1 commited on Aug 31

Commit

303e507

verified ·

1 Parent(s): 85889a1

Update README with official reasoning tags and description

Browse files

Files changed (1) hide show

README.md +21 -13

README.md CHANGED Viewed

@@ -1,21 +1,29 @@
 ---
 tags:
 - gguf
-- llama.cpp
-- unsloth
 ---
-# ministral-3b-reasoning-ru : GGUF
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf fwizzer1/ministral-3b-reasoning-ru --jinja`
-- For multimodal models: `llama-mtmd-cli -hf fwizzer1/ministral-3b-reasoning-ru --jinja`
-## Available Model files:
-- `Ministral-3-3B-Instruct-2512.Q8_0.gguf`
-- `Ministral-3-3B-Instruct-2512.Q4_K_M.gguf`
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+language:
+- ru
+- en
+license: apache-2.0
 tags:
+- reasoning
+- deepseek-r1
+- think
+- mistral
 - gguf
+- lmstudio
+pipeline_tag: text-generation
+base_model: mistralai/Ministral-3-3B-Instruct-2512
 ---
+# 🧠 Fwizzer-R1-3B-RU
+**Fwizzer-R1-3B-RU** — это мощная русскоязычная мыслящая языковая модель на базе архитектуры Ministral 3B, дообученная на датасете `fwizzer1/ru-deepthink-11k` (11 000 пошаговых reasoning-диалогов).
+## ✨ Особенности:
+- **Пошаговое мышление (`<think>...</think>`)**: Модель глубоко анализирует краевые случаи, проверяет формулы и планирует архитектуру перед выдачей ответа.
+- **Идеально для кода и логики**: Высокая точность в Java, Minecraft Modding (Forge/Fabric), алгоритмах и олимпиадных задачах.
+- **Сверхбыстрая на обычных GPU**: Работает со скоростью 80+ токенов/сек на RTX 3050 (4GB VRAM).
+## 🚀 Использование в LM Studio:
+1. Найдите `fwizzer1/Fwizzer-R1-3B-RU` в поиске LM Studio.
+2. Скачайте квантование `Q4_K_M` (2.05 GB).
+3. Задавайте любые вопросы по коду и логике!