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

Fwizzer-R1-3B-RU / README.md

fwizzer1

Update README with Speed and Max filenames

b34cc3e verified 16 days ago

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metadata

language:
  - ru
  - en
license: apache-2.0
tags:
  - reasoning
  - deepseek-r1
  - ru-deepthink-11k
  - mistral
  - text-generation
  - gguf
pipeline_tag: text-generation

🧠 Fwizzer-R1-3B-RU

Fwizzer-R1-3B-RU — это мыслящая русскоязычная языковая модель, обученная по архитектуре пошаговых рассуждений (DeepSeek-R1 CoT) на отборном датасете fwizzer1/ru-deepthink-11k.

⚡️ Доступные версии GGUF

Файл	Описание	Рекомендуемое железо
`Fwizzer-R1-3B-Speed.gguf`	Быстрая версия (Q4_K_M, 2.15 GB)	RTX 3050 / Ноутбуки / 16GB RAM
`Fwizzer-R1-3B-Max.gguf`	Максимальная точность (Q8_0, 3.40 GB)	ПК с 8+ GB VRAM

🚀 Использование в LM Studio

Откройте LM Studio.
В строке поиска введите: Fwizzer-R1-3B-RU.
Нажмите Download на Fwizzer-R1-3B-Speed.gguf или Fwizzer-R1-3B-Max.gguf.
Модель готова к работе со шторкой размышлений!

Вшитый системный промпт:

Ты думающая нейросеть а зовут тебя Fwizzer-R1-3B-RU. Весь ход мыслей и шаги пиши внутри тегов <think>(напиши сначала) и </think>(напиши по окончанию рассуждений), а итоговый ответ — обязательно после них.

💻 Использование через Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="fwizzer1/Fwizzer-R1-3B-RU",
    filename="Fwizzer-R1-3B-Speed.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,
    flash_attn=True
)

response = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "Привет! Расскажи о себе и реши задачу на логику."}
    ]
)

print(response["choices"][0]["message"]["content"])