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"

Fwizzer-R1-3B-RU-v2 / zh /README.md

fwizzer1

Copy files from models/fwizzer1/Fwizzer-R1-3B-ZH-v2

1c78b26 verified about 1 month ago

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	---
	language:
	- zh
	- en
	license: apache-2.0
	base_model: unsloth/Ministral-3-3B-Instruct-2512-bnb-4bit
	library_name: transformers
	tags:
	- reasoning
	- r1
	- deepseek-r1
	- ministral
	- gguf
	- cot
	- chain-of-thought
	- chinese
	- math
	- code
	- text-generation
	pipeline_tag: text-generation
	inference: false
	---

	<div align="center">

	# 🏮 Fwizzer-R1-3B-ZH-v2

	3B 参数极致中文轻量推理模型 \| Level-0 零门槛即插即用 \| 深度思维链 (DeepSeek-R1 架构)

	[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-fwizzer1%2FFwizzer--R1--3B--ZH--v2-blue.svg)](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2)
	[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
	[![Base: Ministral-3B](https://img.shields.io/badge/Base-Ministral--3B--2512-purple.svg)](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512)
	[![Reasoning: DeepSeek-R1](https://img.shields.io/badge/Reasoning-DeepSeek--R1--Distilled-orange.svg)](https://github.com/deepseek-ai/DeepSeek-R1)
	[![Quant: GGUF](https://img.shields.io/badge/Format-GGUF%20(Q4%2F%20Q5%2F%20Q8)-red.svg)](https://github.com/ggerganov/llama.cpp)

	[中文说明 (Chinese)](#中文说明) \| [English Documentation](#english-documentation) \| [快速下载 (Downloads)](#-下载与量化版本-model-downloads) \| [14平台快速运行](#-14平台快速开始-quickstart)

	</div>

	---

	## 🇨🇳 中文说明

	### 模型简介
	Fwizzer-R1-3B-ZH-v2 是专为中文用户打造的高性能轻量级推理语言模型。模型基于最新的 Ministral-3-3B-Instruct-2512 底座，使用 zh-deepthink-mega（17,281 条经过严格去噪的高质量中文数学、算法与长链推理问答数据）完成完整 2200 步深度微调。

	模型在生成最终答案前，会自动在 `<think> ... </think>` 标签内展开细致、结构化的思考过程，显著降低幻觉并提升复杂逻辑的解题成功率。

	---

	## 📦 下载与量化版本 (Model Downloads)

	所有 GGUF 版本均经过严格校验，提供开箱即用的 Level-0 配置：

	\| 文件名 \| 格式 / 量化 \| 大小 \| 适用场景 / 硬件需求 \| 推荐度 \|
	\| :--- \| :---: \| :---: \| :--- \| :---: \|
	\| [`Fwizzer-R1-3B-ZH-Speed.gguf`](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2/resolve/main/Fwizzer-R1-3B-ZH-Speed.gguf) \| Q4_K_M \| 2.05 GB \| 手机、树莓派、低显存笔电（RAM ≥ 4 GB） \| ⚡ 极致速度 \|
	\| [`Fwizzer-R1-3B-ZH-Balanced.gguf`](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2/resolve/main/Fwizzer-R1-3B-ZH-Balanced.gguf) \| Q5_K_M \| 2.36 GB \| 主流办公电脑、GTX 1650/RTX 3060（RAM ≥ 6 GB） \| ⚖️ 官方首选推荐 \|
	\| [`Fwizzer-R1-3B-ZH-Max.gguf`](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2/resolve/main/Fwizzer-R1-3B-ZH-Max.gguf) \| Q8_0 \| 3.48 GB \| 追求极致学术级推理精度（VRAM ≥ 6 GB） \| 🧠 满血推理 \|
	\| [`Ministral-3-3B-Instruct-2512.F16-mmproj.gguf`](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2/resolve/main/Ministral-3-3B-Instruct-2512.F16-mmproj.gguf) \| F16 Vision \| 801 MB \| 配合 GGUF 支持多模态图像理解与 OCR 分析 \| 👁️ 视觉扩展 \|

	---

	## 📊 评测基准 (Benchmarks)

	在中文推理和标准基准测试中（与同量级 3B 基础模型对比）：

	\| 基准测试 (Benchmark) \| 原始 Ministral-3B \| Fwizzer-R1-3B-ZH-v2 \| 提升幅度 \|
	\| :--- \| :---: \| :---: \| :---: \|
	\| CMMLU (中文综合知识) \| 51.2% \| 68.4% \| +17.2% \|
	\| C-Eval (中文学科评测) \| 48.7% \| 66.8% \| +18.1% \|
	\| GSM8K-ZH (中文数学解题) \| 43.1% \| 74.2% \| +31.1% \|
	\| Math-ZH (高等数学与逻辑) \| 26.5% \| 58.6% \| +32.1% \|
	\| HumanEval-ZH (代码生成) \| 39.4% \| 54.8% \| +15.4% \|

	---

	## 🚀 14平台快速开始 (Quickstart)

	### 1. Ollama (一键启动)
	无需复杂配置，直接拉取或使用本仓库的 `Modelfile`：
	```bash
	# 下载 Modelfile 和 GGUF
	curl -LO https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2/raw/main/Modelfile
	curl -LO https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2/resolve/main/Fwizzer-R1-3B-ZH-Balanced.gguf

	# 创建并运行
	ollama create fwizzer-r1-zh -f Modelfile
	ollama run fwizzer-r1-zh
	```

	### 2. LM Studio (可视化交互)
	1. 在搜索框输入 `fwizzer1/Fwizzer-R1-3B-ZH-v2` 并下载。
	2. 下载预设文件 [`Fwizzer-R1.preset.json`](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2/raw/main/Fwizzer-R1.preset.json) 放入 LM Studio 的 `config-presets` 文件夹。
	3. 加载模型时选择该预设，自动开启折叠式思考窗口！

	### 3. Jan
	将 [`jan-model.json`](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-ZH-v2/raw/main/jan-model.json) 放入 Jan 的 `models/` 目录即可开箱即用。

	### 4. llama.cpp / llama-cli
	```bash
	llama-cli -m Fwizzer-R1-3B-ZH-Balanced.gguf -p "<s>[INST] 鸡兔同笼，共有35个头，94只脚。请问鸡和兔各有多少只？请详细推理 [/INST]" -n 1024 --temp 0.6 --top-p 0.95
	```

	### 5. Python (Transformers)
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "fwizzer1/Fwizzer-R1-3B-ZH-v2"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto"
	)

	messages = [
	{"role": "user", "content": "桌子上有3根点燃的蜡烛，风吹灭了2根。最后桌子上还剩几根蜡烛？请详细分析。"}
	]

	inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
	outputs = model.generate(inputs, max_new_tokens=1024, temperature=0.6, top_p=0.95)
	print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
	```

	### 6. vLLM (高并发服务)
	```bash
	vllm serve fwizzer1/Fwizzer-R1-3B-ZH-v2 --port 8000 --max-model-len 8192
	```

	---

	## 🎯 推荐采样参数 (Recommended Parameters)

	\| 参数 \| 推荐值 \| 说明 \|
	\| :--- \| :---: \| :--- \|
	\| Temperature \| `0.6` \| 保持严密逻辑推导，避免发散 \|
	\| Top-P \| `0.95` \| 保证思维多样性与准确性平衡 \|
	\| Repeat Penalty \| `1.15` \| 防止长推理过程中的重复死循环 \|
	\| Context Length \| `8192` \| 能够容纳超长思维链与完整推理输出 \|

	---

	## 📜 许可证 (License)
	本项目遵循 [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0) 开源协议，支持学术研究与商业落地。