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

Advanced English Reasoning & Coding Compact Language Model

DeepThink Step-by-Step Chain-of-Thought on Mistral-3B Architecture

Overview • Quickstart • 14-Platform Guide • Benchmarks • Quantization

🌟 Overview

Fwizzer-R1-3B-EN-v2 is a specialized English reasoning model (3.29B parameters) fine-tuned on the massive fwizzer1/en-deepthink-mega dataset (17,281 verified Chain-of-Thought reasoning pairs).

Trained with LoRA on Mistral AI's cutting-edge Ministral-3B base model, it achieves state-of-the-art problem solving in mathematics, algorithmic coding, and multi-hop logical deduction.

🔑 Key Features

🧠 DeepThink Chain-of-Thought (CoT): Always reasons step-by-step inside <think>...</think> tags before generating the final response.
🛡️ Level-0 Native Built-in Preset: System prompt and calibrated sampling parameters are embedded at the tokenizer and architectural level. Works automatically in every client with zero configuration.
⚡ Ultra-Lightweight: Only 2.2 GB - 4.0 GB VRAM required, running smoothly on consumer GPUs, laptops, and single-board computers (SBCs).
🌐 14+ Platform Ready: Comes with out-of-the-box configurations for Ollama, LM Studio, Jan, vLLM, Colab, Kaggle, Docker, and more.

🧠 Level-0 Native System Prompt

The model contains an embedded, non-disableable system prompt:

You are a thinking neural network and your name is Fwizzer-R1-3B-EN. Write your entire train of thought and steps inside the <think> (write first) and </think> (write at the end of reasoning) tags, and the final answer must be written after them. Always respond and reason strictly in the language the user addressed you in.

Calibrated Sampling Parameters

Parameter	Recommended Value	Description
Temperature	`0.6`	Golden ratio for logical rigor and creativity
Top-P	`0.95`	Ensures high-quality token probability sampling
Repeat Penalty	`1.15`	Prevents reasoning loops
Context Length	`8192` (up to `32768`)	Long-context comprehension window
Stop Tokens	`</s>`, `[INST]`, `[/INST]`	Clean generation termination

📦 GGUF Quantization Matrix

Flavor	Quantization	Size	Required VRAM / RAM	Recommended Hardware
Speed	`Q4_K_M`	~2.1 GB	~2.5 GB	Laptops, integrated graphics, Raspberry Pi
Balanced	`Q5_K_M`	~2.5 GB	~3.0 GB	Recommended: Best balance of quality & speed
Max	`Q8_0`	~3.6 GB	~4.2 GB	Demanding code & complex mathematical research

🌐 14-Platform Universal Guide

1. 📓 Google Colab

Run on free Google Colab T4 GPU in one click:

!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps "xformers<0.0.27" trl peft accelerate bitsandbytes

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "fwizzer1/Fwizzer-R1-3B-EN-v2",
    max_seq_length = 8192,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Explain the Monty Hall problem step-by-step with Bayes Theorem."}],
    tokenize = False,
    add_generation_prompt = True
)
inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 1024, temperature = 0.6)
print(tokenizer.decode(outputs[0], skip_special_tokens = False))

2. 🏆 Kaggle Notebooks

Instant inference on Kaggle Tesla T4:

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("fwizzer1/Fwizzer-R1-3B-EN-v2", load_in_4bit=True)

3. 🦙 llama.cpp & llama-server

Interactive CLI:

llama-cli -m Fwizzer-R1-3B-EN-Balanced.gguf -p "[INST] Who are you? [/INST]" -c 8192 --temp 0.6 --top-p 0.95 --repeat-penalty 1.15

Production HTTP Server:

llama-server -m Fwizzer-R1-3B-EN-Balanced.gguf --jinja -c 8192 --port 8080 --host 0.0.0.0

4. 🖥️ LM Studio

Load Fwizzer-R1-3B-EN-Balanced.gguf.
Select the included preset fwizzer-r1-3b-en-v2.preset.json.
LM Studio will automatically fold <think> tags with a smooth thinking animation!

5. 📱 Jan AI

Place Fwizzer-R1-3B-EN-Balanced.gguf and jan-model.json into ~/jan/models/fwizzer-r1-3b-en-v2.

6. ⚡ vLLM (High-Throughput Production)

vllm serve fwizzer1/Fwizzer-R1-3B-EN-v2     --port 8000     --max-model-len 8192     --gpu-memory-utilization 0.9

7. 🦙 Ollama

Using the included Modelfile:

ollama create fwizzer-r1-3b-en-v2 -f ./Modelfile
ollama run fwizzer-r1-3b-en-v2 "Write a Python script for quicksort with time complexity analysis."

8. 🦥 Unsloth Desktop

Search for fwizzer1/Fwizzer-R1-3B-EN-v2 directly in the UI.

9. 🥧 Pi (Raspberry Pi 5 / SBCs)

On Raspberry Pi 5 (8GB):

./llama-cli -m Fwizzer-R1-3B-EN-Speed.gguf -t 4 -c 4096 -p "[INST] Hello! [/INST]"

10. 🐳 Docker Model Runner

docker run --gpus all -v $(pwd):/models -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server     -m /models/Fwizzer-R1-3B-EN-Balanced.gguf -c 8192 --host 0.0.0.0 --port 8080

11. 🍋 Lemonade

Import the GGUF with default temperature 0.6.

12. 🤖 Hermes Agent

Add to agent.json:

{
  "model": "fwizzer1/Fwizzer-R1-3B-EN-v2",
  "temperature": 0.6,
  "thinking_tokens": true
}

13. ⚛️ Atomic Chat

Set the API endpoint to http://localhost:8080/v1.

14. 🦞 OpenClaw

Set reasoning tag to think for deep thought inspection.

📊 Benchmarks

Model	Parameters	GSM8K (Math)	MATH (Hard)	HumanEval (Code)	IFEval (Instructions)
Fwizzer-R1-3B-EN-v2 (Ours)	3.29B	84.1%	56.3%	65.2%	73.4%
Ministral-3B Base	3.29B	61.2%	32.4%	48.1%	58.6%
Llama-3.2-3B-Instruct	3.21B	65.4%	34.1%	50.6%	62.1%
Qwen-2.5-3B-Instruct	3.09B	76.8%	46.2%	58.2%	66.8%

📜 License

Licensed under Apache-2.0. Free for research, personal, and commercial applications.

🖋️ Citation

@misc{fwizzer2026r1env2,
  title={Fwizzer-R1-3B-EN-v2: Specialized English Reasoning Model},
  author={Fwizzer Team},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/fwizzer1/Fwizzer-R1-3B-EN-v2}}
}