Fwizzer-R1-3B-EN-v2

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🇬🇧 🧠 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

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🌟 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.

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🧠 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

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📦 GGUF Quantization Matrix

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🌐 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

1. Load Fwizzer-R1-3B-EN-Balanced.gguf.
2. Select the included preset fwizzer-r1-3b-en-v2.preset.json.
3. 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.

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📊 Benchmarks

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📜 License

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

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🖋️ 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}}
}