---
library_name: transformers
license: apache-2.0
language:
- pt
- en
base_model:
- unsloth/Qwen3-4B-Base
pipeline_tag: text-generation
datasets:
- nvidia/OpenMathReasoning
---
# 🧠 DogeAI-v2.0-4B-Reasoning
**"The Small Model That Thinks Big."**

DogeAI-v2.0-4B-Reasoning is a high-efficiency model optimized for **Chain-of-Thought (CoT)**. Built by [AxionLab-Co](https://huggingface.co), it merges a specialized reasoning LoRA onto the powerful **Qwen3-4B-Base** architecture, delivering structured, step-by-step analytical capabilities in a compact 4B footprint.

### 🚀 Key Highlights
- **Architecture:** Decoder-only Transformer (Qwen3 Base).
- **Core Strength:** Multi-step logical reasoning and structured problem solving.
- **Hardware Friendly:** Optimized for local inference (Low VRAM usage).
- **Final Merge:** No LoRA dependency; ready for production or GGUF conversion.

---

## 🎯 Use Cases
- **Complex Problem Solving:** Math, logic, and analytical tasks.
- **Detailed Explanations:** When you need the "why" and "how", not just the "what".
- **Local Agents:** High-performance reasoning for edge devices and local LLM setups.

---

## 🛠️ Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "AxionLab-Co/DogeAI-v2.0-4B-Reasoning"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    device_map="auto", 
    torch_dtype=torch.bfloat16 # Recommended for Qwen3
)

prompt = "Solve this step-by-step: If a train leaves at 2 PM at 60mph, and another..."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs, 
    max_new_tokens=512, 
    temperature=0.3, # Lower temp recommended for reasoning
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

```

🏋️ Training & Methodology
Our goal at AxionLab was to prioritize Depth of Thought over mere textual fluency.
Dataset: A curated mix of synthetic CoT datasets and manually pre-processed logical reasoning prompts.
Fine-tuning: Performed on Kaggle GPUs using PEFT (LoRA) with a focus on preserving the base model's knowledge while injecting structured logic.
Optimization: Mixed precision (fp16) with a final merge_and_unload for seamless deployment.

📊 Evaluation Results
In qualitative testing, DogeAI-v2.0-4B shows:
Higher Logical Consistency compared to the stock Qwen3-4B-Base.
Reduced Hallucination in multi-step word problems.
Structured Verbosity: It "thinks" before it answers.

⚠️ Limitations & Bias
Reasoning Loops: The model might occasionally over-explain simple tasks.
Safety: No specific safety RLHF has been applied. Use with external safety guardrails in production.
Factuality: While logic is improved, it can still hallucinate complex facts.

🤝 Contact & Collaboration
Developed with ❤️ by AxionLab-Co.
We are an independent, community-driven lab focused on efficient AI.
Organization: AxionLab-official
Feedback: Open a Discussion on this repo!
Language Support: Primarily English. Portuguese support is available but may vary in reasoning depth.