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, 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
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.