---
base_model: unsloth/Qwen3-4B-Base
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-4B-Base
- grpo
- lora
- sft
- transformers
- trl
- unsloth
license: other
datasets:
- open-r1/OpenR1-Math-220k
language:
- pt
- en
---

# Model Card for DogeAI-v2.0-4B-Reasoning-LoRA

This repository contains a LoRA (Low-Rank Adaptation) fine-tuned on top of Qwen3-4B-Base, focused on improving reasoning, chain-of-thought coherence, and analytical responses.
The LoRA was trained using curated thinking-style datasets on Kaggle with the goal of enhancing logical consistency rather than factual memorization.

# Model Details
# Model Description

This is a reasoning-oriented LoRA adapter designed to be applied to Qwen3-4B-Base.
The training emphasizes structured thinking, multi-step reasoning, and clearer internal deliberation in responses.

Developed by: AxionLab-Co

Model type: LoRA adapter (PEFT)

Language(s) (NLP): Primarily English

License: Apache 2.0 (inherits base model license)

Finetuned from model: Qwen3-4B-Base

Model Sources

Base Model: Qwen3-4B-Base

Training Platform: Kaggle

Frameworks: PyTorch, PEFT, Unsloth

# Uses
# Direct Use

This LoRA is intended to be merged or loaded on top of Qwen3-4B-Base to improve:

Logical reasoning

Step-by-step problem solving

Analytical and structured responses

“Thinking-style” outputs for research and experimentation

# Downstream Use

Merging into a full model for GGUF or standard HF release

Further fine-tuning on domain-specific reasoning tasks

Research on symbolic + neural reasoning hybrids

# Out-of-Scope Use

Safety-critical decision making

Medical, legal, or financial advice

Tasks requiring guaranteed factual correctness

Bias, Risks, and Limitations

The model may overproduce reasoning steps, even when not strictly required

Reasoning quality depends heavily on the base model (Qwen3-4B-Base)

No formal safety fine-tuning was applied beyond the base model

Possible amplification of biases present in the original training data

# Recommendations

# Users should:

Apply external safety layers if deploying in production

Evaluate outputs critically, especially for sensitive topics

Avoid assuming reasoning chains are always correct

How to Get Started with the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel


base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-4B-Base",
    device_map="auto",
    load_in_4bit=True
)


tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Base")


model = PeftModel.from_pretrained(
    base_model,
    "AxionLab-Co/DogeAI-v2.0-4B-Reasoning-LoRA"
)
# Training Details
# Training Data

The LoRA was trained on thinking-oriented datasets, focusing on:

Chain-of-thought style reasoning

Logical explanations

Multi-step analytical prompts

The datasets were curated and preprocessed manually for quality and consistency.

# Training Procedure
# Preprocessing

Tokenization using the base Qwen tokenizer

Filtering of low-quality or malformed reasoning examples

Training Hyperparameters

Training regime: fp16 mixed precision

Fine-tuning method: LoRA (PEFT)

Optimizer: AdamW

Framework: Unsloth

Speeds, Sizes, Times

Training performed on Kaggle GPU environment

LoRA size kept intentionally lightweight for fast loading and merging

# Evaluation
Testing Data, Factors & Metrics
Testing Data

Internal prompt-based reasoning tests

Synthetic reasoning benchmarks (qualitative)

# Factors

Multi-step logic consistency

Response clarity

Hallucination tendencies

Metrics

Qualitative human evaluation

Prompt-level comparison against base model

# Results

The LoRA shows clear improvements in reasoning depth and structure compared to the base model, especially on analytical prompts.

Environmental Impact

Hardware Type: NVIDIA GPU (Kaggle)

Hours used: Few hours (single-session fine-tuning)

Cloud Provider: Kaggle

Compute Region: Unknown

Carbon Emitted: Not formally measured

# Technical Specifications
# Model Architecture and Objective

Transformer-based decoder-only architecture

Objective: enhance reasoning behavior via parameter-efficient fine-tuning

Compute Infrastructure
Hardware

Kaggle-provided NVIDIA GPU

Software

PyTorch

Transformers

PEFT 0.18.1

Unsloth

Citation

If you use this LoRA in research or derivative works, please cite the base model and this repository.

# Model Card Authors

**AxionLab-Co**

# Model Card Contact

For questions, experiments, or collaboration:
**AxionLab-Co on Hugging Face**