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 📌 Model Details Model Description
DogeAI-v2.0-4B-Reasoning is a language model focused on reasoning, structured thinking, and analytical responses, created from merging a reasoning LoRA onto the Qwen3-4B-Base model.
The main objective of this model is to improve logical coherence, the ability to solve problems in multiple steps, and explanatory clarity, without drastically altering the overall behavior of the base model.
This model represents the merged and final version, and can be used without dependence on external LoRA.
Developed by: AxionLab-Co
Funded by: Independent / Community-driven
Shared by: AxionLab-Co
Model type: Decoder-only Transformer (Causal Language Model)
Language(s) (NLP): Primarily English
License: Apache 2.0 (inherits from base model)
Finetuned from model: Qwen3-4B-Base
🔗 Model Sources Repository: Hugging Face – AxionLab-Co/DogeAI-v2.0-4B-Reasoning
Base Model: Qwen/Qwen3-4B-Base
Training Platform: Kaggle
Frameworks: PyTorch, Transformers, PEFT
🎯 Uses Direct Use This model can be used directly for:
Logical and analytical reasoning
Multi-step problem solving
Detailed explanations (“Thinking-Style Responses”)
AI Research, Experimentation, and Learning
Downstream Use
Conversational agents focused on reasoning
Additional fine-tuning in specific domains
Conversion to GGUF and use in engines like llama.cpp
Academic or experimental research
Out-of-Scope Use
This model is not recommended for:
Medical, legal, or financial decisions
Critical safety applications
Use where absolute factuality is mandatory
⚠️ Bias, Risks, and Limitations May generate excessive reasoning chains, even when unnecessary
Inherited potential biases from the base model and training data
Has not undergone specific alignment or safety fine-tuning
Generated reasoning is not guaranteed to be correct
Recommendations
Users should:
Critically evaluate responses
Use additional layers of security in production
Avoid blindly trusting chains of reasoning
🚀 How to Get Started with the Model '' from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained( "AxionLab-Co/DogeAI-v2.0-4B-Reasoning", device_map="auto", torch_dtype="auto" )
tokenizer = AutoTokenizer.from_pretrained( "AxionLab-Co/DogeAI-v2.0-4B-Reasoning" )
inputs = tokenizer("Solve this step by step:", return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ''
🏋️Training Details Training Data
The model was fitted using datasets focused on reasoning and chain-of-thought, containing:
Step-by-step problem solving
Structured explanatory responses
Synthetic and curated analytical prompts
The data were manually pre-processed to improve quality and consistency.
Training Procedure Preprocessing
Tokenization with Qwen's original tokenizer
Filtering of inconsistent or low-quality examples
Training Hyperparameters
Training regime: fp16 mixed precision
Fine-tuning method: LoRA (PEFT)
Optimizer: AdamW
Framework: Transformers + PEFT
Speeds, Sizes, Times
Training performed on Kaggle GPU
LoRA intentionally kept lightweight
Final merge performed via PEFT (merge_and_unload)
📊 Evaluation Testing Data, Factors & Metrics Testing Data
Manual reasoning prompts
Direct comparison with the base model
Factors
Clarity of reasoning
Logical coherence
Tendency to hallucination
Metrics
Qualitative human evaluation
Subjective comparison of responses
Results
The model demonstrates better logical organization and more concise explanations Consistent in direct comparison with Qwen3-4B-Base.
Summary
DogeAI-v2.0-4B-Reasoning prioritizes quality of thought, not just textual fluency.
🌱 Environmental Impact Hardware Type: NVIDIA GPU (Kaggle)
Hours used: Few hours (single-session fine-tuning + merge)
Cloud Provider: Kaggle
Compute Region: Unknown
Carbon Emitted: Not measured
⚙️ Technical Specifications Model Architecture and Objective Decoder-only Transformer
Objective: Improve reasoning via efficient fine-tuning
Compute Infrastructure Hardware
NVIDIA GPU (Kaggle environment)
Software
PyTorch
Transformers
PEFT 0.18.1
📚 Citation If you use this model in research or derivative projects, please cite the base model and this repository.
👥 Model Card Authors AxionLab-Co
📬 Model Card Contact For questions, feedback, or collaboration: AxionLab-Co – Hugging Face
--FOR PORTUGUESE READERS --
🧠 DogeAI-v2.0-4B-Reasoning
📌 Model Details
Model Description
DogeAI-v2.0-4B-Reasoning é um modelo de linguagem focado em raciocínio, pensamento estruturado e respostas analíticas, criado a partir do merge de uma LoRA de reasoning sobre o modelo base Qwen3-4B-Base.
O objetivo principal deste modelo é melhorar a coerência lógica, a capacidade de resolver problemas em múltiplos passos e a clareza explicativa, sem alterar drasticamente o comportamento geral do modelo base.
Este modelo representa a versão merged e final, podendo ser utilizado sem dependência de LoRA externa.
Developed by: AxionLab-Co
Funded by: Independent / Community-driven
Shared by: AxionLab-Co
Model type: Decoder-only Transformer (Causal Language Model)
Language(s) (NLP): Primarily English
License: Apache 2.0 (inherits from base model)
Finetuned from model: Qwen3-4B-Base
🔗 Model Sources
Repository: Hugging Face – AxionLab-Co/DogeAI-v2.0-4B-Reasoning
Base Model: Qwen/Qwen3-4B-Base
Training Platform: Kaggle
Frameworks: PyTorch, Transformers, PEFT
🎯 Uses
Direct Use
Este modelo pode ser utilizado diretamente para:
Raciocínio lógico e analítico
Resolução de problemas em múltiplos passos
Explicações detalhadas (“thinking-style responses”)
Pesquisa, experimentação e aprendizado em IA
Downstream Use
Conversational agents focados em reasoning
Fine-tuning adicional em domínios específicos
Conversão para GGUF e uso em engines como llama.cpp
Pesquisa acadêmica ou experimental
Out-of-Scope Use
Este modelo não é recomendado para:
Decisões médicas, legais ou financeiras
Aplicações críticas de segurança
Uso onde factualidade absoluta é obrigatória
⚠️ Bias, Risks, and Limitations
Pode gerar cadeias de raciocínio excessivas, mesmo quando não necessárias
Herdou possíveis vieses do modelo base e dos dados de treino
Não passou por fine-tuning específico de alinhamento ou safety
Raciocínios gerados não são garantidamente corretos
Recommendations
Usuários devem:
Avaliar criticamente as respostas
Utilizar camadas adicionais de segurança em produção
Evitar confiar cegamente em cadeias de raciocínio
🚀 How to Get Started with the Model
'' from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained( "AxionLab-Co/DogeAI-v2.0-4B-Reasoning", device_map="auto", torch_dtype="auto" )
tokenizer = AutoTokenizer.from_pretrained( "AxionLab-Co/DogeAI-v2.0-4B-Reasoning" )
inputs = tokenizer("Solve this step by step:", return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ''
🏋️ Training Details
Training Data
O modelo foi ajustado utilizando datasets focados em reasoning e chain-of-thought, contendo:
Resolução passo a passo de problemas
Respostas explicativas estruturadas
Prompts analíticos sintéticos e curados
Os dados foram pré-processados manualmente para melhorar qualidade e consistência.
Training Procedure Preprocessing
Tokenização com tokenizer original do Qwen
Filtragem de exemplos inconsistentes ou de baixa qualidade
Training Hyperparameters
Training regime: fp16 mixed precision
Fine-tuning method: LoRA (PEFT)
Optimizer: AdamW
Framework: Transformers + PEFT
Speeds, Sizes, Times
Treinamento realizado em GPU do Kaggle
LoRA mantida propositalmente leve
Merge final realizado via PEFT (merge_and_unload)
📊 Evaluation
Testing Data, Factors & Metrics Testing Data
Prompts manuais de reasoning
Comparação direta com o modelo base
Factors
Clareza do raciocínio
Coerência lógica
Tendência a alucinação
Metrics
Avaliação qualitativa humana
Comparação subjetiva de respostas
Results
O modelo demonstra melhor organização lógica e explicações mais consistentes em comparação direta com o Qwen3-4B-Base.
Summary
DogeAI-v2.0-4B-Reasoning prioriza qualidade de pensamento, não apenas fluência textual.
🌱 Environmental Impact
Hardware Type: NVIDIA GPU (Kaggle)
Hours used: Few hours (single-session fine-tuning + merge)
Cloud Provider: Kaggle
Compute Region: Unknown
Carbon Emitted: Not measured
⚙️ Technical Specifications
Model Architecture and Objective
Decoder-only Transformer
Objetivo: melhorar raciocínio via fine-tuning eficiente
Compute Infrastructure Hardware
NVIDIA GPU (Kaggle environment)
Software
PyTorch
Transformers
PEFT 0.18.1
📚 Citation
Se você utilizar este modelo em pesquisas ou projetos derivados, cite o modelo base e este repositório.
👥 Model Card Authors
AxionLab-Co
📬 Model Card Contact
Para dúvidas, feedback ou colaboração: AxionLab-Co – Hugging Face