🧠 MODEL CARD — DogeAI-v1.0-instruct Model Details
Model Description DogeAI-v1.0-instruct is an early-stage instruction-following language model fine-tuned for conversational use and experimentation. This version is intended as a proof of concept (v1) and focuses on language generation rather than reliable logical reasoning.
Developed by: Arthur(loboGOAT) Funded by: Independent / Community-driven Shared by: Arthur(loboGOAT) Model type: Small Instruction-Tuned Language Model Language(s): Portuguese (primary), multilingual tendencies inherited from base model License: Apache 2.0 (or the same license as the base model, if different) Finetuned from model: Gemma-3-270M-it
Model Sources
Repository: loboGOAT/DogeAI-v1.0-instruct Paper: Not available Demo: Not available
Uses Direct Use
Conversational experiments
Text generation and rewriting
Prompt testing and evaluation
Educational use to study limitations of small LLMs
Downstream Use (Optional)
Further fine-tuning
Research on alignment, reasoning, and instruction-following
Benchmarking small models
Out-of-Scope Use
Tasks requiring reliable logical reasoning
Mathematical proof or formal logic
Decision-making systems
Safety-critical or automated validation tasks
Recommendations
This model should not be relied upon for reasoning-intensive tasks. Users are encouraged to treat DogeAI-v1.0-instruct as an experimental model and expect occasional logical inconsistencies, multilingual drift, or overgeneration.
Future versions aim to address these limitations through:
cleaner datasets
improved stopping criteria
alternative base models
How to Get Started with the Model from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("loboGOAT/DogeAI-v1.0-instruct") model = AutoModelForCausalLM.from_pretrained("loboGOAT/DogeAI-v1.0-instruct")
inputs = tokenizer("Olá! Vamos conversar?", return_tensors="pt") outputs = model.generate( **inputs, max_new_tokens=128, temperature=0.65, top_p=0.95 )
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details Training Data
The model was fine-tuned on a custom instruction-style dataset, primarily in Portuguese, designed to encourage conversational responses. The dataset does not focus on formal logic or structured reasoning.
Training Procedure
Preprocessing
Instruction–response formatting
Text normalization
No explicit chain-of-thought supervision
Training Hyperparameters
Training regime: Supervised fine-tuning (SFT) PEFT: Yes (LoRA-based fine-tuning)
Evaluation Testing Data
Manual testing and prompt-based evaluation.
Factors
Logical consistency
Instruction-following
Language fluency
Metrics
No automated benchmarks were used for this version.
Results
Strong conversational fluency for model size
Inconsistent logical reasoning
Occasional overgeneration beyond intended response
Summary Model Examination
DogeAI-v1.0-instruct demonstrates the strengths and limitations of small instruction-tuned language models. While capable of natural conversation, it lacks robust reasoning abilities, which will be a focus of future iterations.
Environmental Impact
Hardware Type: Consumer GPU / Local Machine Hours used: Low Cloud Provider: None Compute Region: Local Carbon Emitted: Negligible
Technical Specifications Model Architecture and Objective
Decoder-only Transformer
Next-token prediction
Instruction-following objective
Compute Infrastructure
Local training environment.
Hardware
Consumer-grade GPU / CPU
Software
Transformers
PEFT 0.18.0
PyTorch
Citation
BibTeX:
@misc{dogeai_v1_2025, title={DogeAI-v1.0-instruct}, author={Arthur}, year={2025}, note={Early experimental instruction-tuned language model} }
APA: Arthur (2025). DogeAI-v1.0-instruct: An experimental instruction-tuned language model.
Model Card Authors
Arthur
Model Card Contact
(your Hugging Face profile or GitHub)
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