Update README.md

README.md

@@ -8,199 +8,188 @@ tags:
 - transformers
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-# Model Card for Model ID
+🧠 MODEL CARD — DogeAI-v1.0-instruct
+Model Details
 
-<!-- Provide a quick summary of what the model is/does. -->
+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
 
-## Model Details
+Repository: loboGOAT/DogeAI-v1.0-instruct
+Paper: Not available
+Demo: Not available
 
-### Model Description
+Uses
+Direct Use
 
-<!-- Provide a longer summary of what this model is. -->
+Conversational experiments
 
+Text generation and rewriting
 
+Prompt testing and evaluation
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+Educational use to study limitations of small LLMs
 
-### Model Sources [optional]
+Downstream Use (Optional)
 
-<!-- Provide the basic links for the model. -->
+Further fine-tuning
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Research on alignment, reasoning, and instruction-following
 
-## Uses
+Benchmarking small models
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Out-of-Scope Use
 
-### Direct Use
+Tasks requiring reliable logical reasoning
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Mathematical proof or formal logic
 
-[More Information Needed]
+Decision-making systems
 
-### Downstream Use [optional]
+Safety-critical or automated validation tasks
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Recommendations
 
-[More Information Needed]
+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.
 
-### Out-of-Scope Use
+Future versions aim to address these limitations through:
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -
+cleaner datasets
 
+improved stopping criteria
 
-## Bias, Risks, and Limitations
+alternative base models
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+How to Get Started with the Model
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("loboGOAT/DogeAI-v1.0-instruct")
+model = AutoModelForCausalLM.from_pretrained("loboGOAT/DogeAI-v1.0-instruct")
 
-### Recommendations
+inputs = tokenizer("Olá! Vamos conversar?", return_tensors="pt")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=128,
+    temperature=0.65,
+    top_p=0.95
+)
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Training Details
+Training Data
 
-## How to Get Started with the Model
+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.
 
-Use the code below to get started with the model.
+Training Procedure
 
-[More Information Needed]
+Preprocessing
 
-## Training Details
+Instruction–response formatting
 
-### Training Data
+Text normalization
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+No explicit chain-of-thought supervision
 
-[More Information Needed]
+Training Hyperparameters
 
-### Training Procedure
+Training regime: Supervised fine-tuning (SFT)
+PEFT: Yes (LoRA-based fine-tuning)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Evaluation
+Testing Data
 
-#### Preprocessing [optional]
+Manual testing and prompt-based evaluation.
 
-[More Information Needed]
+Factors
 
+Logical consistency
 
-#### Training Hyperparameters
+Instruction-following
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Language fluency
 
-#### Speeds, Sizes, Times [optional]
+Metrics
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+No automated benchmarks were used for this version.
 
-[More Information Needed]
+Results
 
-## Evaluation
+Strong conversational fluency for model size
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Inconsistent logical reasoning
 
-### Testing Data, Factors & Metrics
+Occasional overgeneration beyond intended response
 
-#### Testing Data
+Summary
+Model Examination
 
-<!-- This should link to a Dataset Card if possible. -->
+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.
 
-[More Information Needed]
+Environmental Impact
 
-#### Factors
+Hardware Type: Consumer GPU / Local Machine
+Hours used: Low
+Cloud Provider: None
+Compute Region: Local
+Carbon Emitted: Negligible
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Technical Specifications
+Model Architecture and Objective
 
-[More Information Needed]
+Decoder-only Transformer
 
-#### Metrics
+Next-token prediction
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Instruction-following objective
 
-[More Information Needed]
+Compute Infrastructure
 
-### Results
+Local training environment.
 
-[More Information Needed]
+Hardware
 
-#### Summary
+Consumer-grade GPU / CPU
 
+Software
 
+Transformers
 
-## Model Examination [optional]
+PEFT 0.18.0
 
-<!-- Relevant interpretability work for the model goes here -->
+PyTorch
 
-[More Information Needed]
+Citation
 
-## Environmental Impact
+BibTeX:
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+@misc{dogeai_v1_2025,
+  title={DogeAI-v1.0-instruct},
+  author={Arthur},
+  year={2025},
+  note={Early experimental instruction-tuned language model}
+}
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+APA:
+Arthur (2025). DogeAI-v1.0-instruct: An experimental instruction-tuned language model.
 
-## Technical Specifications [optional]
+Model Card Authors
 
-### Model Architecture and Objective
+Arthur
 
-[More Information Needed]
+Model Card Contact
 
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.18.0
+(your Hugging Face profile or GitHub)