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README.md

@@ -1,105 +1,206 @@
 ---
-license: apache-2.0
-language:
-- pt
-- en
-- hi
-- fr
-base_model:
-- google/gemma-3-270m-it
-pipeline_tag: text-generation
+base_model: google/gemma-3-270m-it
 library_name: peft
+pipeline_tag: text-generation
 tags:
-- 300mparams
-- finetuned
-- text-generation-inference
+- base_model:adapter:google/gemma-3-270m-it
+- lora
+- transformers
 ---
 
-# DogeAI-v1.0 (Gemma-3-270M-it LoRA Fine-Tuned)
+# Model Card for Model ID
 
-Welcome to **DogeAI-v1.0**, a **LoRA fine-tuned version of Gemma-3-270M-it**, created by **Arthur**! 🚀  
+<!-- Provide a quick summary of what the model is/does. -->
 
-This model is trained to answer questions, perform text tasks, and chat naturally, mainly in **Portuguese**, but it is also **multilingual** and can handle inputs in other languages.  
 
-⚠️ **Warning:** While it performs many tasks very well, it sometimes **hallucinates facts**. Always double-check critical information.  
 
----
+## Model Details
 
-## 🧠 About the Model
+### Model Description
 
-- **Base:** Gemma-3-270M-it  
-- **Fine-tuning:** LoRA on a custom Portuguese dataset  
-- **Capabilities:**
-  - Natural conversation in Portuguese  
-  - Answering general questions  
-  - Creating summaries, explanatory texts, and small code snippets  
-  - Handling multiple languages (English, Spanish, etc.)  
-- **Limitations:**
-  - May invent facts or answers (hallucinations)  
-  - Not 100% reliable for critical data  
-  - Best used with clear and concise prompts  
+<!-- Provide a longer summary of what this model is. -->
 
----
 
-## 📌 Recommended Prompt Format
 
-For best results, use the following format:
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
 
-<bos> <start_of_turn>user [Your question or message here] <start_of_turn>model ```
-<bos> → start of prompt
+[More Information Needed]
 
-<start_of_turn>user> → indicates user input
+## Citation [optional]
 
-<start_of_turn>model> → indicates where the model should generate a response
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-Example:
+**BibTeX:**
 
-php-template
-Copiar código
-<bos>
-<start_of_turn>user
-What is the capital of Brazil?
-<start_of_turn>model
-The model will respond with something like:
-Brasília.
+[More Information Needed]
 
-⚡ How to Use
-python
------
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-import torch
+**APA:**
 
-BASE_MODEL = "loboGOAT/DogeAI-v1.0"
+[More Information Needed]
 
-tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
-model = AutoModelForCausalLM.from_pretrained(BASE_MODEL)
-model = PeftModel.from_pretrained(model, BASE_MODEL)
-model.eval()
+## Glossary [optional]
 
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-model.to(DEVICE)
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-# Example generation
-prompt = "<bos>\n<start_of_turn>user\nWhat is the capital of Brazil?\n<start_of_turn>model\n"
-inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
-output = model.generate(**inputs, max_new_tokens=150, do_sample=True, temperature=0.7)
-response = tokenizer.decode(output[0], skip_special_tokens=True)
-print(response.split("<start_of_turn>model")[-1].strip())
-💡 Recommendations
-Use short and clear prompts for more reliable answers.
+[More Information Needed]
 
-Avoid asking highly specific factual questions that require historical or scientific accuracy; the model may hallucinate.
+## More Information [optional]
 
-Great for web projects using Flask, FastAPI, or Gradio.
+[More Information Needed]
 
-Multilingual: Portuguese is most accurate; other languages work but may mix or slightly confuse outputs.
+## Model Card Authors [optional]
 
-📝 About the Creator
-Creator: Arthur Samuel
+[More Information Needed]
 
-Young game and AI developer from Brazil
+## Model Card Contact
 
-Experienced with fine-tuning models, LoRA, and chatbot systems
+[More Information Needed]
+### Framework versions
 
-Passionate about turning AIs into fun and useful tools for everyone
+- PEFT 0.18.0

adapter_config.json

@@ -0,0 +1,43 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "google/gemma-3-270m-it",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.0",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "v_proj",
+    "o_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
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+size 5917192