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library_name: transformers
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---
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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## Training Details
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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##
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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library_name: transformers
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tags:
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- legal
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- philippine-law
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- tagalog
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- filipino
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- barangay
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- qwen2
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- lora
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- fine-tuned
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language:
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- en
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- tl
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license: apache-2.0
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base_model: Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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---
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# βοΈ Abogado β Open Source Philippine Law AI
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**Abogado** (Filipino for "Lawyer") is an open-source AI assistant fine-tuned to help Filipinos understand Philippine law. It is specifically designed for **barangay officials, local councilors, mediators**, and ordinary citizens who need accessible legal information.
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> **β οΈ Disclaimer:** Abogado is NOT a lawyer. It provides legal information for educational purposes only. Always consult a licensed Philippine attorney for actual legal advice. For free legal assistance, contact the Public Attorney's Office (PAO) or IBP legal aid.
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## Why Abogado?
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- Most AI models are trained on US/UK law and perform poorly on Philippine law
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- Millions of barangay officials handle legal disputes daily without formal legal training
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- Access to legal information in the Philippines is expensive and inaccessible for many
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- Abogado aims to democratize legal knowledge for every Filipino
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## Model Details
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- **Base Model:** Qwen/Qwen2.5-3B-Instruct
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- **Method:** QLoRA fine-tuning (4-bit quantization, LoRA rank 16)
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- **Training Data:** 106 Q&A pairs from the 1987 Philippine Constitution
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- **Languages:** English and Filipino/Tagalog
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- **License:** Apache 2.0 (fully open source)
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- **Hardware:** Kaggle T4 GPU
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## What It Knows
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Currently trained on:
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1987 Philippine Constitution (all 18 Articles)
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Bill of Rights practical applications
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Katarungang Pambarangay (Barangay Justice System)
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VAWC (RA 9262) basics and Barangay Protection Orders
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Local government structure and powers
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Rights of arrested persons
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Safety behaviors (refuses private data, recommends real lawyers)
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### Planned future training data:
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- π Local Government Code (RA 7160)
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- π Revised Penal Code
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- π Family Code
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- π Labor Code
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- π RA 9262 (VAWC) full text
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- π RA 7610 (Child Protection)
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- π Supreme Court jurisprudence
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- π Rules on Summary Procedure
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## How to Use
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### With Transformers (Python)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/abogado")
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tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/abogado")
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messages = [
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{"role": "system", "content": "You are Abogado, an open-source Philippine law assistant."},
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{"role": "user", "content": "Can a barangay captain issue a warrant of arrest?"},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### With Ollama (Local/Docker)
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```bash
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# Download the GGUF version and create a Modelfile, then:
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ollama create abogado -f Modelfile
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ollama run abogado "What is the Katarungang Pambarangay?"
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```
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## Target Users
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- ποΈ **Barangay Officials** β Captains, Kagawads, Lupon members handling disputes
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- ποΈ **Local Councilors** β Municipal/city council members drafting ordinances
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- π€ **Mediators** β Lupong Tagapamayapa members conducting conciliation
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- π **Law Students** β For study and bar exam review
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- π΅π **Filipino Citizens** β Anyone who wants to understand their rights
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## Safety Features
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Abogado is trained to:
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- β **Refuse** to review actual case documents or provide specific legal advice
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- β οΈ **Warn** users when they share personal or confidential information
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**Redirect** users to licensed attorneys, PAO, or IBP legal aid
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- π **Always include** a disclaimer that information is for educational purposes only
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## Limitations
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- Currently trained only on the Philippine Constitution (106 Q&A pairs)
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- May produce inaccurate or incomplete legal information
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- Cannot replace professional legal advice
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- Knowledge is limited to training data β does not have access to the latest laws or jurisprudence
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- Performance will improve significantly as more training data is added
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## Contributing
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Abogado is open source! You can help by:
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1. **Adding more training data** β Q&A pairs from other Philippine laws
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2. **Testing and reporting issues** β File issues on the repo
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3. **Translating** β Adding more Filipino/Tagalog Q&A pairs
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4. **Sharing** β Tell barangay officials, law students, and communities about Abogado
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## Training Details
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- **Epochs:** 5
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- **Batch size:** 2 (with gradient accumulation of 4, effective batch size 8)
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- **Learning rate:** 2e-4
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- **Optimizer:** AdamW 8-bit
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- **Precision:** FP16
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- **LoRA rank:** 16
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- **LoRA alpha:** 16
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- **Target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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## License
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Apache 2.0 β Use it freely, modify it, share it. Help make law accessible for every Filipino.
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## Acknowledgments
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- Base model by [Qwen Team](https://huggingface.co/Qwen)
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- Philippine Constitution text from the [Official Gazette](https://www.officialgazette.gov.ph)
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- Built with [Hugging Face Transformers](https://huggingface.co/docs/transformers), [PEFT](https://huggingface.co/docs/peft), and [TRL](https://huggingface.co/docs/trl)
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| 144 |
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| 145 |
+
*"Kaalaman sa batas, para sa lahat." (Legal knowledge, for everyone.)*
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