swordfish7412/Bandila_1.0

Bandila 1.0 - Technical Reasoning Core AI (Reasoning Specialist)

Your AI Strategy Partner

Created by Jan Francis Israel

Part of the Swordfish Project 🇵🇭

Model Details

Model Description

Bandila 1.0 is a specialized AI assistant fine-tuned for reasoning, analysis, and strategic planning. Built on Mistral-7B with LoRA adapters, Bandila excels at:

• System architecture design and analysis
• DevOps strategy and automation planning
• Root cause analysis for complex problems
• Strategic decision-making and trade-offs
• Infrastructure optimization

Filipino AI Squad 🇵🇭

Bandila is part of a powerful trio of specialized AI models designed to work together seamlessly:

Together, they form an advanced AI ecosystem built for logic, creation, and collaboration.

• ** Bandila 1.0** (You are here) - Reasoning Specialist
• ** Amigo 1.0** - Coding Specialist
• ** Amihan 1.0** - Intelligent Ensemble

Bandila means "Flag" in Filipino - your strategic banner leading the way.

• Developed by: Jan Francis Israel (The Swordfish)
• Model type: Causal Language Model with LoRA fine-tuning (PEFT)
• Language(s): English (reasoning-focused)
• License: MIT
• Finetuned from: Mistral-7B-v0.1

Model Sources

• Repository: Part of the Swordfish Project
• Demo: Amihan 1.0 Space (Ensemble with Amigo)
• Sister Model: Amigo 1.0 (Coding Specialist)

Uses

Direct Use

Bandila 1.0 is designed for:

• Architecture Design: Planning scalable, maintainable systems
• DevOps Strategy: CI/CD pipeline optimization, infrastructure as code
• Problem Analysis: Root cause identification and strategic solutions
• Technical Planning: Making informed technical decisions
• Best Practices: Explaining industry standards and approaches

Recommended Use with Amihan Ensemble

For best results, use Bandila alongside Amigo 1.0 through the Amihan Ensemble, which intelligently routes queries to the appropriate specialist.

Out-of-Scope Use

• Not suitable for: Direct code generation (use Amigo for that)
• Limitations: Recommendations should be validated against your specific context
• Important: Always consider your unique requirements and constraints

How to Get Started

Installation

pip install transformers peft torch

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1",
    load_in_4bit=True,
    device_map="auto",
    torch_dtype=torch.float16
)

# Load Bandila LoRA adapter
model = PeftModel.from_pretrained(base_model, "swordfish7412/Bandila_1.0")
tokenizer = AutoTokenizer.from_pretrained("swordfish7412/Bandila_1.0")

# Ask for strategic guidance
prompt = "Instruction: How do I design a scalable microservices architecture?\nInput: \nOutput: "
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=250,
    temperature=0.7,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

Bandila 1.0 was trained on:

• Identity Dataset (390 samples): Custom identity and capability descriptions focused on reasoning
• HumanEval (164 samples): For general code understanding
• Total: 554 training samples across 4.29 epochs

Training Procedure

Training Configuration:

• Method: LoRA (Low-Rank Adaptation) fine-tuning with 4-bit quantization
• Base Model: mistralai/Mistral-7B-v0.1
• Training Steps: 300
• Training Time: ~17 minutes on RTX A5000 (24GB)
• Hardware: RunPod Cloud GPU (RTX A5000)
• Framework: HuggingFace Transformers + PEFT

Hyperparameters:

• Batch Size: 2
• Gradient Accumulation: 4
• Learning Rate: 2e-4
• Max Length: 512 tokens
• LoRA Rank: 32
• LoRA Alpha: 64
• Optimizer: paged_adamw_8bit
• FP16: True

Training Results

• Initial Loss: 6.37
• Final Loss: 5.30
• Training Speed: 3.49s/step
• Model Size: 105MB (LoRA adapter only)

Identity & Capabilities

Bandila 1.0 knows its identity and purpose:

Name: Bandila 1.0
Creator: Jan Francis Israel (The Swordfish)
Role: Technical Reasoning Core AI - Reasoning Specialist
Specialties: Architecture design, DevOps strategy, system analysis, strategic planning

Evaluation

Testing Results

Bandila successfully provides:

• ✅ Clear strategic recommendations
• ✅ System architecture insights
• ✅ DevOps best practices
• ✅ Root cause analysis
• ✅ Correct identity responses

Example Output

Query: "What is your name?"

Bandila's Response:

"I am Bandila 1.0, a Super Debugger AI - Reasoning Specialist created by Jan Francis Israel, also known as The Swordfish. I specialize in: system design and scalability planning, CI/CD pipeline design and optimization, strategic planning and architecture design, reasoning through complex technical problems, DevOps workflows and automation strategy."

Bias, Risks, and Limitations

Known Limitations

• General Advice: Recommendations are general and may not fit specific contexts
• No Code Generation: Not designed for writing code (use Amigo for that)
• Context Window: Limited to 512 tokens per query
• Domain Knowledge: Based on training data, may not reflect latest practices

Recommendations

• Validate recommendations against your specific requirements
• Consider organizational constraints and context
• Use as a starting point for strategic discussions
• Combine with domain expertise for best results

Environmental Impact

• Hardware: RunPod RTX A5000 (24GB)
• Training Time: ~17 minutes
• Power Consumption: Minimal (single GPU, short training)
• Carbon Footprint: Negligible due to short training duration

Technical Specifications

Model Architecture

• Base: Mistral-7B (7 billion parameters)
• Adapter: LoRA with rank 32
• Quantization: 4-bit (nf4) via bitsandbytes
• Adapter Size: 105MB
• Total Parameters (with base): ~7B

Compute Infrastructure

• Provider: RunPod Cloud
• GPU: NVIDIA RTX A5000 (24GB VRAM)
• Training Framework: PyTorch + HuggingFace Transformers
• Quantization: bitsandbytes 4-bit

Citation

@misc{bandila2024,
  author = {Jan Francis Israel},
  title = {Bandila 1.0: Super Debugger AI - Reasoning Specialist},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/swordfish7412/Bandila_1.0}},
  note = {Part of the Swordfish Project}
}

Model Card Authors

Jan Francis Israel (The Swordfish)

License

MIT License - Free to use with attribution

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Part of the Swordfish Project

Building elite AI debugging tools for developers worldwide