---
license: apache-2.0
base_model: allura-forge/Llama-3.3-8B-Instruct
datasets:
- TeichAI/claude-4.5-opus-high-reasoning-250x
language:
- en
tags:
- thinking
- reasoning
- instruct
- economics
- finance
- analysis
- llama3.3
- unsloth
- finetune
- bfloat16
- 128k context
pipeline_tag: text-generation
library_name: transformers
model_type: llama
---

# AEGIS Conduct - Economic Analysis Model

## Model Overview

This repository contains the Llama 3.3 8B Instruct model with thinking capabilities, fine-tuned for economic and financial analysis using Claude 4.5-Opus High Reasoning dataset.

**Key Features:**
- **Thinking Mode**: Automatic activation for complex reasoning
- **Economic Focus**: Specialized for financial analysis and market insights  
- **128k Context**: Extended context window for comprehensive analysis
- **Optimized**: Fine-tuned with Unsloth for efficient inference

## Model Details

- **Base Model**: allura-forge/Llama-3.3-8B-Instruct  
- **Fine-tuning Dataset**: TeichAI/claude-4.5-opus-high-reasoning-250x
- **Context Length**: 128k tokens
- **Training Method**: Unsloth (3 epochs)
- **Format**: SafeTensors
- **Precision**: bfloat16

## Repository Structure

All model files are now located in the root directory for optimal compatibility:

```
├── config.json                    # Model configuration
├── generation_config.json         # Generation parameters
├── tokenizer.json                 # Tokenizer vocabulary
├── tokenizer_config.json          # Tokenizer configuration
├── special_tokens_map.json        # Special tokens mapping
├── chat_template.jinja            # Chat template
├── model.safetensors.index.json   # Model index
├── model-00001-of-00004.safetensors  # Model weights (part 1)
├── model-00002-of-00004.safetensors  # Model weights (part 2)
├── model-00003-of-00004.safetensors  # Model weights (part 3)
├── model-00004-of-00004.safetensors  # Model weights (part 4)
├── reco.py                        # Model utilities
├── matrix-neo-reloaded-fight.gif  # Visual asset
└── README.md                      # This file
```

## Usage

### Quick Start with Transformers

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer directly (no subfolder needed)
tokenizer = AutoTokenizer.from_pretrained("Gaston895/aegisconduct")
model = AutoModelForCausalLM.from_pretrained("Gaston895/aegisconduct")

# Generate response
inputs = tokenizer("Analyze the economic impact of inflation on consumer spending:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Thinking Mode Activation

The model automatically activates thinking mode for complex reasoning:

```python
# These prompts will trigger thinking mode
prompts = [
    "Think deeply: Analyze the economic implications of rising interest rates",
    "Explain the financial impact of supply chain disruptions",
    "Think through: What are the long-term effects of quantitative easing?"
]
```

### Recommended Settings

- **Temperature**: 0.7
- **Repetition Penalty**: 1.05  
- **Top-p**: 0.95
- **Min-p**: 0.05
- **Top-k**: 40
- **Context Window**: 4k minimum, 8k+ recommended

## Capabilities

This model excels at:

- **Economic Analysis**: Market trends, policy impacts, forecasting
- **Financial Planning**: Investment strategies, risk assessment
- **Data Interpretation**: Economic indicators, statistical analysis  
- **Policy Analysis**: Regulatory impacts, fiscal policy effects
- **Global Economics**: International trade, currency analysis
- **Research**: Academic-level economic reasoning and explanation

## Example Outputs

The model provides detailed, step-by-step reasoning for complex economic questions, often showing its "thinking" process before delivering final answers.

## Technical Notes

- All model files are in the root directory for direct loading
- Supports both instruct and thinking modes
- No system prompt required (thinking tags self-generate)
- Compatible with quantization (Q4KS, IQ3_M recommended minimum)
- Optimized for inference with various backends (transformers, llama.cpp, etc.)

## License

Apache 2.0 (inherited from base model)

## Credits

- **Base Model**: [allura-forge/Llama-3.3-8B-Instruct](https://huggingface.co/allura-forge/Llama-3.3-8B-Instruct)
- **Dataset**: [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x)
- **Training Framework**: [Unsloth](https://github.com/unslothai/unsloth)