README.md

---

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
---


# AEGIS Conduct - Economic Analysis Model

<img src="econ/matrix-neo-reloaded-fight.gif" style="float:right; width:300px; height:300px; padding:10px;">

## 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

The model files are organized in the econ/ directory:

```

econ/

├── 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

```

## Usage

### Quick Start with Transformers

```python

from transformers import AutoTokenizer, AutoModelForCausalLM



# Load model and tokenizer from econ subdirectory

tokenizer = AutoTokenizer.from_pretrained("Gaston895/aegisconduct", subfolder="econ")

model = AutoModelForCausalLM.from_pretrained("Gaston895/aegisconduct", subfolder="econ")



# 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. Use prompts like:

```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

- Optimized for inference with various backends (transformers, llama.cpp, etc.)
- Supports both instruct and thinking modes
- No system prompt required (thinking tags self-generate)
- Compatible with quantization (Q4KS, IQ3_M recommended minimum)



## 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)