AEGIS Conduct - Economic Analysis Model

Name: aegisconduct
Rating: 2.5 (30 reviews)

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
Dataset: TeichAI/claude-4.5-opus-high-reasoning-250x
Training Framework: Unsloth