metadata
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
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:
# 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