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---
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license: apache-2.0
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base_model: allura-forge/Llama-3.3-8B-Instruct
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datasets:
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- TeichAI/claude-4.5-opus-high-reasoning-250x
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language:
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- en
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tags:
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- thinking
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- reasoning
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- instruct
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- economics
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- finance
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- analysis
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- llama3.3
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- unsloth
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- finetune
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- bfloat16
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- 128k context
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pipeline_tag: text-generation
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library_name: transformers
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model_type: llama
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---
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# AEGIS Conduct - Economic Analysis Model
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## Model Overview
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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.
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**Key Features:**
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- **Thinking Mode**: Automatic activation for complex reasoning
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- **Economic Focus**: Specialized for financial analysis and market insights
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- **128k Context**: Extended context window for comprehensive analysis
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- **Optimized**: Fine-tuned with Unsloth for efficient inference
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## Model Details
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- **Base Model**: allura-forge/Llama-3.3-8B-Instruct
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- **Fine-tuning Dataset**: TeichAI/claude-4.5-opus-high-reasoning-250x
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- **Context Length**: 128k tokens
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- **Training Method**: Unsloth (3 epochs)
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- **Format**: SafeTensors
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- **Precision**: bfloat16
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## Repository Structure
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All model files are now located in the root directory for optimal compatibility:
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```
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├── config.json # Model configuration
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├── generation_config.json # Generation parameters
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├── tokenizer.json # Tokenizer vocabulary
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├── tokenizer_config.json # Tokenizer configuration
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├── special_tokens_map.json # Special tokens mapping
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├── chat_template.jinja # Chat template
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├── model.safetensors.index.json # Model index
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├── model-00001-of-00004.safetensors # Model weights (part 1)
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├── model-00002-of-00004.safetensors # Model weights (part 2)
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├── model-00003-of-00004.safetensors # Model weights (part 3)
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├── model-00004-of-00004.safetensors # Model weights (part 4)
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├── reco.py # Model utilities
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├── matrix-neo-reloaded-fight.gif # Visual asset
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└── README.md # This file
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```
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## Usage
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### Quick Start with Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Load model and tokenizer directly (no subfolder needed)
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tokenizer = AutoTokenizer.from_pretrained("Gaston895/aegisconduct")
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model = AutoModelForCausalLM.from_pretrained("Gaston895/aegisconduct")
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# Generate response
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inputs = tokenizer("Analyze the economic impact of inflation on consumer spending:", return_tensors="pt")
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outputs = model.generate(**inputs, max_length=512, temperature=0.7)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### Thinking Mode Activation
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The model automatically activates thinking mode for complex reasoning:
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```python
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# These prompts will trigger thinking mode
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prompts = [
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"Think deeply: Analyze the economic implications of rising interest rates",
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"Explain the financial impact of supply chain disruptions",
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"Think through: What are the long-term effects of quantitative easing?"
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]
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```
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### Recommended Settings
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- **Temperature**: 0.7
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- **Repetition Penalty**: 1.05
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- **Top-p**: 0.95
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- **Min-p**: 0.05
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- **Top-k**: 40
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- **Context Window**: 4k minimum, 8k+ recommended
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## Capabilities
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This model excels at:
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- **Economic Analysis**: Market trends, policy impacts, forecasting
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- **Financial Planning**: Investment strategies, risk assessment
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- **Data Interpretation**: Economic indicators, statistical analysis
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- **Policy Analysis**: Regulatory impacts, fiscal policy effects
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- **Global Economics**: International trade, currency analysis
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- **Research**: Academic-level economic reasoning and explanation
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## Example Outputs
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The model provides detailed, step-by-step reasoning for complex economic questions, often showing its "thinking" process before delivering final answers.
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## Technical Notes
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- All model files are in the root directory for direct loading
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- Supports both instruct and thinking modes
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- No system prompt required (thinking tags self-generate)
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- Compatible with quantization (Q4KS, IQ3_M recommended minimum)
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- Optimized for inference with various backends (transformers, llama.cpp, etc.)
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## License
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Apache 2.0 (inherited from base model)
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## Credits
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- **Base Model**: [allura-forge/Llama-3.3-8B-Instruct](https://huggingface.co/allura-forge/Llama-3.3-8B-Instruct)
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- **Dataset**: [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x)
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- **Training Framework**: [Unsloth](https://github.com/unslothai/unsloth)
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