Instructions to use Gaston895/aegisconduct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gaston895/aegisconduct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Gaston895/aegisconduct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gaston895/aegisconduct")
model = AutoModelForCausalLM.from_pretrained("Gaston895/aegisconduct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gaston895/aegisconduct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gaston895/aegisconduct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gaston895/aegisconduct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Gaston895/aegisconduct

SGLang

How to use Gaston895/aegisconduct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Gaston895/aegisconduct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gaston895/aegisconduct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Gaston895/aegisconduct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gaston895/aegisconduct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Gaston895/aegisconduct with Docker Model Runner:
```
docker model run hf.co/Gaston895/aegisconduct
```

aegisconduct / README.md

Gaston895

Update README with correct repository structure and enhanced documentation

d097231 verified 8 months ago

4.81 kB

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

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

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:

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