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")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

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 Studio new

How to use Gaston895/aegisconduct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Gaston895/aegisconduct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Gaston895/aegisconduct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Gaston895/aegisconduct to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Gaston895/aegisconduct",
    max_seq_length=2048,
)

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

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

Downloads last month: 9

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Gaston895/aegisconduct

Base model

allura-forge/Llama-3.3-8B-Instruct

Finetuned

(12)

this model

Gaston895
/

aegisconduct