Ace_v4.2_Mini

ACE v4.2 Mini: Advanced Cognitive Engine

Model Overview

Model Name: CrashOverrideX/Ace_v4.2_Mini
Base Model: meta-llama/Llama-3.2-3B-Chat-hf
License: Apache License 2.0

Description

ACE v4.2 Mini is an Advanced Cognitive Engine built on the Llama-3.2-3B-Chat foundation model. This model implements a mixture-of-experts architecture designed for cognitive science applications and intelligent assistant tasks. It combines the efficiency of parameter-efficient fine-tuning (PEFT) with LoRA adapters to create a specialized conversational AI system.

Architecture

• Base Architecture: Llama-3.2-3B-Chat (Causal Language Model)
• Training Method: Parameter-Efficient Fine-Tuning (PEFT) with LoRA
• Specialization: Advanced Cognitive Engine with mixture-of-experts design
• Focus: Friendly assistant capabilities with cognitive architecture enhancements

Quick Start Guide

Intended Use Cases

• Conversational AI and chatbot applications
• Cognitive science research and demonstrations
• Educational tools for AI and cognitive architecture concepts
• General-purpose text generation with enhanced reasoning capabilities

Model Capabilities

• Natural language understanding and generation
• Multi-turn conversations with context awareness
• Cognitive reasoning and problem-solving assistance
• Friendly and helpful assistant interactions

Performance Stats

Limitations

• Based on 3B parameter model, may have limitations compared to larger models
• Performance dependent on the quality and scope of fine-tuning data
• May require specific prompting techniques for optimal cognitive architecture features
• Subject to the same general limitations as other language models (hallucination, bias, etc.)

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CrashOverrideX/Ace_v4.2_Mini")
model = AutoModelForCausalLM.from_pretrained("CrashOverrideX/Ace_v4.2_Mini")

# Generate text
input_text = "Hello, how can you help me today?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Platform Support

Training Details

• Base Model: meta-llama/Llama-3.2-3B-Chat-hf
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Framework: Transformers library compatible
• Optimization: Parameter-efficient training approach

Research Foundation

Social Media & Links

Generated Content Examples

Evaluation

Evaluation results and benchmarks will be added as they become available.

Full Documentation

For complete documentation, installation guides, and advanced usage, please visit:

• Complete README
• Ace Files Directory

Contact

• Author: CrashOverrideX
• Platform: Hugging Face
• Model Repository: CrashOverrideX/Ace_v4.2_Mini

Tags

llm, ace, cognitive-architecture, mixture-of-experts, llama-3.2, peft, lora, friendly-assistant