PathfinderAI 4.0

Model Overview

This model is a fine-tuned version of FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview, based on the Qwen2 architecture. It has been optimized using Unsloth for significantly improved training efficiency, reducing compute time by 2x while maintaining high performance across various NLP benchmarks.

Fine-tuning was performed using Hugging Face’s TRL (Transformers Reinforcement Learning) library, ensuring adaptability for complex reasoning, natural language generation (NLG), and conversational AI tasks.

Model Details

Developed by: Daemontatox
Base Model: FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview
License: Apache-2.0
Model Type: Qwen2-based large-scale transformer
Optimization Framework: Unsloth
Fine-tuning Methodology: LoRA (Low-Rank Adaptation) & Full Fine-Tuning
Quantization Support: 4-bit and 8-bit for deployment on resource-constrained devices
Training Library: Hugging Face TRL

Training & Fine-Tuning Details

Optimization with Unsloth

Unsloth significantly accelerates fine-tuning by reducing memory overhead and improving hardware utilization. The model was fine-tuned twice as fast as conventional methods, leveraging Flash Attention 2 and PagedAttention for enhanced performance.

Fine-Tuning Method

The model was fine-tuned using parameter-efficient techniques, including:

QLoRA (Quantized LoRA) for reduced memory usage.
Full fine-tuning on select layers to maintain original capabilities while improving specific tasks.
RLHF (Reinforcement Learning with Human Feedback) for improved alignment with human preferences.

Intended Use & Applications

Primary Use Cases

Conversational AI: Enhances chatbot interactions with better contextual awareness and logical coherence.
Text Generation & Completion: Ideal for content creation, report writing, and creative writing.
Mathematical & Logical Reasoning: Can assist in education, problem-solving, and automated theorem proving.
Research & Development: Useful for scientific research, data analysis, and language modeling experiments.

Deployment

The model supports 4-bit and 8-bit quantization, making it deployable on resource-constrained devices while maintaining high performance.

Limitations & Ethical Considerations

Limitations

Bias & Hallucination: The model may still generate biased or hallucinated outputs, especially in highly subjective or low-resource domains.
Computation Requirements: While optimized, the model still requires significant GPU resources for inference at full precision.
Context Length Constraints: Long-context understanding is improved, but performance may degrade on extremely long prompts.

Ethical Considerations

Use responsibly: The model should not be used for misinformation, deepfake generation, or harmful AI applications.
Bias Mitigation: Efforts have been made to reduce bias, but users should validate outputs in sensitive applications.

How to Use the Model

Example Code for Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Daemontatox/PathFinderAI4.0"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "Explain the significance of reinforcement learning in AI."
inputs = tokenizer(input_text, return_tensors="pt")

output = model.generate(**inputs, max_length=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Using with Unsloth (Optimized LoRA Inference)

from unsloth import FastAutoModelForCausalLM

model = FastAutoModelForCausalLM.from_pretrained(model_name,
    load_in_4bit=True  # Efficient deployment
)


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Acknowledgments

Special thanks to:

Unsloth AI for their efficient fine-tuning framework.

The open-source AI community for continuous innovation.

Daemontatox
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PathFinderAI4.0