base_model: FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- reason
- Chain-of-Thought
- deep thinking
license: apache-2.0
language:
- en
datasets:
- bespokelabs/Bespoke-Stratos-17k
- Daemontatox/Deepthinking-COT
- Daemontatox/Qwqloncotam
- Daemontatox/Reasoning_am
library_name: transformers
new_version: Daemontatox/PathFinderAI4.0
pipeline_tag: text-generation
PathfinderAI 4.0
WARNING THIS IS A FAILED FINETUNE
THIS IS MERELY A TEST ATTEMPT OF FINETUNING
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.