base_model: unsloth/qwq-32b-preview-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
new_version: Daemontatox/PathFinderAI2.0
QWQ-32B Model Card
- Developed by: Daemontatox
- License: Apache-2.0
- Base Model: unsloth/qwq-32b-preview-bnb-4bit
Model Overview
The PathFinderAI2.0 is an advanced large language model (LLM) designed for high-performance text generation tasks. It has been finetuned from the base model using the Unsloth framework and Hugging Face's TRL library, achieving superior speed and efficiency during training.
Key Features
- Enhanced Training Speed: Training was completed 2x faster compared to traditional methods, thanks to the optimization techniques provided by Unsloth.
- Transformer-Based Architecture: Built on the Qwen2 architecture, ensuring state-of-the-art performance in text generation and comprehension.
- Low-Bit Quantization: Utilizes 4-bit quantization (bnb-4bit), offering a balance between performance and computational efficiency.
Use Cases
- Creative Writing and Content Generation
- Summarization and Translation
- Dialogue and Conversational Agents
- Research Assistance
Performance Metrics
The PathFinderAI2.0 model demonstrates High-level benchmarks across multiple text-generation datasets, highlighting its capabilities in both reasoning and creativity-focused tasks. Detailed evaluation results will be released in an upcoming report.
Model Training
The finetuning process leveraged:
- Unsloth: A next-generation framework for faster and efficient LLM training.
- Hugging Face's TRL library: Tools for reinforcement learning with human feedback (RLHF).
Limitations
- Requires significant GPU resources for deployment despite the 4-bit quantization.
- Not explicitly designed for domain-specific tasks; additional fine-tuning may be required.
Getting Started
You can load the model with Hugging Face's Transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Daemontatox/PathFinderAI2.0")
model = AutoModelForCausalLM.from_pretrained("Daemontatox/PathFinderAI2.0", device_map="auto", load_in_4bit=True)
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Acknowledgments
Special thanks to the Unsloth team and the Hugging Face community for their support and tools, making the development of PathFinderAI2.0 possible.
