bhuvana-ak7/OrpoLlama-3.2-1B-V1_q4_k_m
The bhuvana-ak7/OrpoLlama-3.2-1B-V1_q4_k_m is a 1 billion parameter quantized language model, fine-tuned by bhuvana-ak7 using the ORPO (Optimized Regularization for Prompt Optimization) Trainer. It is based on the meta-llama/Llama-3.2-1B architecture and was trained on a subset of the mlabonne/orpo-dpo-mix-40k dataset. This model maintains the performance of its base version, as indicated by hellaswag benchmarks, making it suitable for efficient deployment in applications requiring a compact yet capable LLM.
Loading preview...
Model Overview
The bhuvana-ak7/OrpoLlama-3.2-1B-V1_q4_k_m is a 1 billion parameter language model, representing a quantized version of the bhuvana-ak7/OrpoLlama-3.2-1B-V1 model. It is built upon the meta-llama/Llama-3.2-1B architecture and has been fine-tuned using the ORPO (Optimized Regularization for Prompt Optimization) Trainer.
Key Characteristics
- Quantized Version: This model is a quantized variant, designed for more efficient inference and reduced memory footprint.
- ORPO Fine-tuning: It leverages the ORPO training method, specifically fine-tuned on 1000 samples from the
mlabonne/orpo-dpo-mix-40kdataset. - Performance Preservation: Despite quantization, the model demonstrates identical performance to its non-quantized base model on the hellaswag benchmark, with an
accof 0.4772 andacc_normof 0.6366. - Context Length: Supports a context length of 32768 tokens.
Use Cases
This model is well-suited for applications where:
- Resource Efficiency is critical, due to its quantized nature.
- Compact Deployment is required, offering a capable LLM in a smaller package.
- General Language Understanding tasks are needed, maintaining performance on benchmarks like hellaswag.