yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step2304
The yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step2304 is an 8 billion parameter language model based on the Llama architecture. This model has been fine-tuned using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) techniques. With an 8192 token context length, it is designed for general language generation tasks, leveraging advanced alignment methods.
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Overview
This model, yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step2304, is an 8 billion parameter language model built upon the Llama architecture. It has undergone a two-stage fine-tuning process, incorporating both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) with a beta value of 1e-1, specifically at step 2304 of its training. The model supports a context length of 8192 tokens.
Key Characteristics
- Architecture: Llama-based, 8 billion parameters.
- Fine-tuning: Utilizes Supervised Fine-Tuning (SFT) for initial instruction following and Direct Preference Optimization (DPO) for alignment with human preferences.
- Context Length: Supports an 8192-token context window, allowing for processing longer inputs and generating more coherent responses.
Potential Use Cases
Given its Llama base and advanced alignment techniques, this model is suitable for a variety of general-purpose natural language processing tasks. However, specific performance metrics and intended applications are not detailed in the provided model card. Users should conduct their own evaluations for specific use cases.