yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step10240
The yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step10240 is an 8 billion parameter language model with an 8192 token context length. This model is a fine-tuned variant, likely based on the Llama architecture, optimized through Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Its specific differentiators and primary use cases are not detailed in the provided model card, which indicates 'More Information Needed' across most sections.
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Overview
The yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step10240 is an 8 billion parameter language model, likely derived from the Llama family, featuring an 8192 token context window. The model's name suggests it has undergone Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) with a beta value of 1e-1, trained up to step 10240. However, the provided model card indicates that detailed information regarding its development, specific capabilities, intended uses, training data, evaluation results, and potential biases or limitations is currently unavailable.
Key Characteristics
- Parameter Count: 8 billion parameters
- Context Length: 8192 tokens
- Training Method: Implies Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO)
Current Status
As per the model card, most sections, including direct use cases, downstream applications, out-of-scope uses, bias, risks, limitations, training data, training hyperparameters, and evaluation results, are marked with "More Information Needed." Users are advised to exercise caution and await further documentation before deploying this model in production environments.