yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step3328

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Apr 6, 2026Architecture:Transformer Cold

The yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step3328 is an 8 billion parameter language model. This model is a fine-tuned variant, likely based on the Llama architecture, and has undergone Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Its specific differentiators and primary use cases are not detailed in the provided information, suggesting it may be a foundational or general-purpose model awaiting further specialization.

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Model Overview

The yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step3328 is an 8 billion parameter language model, likely derived from the Llama architecture. The model's name indicates it has undergone a training process involving Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), with a beta value of 1e-1 and trained up to step 3328. This suggests an iterative refinement process aimed at aligning the model's outputs with human preferences.

Key Characteristics

  • Parameter Count: 8 billion parameters, placing it in the medium-sized LLM category.
  • Training Methodology: Utilizes Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for enhanced performance and alignment.
  • Context Length: Supports an 8192-token context window, allowing for processing and generating longer sequences of text.

Current Status

As per the provided model card, specific details regarding its development, funding, language support, license, and finetuning base model are currently marked as "More Information Needed." Similarly, direct use cases, downstream applications, and out-of-scope uses are not yet defined. Users should be aware that detailed information on bias, risks, limitations, and training specifics (data, hyperparameters, evaluation) is pending.

Recommendations

Users are advised to exercise caution and conduct their own evaluations due to the lack of comprehensive documentation. Further recommendations will be provided once more information regarding the model's intended use, capabilities, and limitations becomes available.