yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step8704
The yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step8704 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 that more information is needed regarding its development, training, and intended applications.
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Overview
This model, yunjae-won/mpq3_llama8b_sft_dpo_beta1e-1_step8704, is an 8 billion parameter language model with an 8192 token context length. It has been fine-tuned using a combination of Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), suggesting an emphasis on aligning its outputs with human preferences or specific task instructions. The model card indicates that further details regarding its architecture, training data, and specific development are currently pending.
Key Characteristics
- Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192 token context window.
- Training Methodology: Utilizes Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO).
Current Limitations
The provided model card explicitly states that "More Information Needed" for several critical sections, including:
- Model type, language(s), and license.
- Specific use cases (direct and downstream).
- Bias, risks, and limitations.
- Training data and procedure details.
- Evaluation results and environmental impact.
Users should be aware that without this information, the model's capabilities, appropriate applications, and potential risks are not fully documented.