Model Overview

This model, Adanato/llama3_8b_instruct_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_2, is an 8 billion parameter instruction-tuned variant of the meta-llama/Meta-Llama-3-8B-Instruct base model. It has been fine-tuned on the qwen25_qwen3_rank_only_cluster_2 dataset, indicating a specialized focus on tasks involving ranking or comparative analysis.

Key Characteristics

• Base Model: Meta-Llama-3-8B-Instruct
• Parameter Count: 8 billion parameters
• Context Length: 8192 tokens
• Specialization: Fine-tuned on a dataset (qwen25_qwen3_rank_only_cluster_2) suggesting optimization for ranking-related tasks.

Training Details

The model was trained with a learning rate of 1e-05, a batch size of 128 (total across 4 devices with gradient accumulation), and utilized the AdamW_Torch_Fused optimizer. Training was conducted for 1 epoch with a cosine learning rate scheduler and a 0.1 warmup ratio. The training environment included Transformers 4.57.1, Pytorch 2.10.0+cu128, Datasets 4.0.0, and Tokenizers 0.22.2.

Potential Use Cases

Given its fine-tuning on a ranking-specific dataset, this model is likely best suited for applications that involve:

• Ranking text outputs or responses.
• Comparative analysis of different options.
• Tasks where understanding and generating ranked lists are crucial.