Adanato/llama3_8b_instruct_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_2
Adanato/llama3_8b_instruct_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_2 is an 8 billion parameter instruction-tuned language model, fine-tuned from Meta-Llama-3-8B-Instruct. This model is specifically adapted using the qwen25_qwen3_rank_only_cluster_2 dataset, suggesting a specialization in tasks related to ranking or comparative evaluation. With an 8192-token context length, it is suitable for applications requiring nuanced understanding and generation based on ranked data.
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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.