Adanato/llama3_8b_instruct_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_5
Adanato/llama3_8b_instruct_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_5 is an 8 billion parameter instruction-tuned language model, fine-tuned from Meta-Llama-3-8B-Instruct. It was trained on the qwen25_qwen3_rank_only_cluster_5 dataset with a context length of 8192 tokens. This model is specialized through its fine-tuning process, aiming to adapt the base Llama 3 capabilities to specific data characteristics, making it suitable for tasks aligned with its training dataset.
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Overview
This model, Adanato/llama3_8b_instruct_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_5, is an 8 billion parameter instruction-tuned language model. It is a fine-tuned variant of the robust Meta-Llama-3-8B-Instruct base model, specifically adapted using the qwen25_qwen3_rank_only_cluster_5 dataset.
Key Capabilities
- Instruction Following: Inherits and refines the instruction-following capabilities of the Llama 3 8B Instruct base model.
- Specialized Adaptation: Fine-tuned on a specific dataset, suggesting potential specialization for tasks or data distributions present in
qwen25_qwen3_rank_only_cluster_5. - Context Handling: Supports a context length of 8192 tokens, allowing for processing of moderately long inputs.
Training Details
The model was trained with a learning rate of 1e-05, a total training batch size of 128 (across 4 GPUs with 8 gradient accumulation steps), and a cosine learning rate scheduler with a 0.1 warmup ratio. Training was conducted for 1 epoch using the AdamW_TORCH_FUSED optimizer.
Good for
- Applications requiring a specialized Llama 3 8B Instruct model tailored to the characteristics of the
qwen25_qwen3_rank_only_cluster_5dataset. - Tasks where the specific fine-tuning data provides an advantage over the general-purpose base model.
- Scenarios benefiting from an 8B parameter model with an 8192-token context window.