Adanato/mistral_nemo_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_1
Adanato/mistral_nemo_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_1 is a 12 billion parameter language model fine-tuned from Mistral-Nemo-Instruct-2407. This model was specifically fine-tuned on the qwen25_qwen3_rank_only_cluster_1 dataset, suggesting a specialization in tasks related to ranking or comparative analysis within the Qwen2.5 and Qwen3 model outputs. Its primary use case is likely for applications requiring nuanced understanding and ranking capabilities derived from the specified Qwen model clusters.
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Overview
This model, Adanato/mistral_nemo_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_1, is a 12 billion parameter language model built upon the mistralai/Mistral-Nemo-Instruct-2407 architecture. It has been specifically fine-tuned using the qwen25_qwen3_rank_only_cluster_1 dataset.
Key Capabilities
- Specialized Fine-tuning: The model's training on a specific ranking dataset (qwen25_qwen3_rank_only_cluster_1) indicates a potential for enhanced performance in tasks involving comparative evaluation or preference ranking, particularly concerning outputs from Qwen2.5 and Qwen3 models.
- Base Model Strength: Inherits the foundational capabilities of the Mistral-Nemo-Instruct-2407 model, which typically includes strong general language understanding and instruction following.
Training Details
The fine-tuning process involved a learning rate of 1e-05, a train batch size of 4, and a total training batch size of 128 across 4 GPUs. The training utilized the AdamW_Torch_Fused optimizer with a cosine learning rate scheduler and a warmup ratio of 0.1 over 1 epoch.
Good For
- Applications requiring ranking or preference analysis of text, especially in contexts related to Qwen2.5 and Qwen3 model outputs.
- Tasks where a model needs to discern subtle differences and assign relative scores or ranks based on specific criteria.
- Developers looking for a specialized model that leverages the strengths of Mistral-Nemo-Instruct with a targeted ranking capability.