jaymanaryan/CleanPool-v2-w30
The jaymanaryan/CleanPool-v2-w30 is a 0.8 billion parameter language model merge, built upon the Qwen3-0.6B base using the DARE TIES method. This model integrates several specialized Qwen3-0.6B variants, including those focused on 'Treatment' and 'Diagnose' tasks, alongside 'dreamwriter' and 'Cerium' models. It is designed to combine diverse capabilities from its constituent models, offering a versatile small-scale language solution with a 32768 token context length.
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Model Overview
The jaymanaryan/CleanPool-v2-w30 is a 0.8 billion parameter language model created by jaymanaryan through a merge of several pre-trained models. It utilizes the DARE TIES merge method, with Qwen/Qwen3-0.6B serving as the foundational base model.
Key Characteristics
This model is a composite of multiple specialized Qwen3-0.6B variants, aiming to consolidate their respective strengths. The merged components include:
mrfakename/dreamwriter-0.6b-betaprithivMLmods/Cerium-Qwen3-R1-Devsuayptalha/Qwen3-0.6B-Treatmentsuayptalha/Qwen3-0.6B-DiagnoseprithivMLmods/Nenque-MoT-0.6B-Elite14
Each of these models contributed to the final merge with specific density and weight parameters, as configured in the DARE TIES process. The merge configuration also specified int8_mask: true and dtype: bfloat16.
Potential Use Cases
Given its merged nature from models with names suggesting specific functionalities (e.g., 'Treatment', 'Diagnose', 'dreamwriter'), CleanPool-v2-w30 is likely intended for applications requiring a blend of capabilities. Its small parameter count (0.8B) and substantial context length (32768 tokens) make it suitable for efficient deployment in scenarios where resource constraints are a factor, while still handling moderately long inputs.