Including TerminalTraj's data, models, and paper
AI & ML interests
None defined yet.
Recent Activity
Papers
CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation
AutoMV: An Automatic Multi-Agent System for Music Video Generation
https://arxiv.org/abs/2406.13923
CriticLean
Data and model collection for MARBLE: https://github.com/a43992899/MARBLE/
This is the collection of COIG-P's datasets
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m-a-p/COIG-P
Viewer β’ Updated β’ 1.01M β’ 58 β’ 29 -
m-a-p/COIG-P-CRM
Viewer β’ Updated β’ 484k β’ 9 β’ 4 -
m-a-p/COIG-CRBench
Viewer β’ Updated β’ 1.04k β’ 14 β’ 2 -
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values
Paper β’ 2504.05535 β’ Published β’ 44
MuPT
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m-a-p/OpenCodeInterpreter-DS-1.3B
Text Generation β’ 1B β’ Updated β’ 64 β’ 25 -
m-a-p/OpenCodeInterpreter-DS-6.7B
Text Generation β’ 7B β’ Updated β’ 148 β’ 135 -
m-a-p/OpenCodeInterpreter-DS-33B
Text Generation β’ Updated β’ 63 β’ 148 -
m-a-p/OpenCodeInterpreter-CL-7B
Text Generation β’ Updated β’ 94 β’ 11
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MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Paper β’ 2306.00107 β’ Published β’ 4 -
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Paper β’ 2309.08730 β’ Published β’ 2 -
ChatMusician: Understanding and Generating Music Intrinsically with LLM
Paper β’ 2402.16153 β’ Published β’ 58 -
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
Paper β’ 2401.11944 β’ Published β’ 27
This is the checkpoints and datasets of MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Reverse-Engineered Reasoning
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TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling
Paper β’ 2508.17445 β’ Published β’ 80 -
m-a-p/TreePO-Qwen2.5-7B
Text Generation β’ 8B β’ Updated β’ 30 β’ 2 -
m-a-p/TreePO_data
Viewer β’ Updated β’ 3.12k β’ 194 -
m-a-p/TreePO-Qwen2.5-7B_fixed-div
8B β’ Updated β’ 22
All 1.3B & 340M hybrid linear-attention experiments.
This is the collections of COIG-P's models
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m-a-p/Infinity-Instruct-3M-0625-Llama3-8B-COIG-P
Text Generation β’ 8B β’ Updated β’ 25 -
m-a-p/Qwen2.5-Instruct-7B-COIG-P
Text Generation β’ 8B β’ Updated β’ 26 -
m-a-p/Infinity-Instruct-3M-0625-Mistral-7B-COIG-P
Text Generation β’ 7B β’ Updated β’ 26 -
m-a-p/Qwen2-Instruct-7B-COIG-P
Text Generation β’ 8B β’ Updated β’ 21
YuE: Open Full-song Generation Foundation Model
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m-a-p/YuE-s1-7B-anneal-en-cot
Text Generation β’ 6B β’ Updated β’ 14.4k β’ 442 -
m-a-p/YuE-s1-7B-anneal-en-icl
Text Generation β’ 6B β’ Updated β’ 768 β’ 52 -
m-a-p/YuE-s1-7B-anneal-jp-kr-cot
Text Generation β’ 6B β’ Updated β’ 2.37k β’ 23 -
m-a-p/YuE-s1-7B-anneal-jp-kr-icl
Text Generation β’ 6B β’ Updated β’ 107 β’ 11
The checkpoints for the MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training.
Dataset and Models of Chinese Open Instruction Generalist Series.
Datasets, Benchmark and Models of ChatMusician: Understanding and Generating Music Intrinsically with LLM
Neo
Including TerminalTraj's data, models, and paper
Reverse-Engineered Reasoning
https://arxiv.org/abs/2406.13923
-
TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling
Paper β’ 2508.17445 β’ Published β’ 80 -
m-a-p/TreePO-Qwen2.5-7B
Text Generation β’ 8B β’ Updated β’ 30 β’ 2 -
m-a-p/TreePO_data
Viewer β’ Updated β’ 3.12k β’ 194 -
m-a-p/TreePO-Qwen2.5-7B_fixed-div
8B β’ Updated β’ 22
CriticLean
All 1.3B & 340M hybrid linear-attention experiments.
Data and model collection for MARBLE: https://github.com/a43992899/MARBLE/
This is the collections of COIG-P's models
-
m-a-p/Infinity-Instruct-3M-0625-Llama3-8B-COIG-P
Text Generation β’ 8B β’ Updated β’ 25 -
m-a-p/Qwen2.5-Instruct-7B-COIG-P
Text Generation β’ 8B β’ Updated β’ 26 -
m-a-p/Infinity-Instruct-3M-0625-Mistral-7B-COIG-P
Text Generation β’ 7B β’ Updated β’ 26 -
m-a-p/Qwen2-Instruct-7B-COIG-P
Text Generation β’ 8B β’ Updated β’ 21
This is the collection of COIG-P's datasets
-
m-a-p/COIG-P
Viewer β’ Updated β’ 1.01M β’ 58 β’ 29 -
m-a-p/COIG-P-CRM
Viewer β’ Updated β’ 484k β’ 9 β’ 4 -
m-a-p/COIG-CRBench
Viewer β’ Updated β’ 1.04k β’ 14 β’ 2 -
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values
Paper β’ 2504.05535 β’ Published β’ 44
YuE: Open Full-song Generation Foundation Model
-
m-a-p/YuE-s1-7B-anneal-en-cot
Text Generation β’ 6B β’ Updated β’ 14.4k β’ 442 -
m-a-p/YuE-s1-7B-anneal-en-icl
Text Generation β’ 6B β’ Updated β’ 768 β’ 52 -
m-a-p/YuE-s1-7B-anneal-jp-kr-cot
Text Generation β’ 6B β’ Updated β’ 2.37k β’ 23 -
m-a-p/YuE-s1-7B-anneal-jp-kr-icl
Text Generation β’ 6B β’ Updated β’ 107 β’ 11
The checkpoints for the MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training.
MuPT
Dataset and Models of Chinese Open Instruction Generalist Series.
-
m-a-p/OpenCodeInterpreter-DS-1.3B
Text Generation β’ 1B β’ Updated β’ 64 β’ 25 -
m-a-p/OpenCodeInterpreter-DS-6.7B
Text Generation β’ 7B β’ Updated β’ 148 β’ 135 -
m-a-p/OpenCodeInterpreter-DS-33B
Text Generation β’ Updated β’ 63 β’ 148 -
m-a-p/OpenCodeInterpreter-CL-7B
Text Generation β’ Updated β’ 94 β’ 11
Datasets, Benchmark and Models of ChatMusician: Understanding and Generating Music Intrinsically with LLM
-
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Paper β’ 2306.00107 β’ Published β’ 4 -
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Paper β’ 2309.08730 β’ Published β’ 2 -
ChatMusician: Understanding and Generating Music Intrinsically with LLM
Paper β’ 2402.16153 β’ Published β’ 58 -
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
Paper β’ 2401.11944 β’ Published β’ 27
This is the checkpoints and datasets of MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Neo