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C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
Paper β’ 2512.21332 β’ Published β’ 16 -
codefuse-ai/C2LLM-7B
Feature Extraction β’ 8B β’ Updated β’ 311 β’ 9 -
codefuse-ai/C2LLM-0.5B
Feature Extraction β’ 0.5B β’ Updated β’ 535 β’ 8 -
codefuse-ai/F2LLM-0.6B
Feature Extraction β’ 0.6B β’ Updated β’ 753 β’ 12
CodeFuse AI
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Papers
C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
Organization Card
Hello World! This is CodeFuse!
CodeFuse aims to develop Code Large Language Models (Code LLMs) to support and enhance full-lifecycle AI native sotware developing, covering crucial stages such as design requirements, coding, testing, building, deployment, operations, and insight analysis;
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MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Paper β’ 2311.02303 β’ Published β’ 12 -
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Paper β’ 2310.06266 β’ Published β’ 2 -
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models
Paper β’ 2410.06741 β’ Published β’ 3 -
Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM
Paper β’ 2503.17793 β’ Published β’ 23
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C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
Paper β’ 2512.21332 β’ Published β’ 16 -
codefuse-ai/C2LLM-7B
Feature Extraction β’ 8B β’ Updated β’ 311 β’ 9 -
codefuse-ai/C2LLM-0.5B
Feature Extraction β’ 0.5B β’ Updated β’ 535 β’ 8 -
codefuse-ai/F2LLM-0.6B
Feature Extraction β’ 0.6B β’ Updated β’ 753 β’ 12
-
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Paper β’ 2311.02303 β’ Published β’ 12 -
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Paper β’ 2310.06266 β’ Published β’ 2 -
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models
Paper β’ 2410.06741 β’ Published β’ 3 -
Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM
Paper β’ 2503.17793 β’ Published β’ 23
models
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codefuse-ai/OpAgent
Updated
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codefuse-ai/F2LLM-v2-4B-Preview
Feature Extraction
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4B
β’
Updated
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9
β’
1
codefuse-ai/F2LLM-v2-0.6B-Preview
Feature Extraction
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0.6B
β’
Updated
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10
β’
1
codefuse-ai/F2LLM-v2-1.7B-Preview
Feature Extraction
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2B
β’
Updated
β’
8
β’
2
codefuse-ai/F2LLM-v2-8B-Preview
Feature Extraction
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8B
β’
Updated
β’
19
β’
3
codefuse-ai/C2LLM-0.5B
Feature Extraction
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0.5B
β’
Updated
β’
535
β’
8
codefuse-ai/C2LLM-7B
Feature Extraction
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8B
β’
Updated
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311
β’
9
codefuse-ai/CodeFuse-SVR-8B
Updated
codefuse-ai/F2LLM-4B
Feature Extraction
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4B
β’
Updated
β’
475
β’
11
codefuse-ai/F2LLM-1.7B
Feature Extraction
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2B
β’
Updated
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67
β’
8
datasets
8
codefuse-ai/F2LLM
Preview
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Updated
β’
1.17k
β’
8
codefuse-ai/CodeFuse_codeedit
Viewer
β’
Updated
β’
61
β’
43
β’
3
codefuse-ai/CodeGraph
Viewer
β’
Updated
β’
275
β’
153
β’
5
codefuse-ai/Evol-instruction-66k
Updated
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270
β’
74
codefuse-ai/CodeExercise-Python-27k
Updated
β’
668
β’
67
codefuse-ai/GALLa
Viewer
β’
Updated
β’
627k
β’
57
β’
3
codefuse-ai/CodeFuse-DevOps-Eval
Preview
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62
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20
codefuse-ai/CodeFuseEval
Updated
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244
β’
8