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F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World
Paper β’ 2603.19223 β’ Published β’ 30 -
codefuse-ai/F2LLM-v2-14B
Feature Extraction β’ 14B β’ Updated β’ 693 β’ 5 -
codefuse-ai/F2LLM-v2-8B
Feature Extraction β’ 8B β’ Updated β’ 450 β’ 3 -
codefuse-ai/F2LLM-v2-4B
Feature Extraction β’ 4B β’ Updated β’ 808 β’ 2
CodeFuse AI
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AI & ML interests
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Recent Activity
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Papers
F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World
C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
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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F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World
Paper β’ 2603.19223 β’ Published β’ 30 -
C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
Paper β’ 2512.21332 β’ Published β’ 17 -
F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
Paper β’ 2510.02294 β’ Published β’ 48
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F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World
Paper β’ 2603.19223 β’ Published β’ 30 -
codefuse-ai/F2LLM-v2-14B
Feature Extraction β’ 14B β’ Updated β’ 693 β’ 5 -
codefuse-ai/F2LLM-v2-8B
Feature Extraction β’ 8B β’ Updated β’ 450 β’ 3 -
codefuse-ai/F2LLM-v2-4B
Feature Extraction β’ 4B β’ Updated β’ 808 β’ 2
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F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World
Paper β’ 2603.19223 β’ Published β’ 30 -
C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
Paper β’ 2512.21332 β’ Published β’ 17 -
F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
Paper β’ 2510.02294 β’ Published β’ 48
models 44
codefuse-ai/F2LLM-v2-14B
Feature Extraction β’ 14B β’ Updated β’ 693 β’ 5
codefuse-ai/F2LLM-v2-8B
Feature Extraction β’ 8B β’ Updated β’ 450 β’ 3
codefuse-ai/F2LLM-v2-4B
Feature Extraction β’ 4B β’ Updated β’ 808 β’ 2
codefuse-ai/F2LLM-v2-1.7B
Feature Extraction β’ Updated β’ 1.65k β’ 4
codefuse-ai/F2LLM-v2-0.6B
Feature Extraction β’ Updated β’ 1.1k β’ 2
codefuse-ai/F2LLM-v2-330M
Feature Extraction β’ Updated β’ 242 β’ 2
codefuse-ai/F2LLM-v2-160M
Feature Extraction β’ Updated β’ 556 β’ 2
codefuse-ai/F2LLM-v2-80M
Feature Extraction β’ Updated β’ 465 β’ 5
codefuse-ai/F2LLM-v2-14B-Preview
Feature Extraction β’ 14B β’ Updated β’ 31 β’ 2
codefuse-ai/F2LLM-v2-8B-Preview
Feature Extraction β’ 8B β’ Updated β’ 34 β’ 3
datasets 9
codefuse-ai/F2LLM-v2
Viewer β’ Updated β’ 60.1M β’ 710 β’ 3
codefuse-ai/F2LLM
Preview β’ Updated β’ 1.93k β’ 8
codefuse-ai/CodeFuse_codeedit
Viewer β’ Updated β’ 61 β’ 30 β’ 3
codefuse-ai/CodeGraph
Viewer β’ Updated β’ 275 β’ 236 β’ 5
codefuse-ai/Evol-instruction-66k
Updated β’ 207 β’ 75
codefuse-ai/CodeExercise-Python-27k
Updated β’ 507 β’ 67
codefuse-ai/GALLa
Viewer β’ Updated β’ 627k β’ 97 β’ 3
codefuse-ai/CodeFuse-DevOps-Eval
Preview β’ Updated β’ 86 β’ 20
codefuse-ai/CodeFuseEval
Updated β’ 285 β’ 8