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C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
Paper • 2512.21332 • Published • 6 -
codefuse-ai/C2LLM-7B
Feature Extraction • 8B • Updated • 91 • 4 -
codefuse-ai/C2LLM-0.5B
Feature Extraction • 0.5B • Updated • 72 • 5 -
codefuse-ai/F2LLM-0.6B
Feature Extraction • 0.6B • Updated • 229 • 11
CodeFuse AI
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Recent Activity
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 • 2311.02303 • Published • 12 -
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Paper • 2310.06266 • Published • 2 -
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models
Paper • 2410.06741 • Published • 3 -
Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM
Paper • 2503.17793 • Published • 23
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C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
Paper • 2512.21332 • Published • 6 -
codefuse-ai/C2LLM-7B
Feature Extraction • 8B • Updated • 91 • 4 -
codefuse-ai/C2LLM-0.5B
Feature Extraction • 0.5B • Updated • 72 • 5 -
codefuse-ai/F2LLM-0.6B
Feature Extraction • 0.6B • Updated • 229 • 11
-
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Paper • 2311.02303 • Published • 12 -
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Paper • 2310.06266 • Published • 2 -
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models
Paper • 2410.06741 • Published • 3 -
Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM
Paper • 2503.17793 • Published • 23
models
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codefuse-ai/C2LLM-0.5B
Feature Extraction
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0.5B
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Updated
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72
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5
codefuse-ai/C2LLM-7B
Feature Extraction
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8B
•
Updated
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91
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4
codefuse-ai/F2LLM-4B
Feature Extraction
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4B
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Updated
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1.23k
•
10
codefuse-ai/F2LLM-1.7B
Feature Extraction
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2B
•
Updated
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195
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7
codefuse-ai/F2LLM-0.6B
Feature Extraction
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0.6B
•
Updated
•
229
•
11
codefuse-ai/CodeFuse-CGM-72B
73B
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Updated
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28
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14
codefuse-ai/Rodimus-Plus-Coder-4B-Chat
5B
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Updated
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14
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2
codefuse-ai/Rodimus-Plus-Coder-4B-Base
Updated
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15
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1
codefuse-ai/Rodimus-Plus-Coder-1.6B-Chat
2B
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Updated
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14
codefuse-ai/Rodimus-Plus-Coder-1.6B-Base
Updated
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9
datasets
8
codefuse-ai/F2LLM
Preview
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Updated
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1.82k
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7
codefuse-ai/CodeFuse_codeedit
Viewer
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Updated
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61
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75
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2
codefuse-ai/CodeGraph
Viewer
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Updated
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275
•
771
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5
codefuse-ai/Evol-instruction-66k
Updated
•
254
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74
codefuse-ai/CodeExercise-Python-27k
Updated
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544
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66
codefuse-ai/GALLa
Viewer
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Updated
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627k
•
77
•
3
codefuse-ai/CodeFuse-DevOps-Eval
Preview
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Updated
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172
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19
codefuse-ai/CodeFuseEval
Updated
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301
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7