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AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 133 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 136 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 52
Collections
Discover the best community collections!
Collections including paper arxiv:2403.13372
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AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 133 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 136 -
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Paper • 2509.03405 • Published • 17 -
KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications
Paper • 2503.17247 • Published • 1
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LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 136 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 52 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 251
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Survey on Evaluation of LLM-based Agents
Paper • 2503.16416 • Published • 96 -
Qwen2.5-Omni Technical Report
Paper • 2503.20215 • Published • 165 -
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 302 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 136
-
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 133 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 136 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 52
-
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 136 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 52 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 251
-
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 133 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 136 -
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Paper • 2509.03405 • Published • 17 -
KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications
Paper • 2503.17247 • Published • 1
-
Survey on Evaluation of LLM-based Agents
Paper • 2503.16416 • Published • 96 -
Qwen2.5-Omni Technical Report
Paper • 2503.20215 • Published • 165 -
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 302 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 136