--- title: "Who cites TinyLLM: A Framework for Training — 6 citing papers" description: "6 papers of the Elamur Research library cite «TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers»…" canonical: "https://research.elamur.ai/free/en/papers/kandala-2024-tinyllm-framework-training-deploying-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kandala-2024-tinyllm-framework-training-deploying-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [TinyLLM: A Framework for Training and Deploying…](https://md.research.elamur.ai/free/en/papers/kandala-2024-tinyllm-framework-training-deploying-language/) / cited by # Who cites «TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers» 6 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers»](https://md.research.elamur.ai/free/en/papers/kandala-2024-tinyllm-framework-training-deploying-language/) 1. **[BitMar: Low-Bit Multimodal Fusion with Episodic Memory for Edge Devices](https://md.research.elamur.ai/free/en/papers/aman-2025-bitmar-low-bit-multimodal-fusion/) [BabyLM Workshop](https://md.research.elamur.ai/free/en/venues/babylm/)**Euhid Aman, Esteban Carlin, Hsing-Kuo Kenneth Pao, et al.*2025 · 1 citation* 2. **[Reason-to-Learn (R2L): Multi-Agent Knowledge Distillation for Lightweight LLMs in Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/tu-2026-reason-learn-r2l-multi-agent/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Le-Huy Tu, Quan Nguyen, Vincent NGUYEN, et al.*2026* 4 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kandala-2024-tinyllm-framework-training-deploying-language/) — TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers