✨LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

📝 Summary:
LlamaFactory is a unified framework for efficient, no-code fine-tuning of over 100 large language models. It provides a web-based user interface, LlamaBoard, to simplify customization for various tasks.

🔹 Publication Date: Published on Mar 20, 2024

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/llamafactory-unified-efficient-fine-tuning-of-100-language-models
• PDF: https://arxiv.org/pdf/2403.13372
• Project Page: https://huggingface.co/spaces/hiyouga/LLaMA-Board
• Github: https://github.com/hiyouga/LLaMA-Factory

🔹 Models citing this paper:
• https://huggingface.co/AELLM/Llama-3.2-Chibi-3B
• https://huggingface.co/GXMZU/Qwen3-14B-ai-expert-250925
• https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Justinrune/LLaMA-Factory
• https://huggingface.co/spaces/featherless-ai/try-this-model
• https://huggingface.co/spaces/Darok/Featherless-Feud

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✓ https://t.me/DataScienceT

#LlamaFactory #LLM #FineTuning #AI #MachineLearning

✨Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

📝 Summary:
Easy Dataset is a framework that synthesizes LLM fine-tuning data from unstructured documents using a GUI and LLMs. It generates domain-specific question-answer pairs with human oversight. This improves LLM performance in specific domains while retaining general knowledge.

🔹 Publication Date: Published on Jul 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.04009
• PDF: https://arxiv.org/pdf/2507.04009
• Github: https://github.com/ConardLi/easy-dataset

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✓ https://t.me/DataScienceT

#LLM #DataSynthesis #FineTuning #AI #NLP

✨GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation

📝 Summary:
GraphGen is a framework that enhances synthetic data generation for LLMs by constructing fine-grained knowledge graphs. It targets high-value knowledge gaps, uses multi-hop sampling, and style-controlled generation to create diverse and accurate QA pairs. This approach outperforms conventional me...

🔹 Publication Date: Published on May 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.20416
• PDF: https://arxiv.org/pdf/2505.20416
• Project Page: https://huggingface.co/spaces/chenzihong/GraphGen
• Github: https://github.com/open-sciencelab/GraphGen

✨ Datasets citing this paper:
• https://huggingface.co/datasets/chenzihong/GraphGen-Data

✨ Spaces citing this paper:
• https://huggingface.co/spaces/chenzihong/GraphGen

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✓ https://t.me/DataScienceT

#LLMs #KnowledgeGraphs #SyntheticData #FineTuning #NLP

🤖🧠 How to Run and Fine-Tune Kimi K2 Thinking Locally with Unsloth

🗓️ 11 Dec 2025
📚 AI News & Trends

The demand for efficient and powerful large language models (LLMs) continues to rise as developers and researchers seek new ways to optimize reasoning, coding, and conversational AI performance. One of the most impressive open-source AI systems available today is Kimi K2 Thinking, created by Moonshot AI. Through collaboration with Unsloth, users can now fine-tune and ...

#KimiK2Thinking #Unsloth #LLMs #LargeLanguageModels #AI #FineTuning

✨SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving

📝 Summary:
SWE-Lego achieves state-of-the-art software issue resolution through a lightweight supervised fine-tuning approach. It uses a high-quality dataset and refined training procedures like error masking and a difficulty-based curriculum, outperforming complex methods. Performance is further boosted by...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01426
• PDF: https://arxiv.org/pdf/2601.01426
• Project Page: https://github.com/SWE-Lego/SWE-Lego
• Github: https://github.com/SWE-Lego/SWE-Lego

🔹 Models citing this paper:
• https://huggingface.co/SWE-Lego/SWE-Lego-Qwen3-8B
• https://huggingface.co/SWE-Lego/SWE-Lego-Qwen3-32B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/SWE-Lego/SWE-Lego-Real-Data
• https://huggingface.co/datasets/SWE-Lego/SWE-Lego-Synthetic-Data

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✓ https://t.me/DataScienceT

#SoftwareEngineering #MachineLearning #LLM #FineTuning #AIforCode