Fine-tune Llama 3.1 Ultra-Efficiently with @UnslothAI New comprehensive guide about supervised fine-tuning on @huggingface
. Over the last year, I've done a lot of fine-tuning and blogging. This guide brings it all together. Article: https://
huggingface.co/blog/mlabonne/
sft-llama3
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LLMS
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Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth
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PEFT v0.12.0 Release: OLoRA, X-LoRA, FourierFT Methods
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in case you missed it last week: peft v0.12.0 just dropped With some cool new param-efficient methods like OLoRA, X-LoRA, FourierFT, and more
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SearchGPT: The AI Redefining Internet Search
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[#Article] SearchGPT: The AI Redefining Internet Search https://actuia.com/actualite/searchgpt-lia-qui-redefinit-la-recherche-sur-internet/
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Microsoft Generative AI for Beginners: 18 Lessons Learning Guide
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GitHub – microsoft/generative-ai-for-beginners: 18 Lessons, Get Started Building with Generative AI https://
bit.ly/461joDf https://
bit.ly/4fbeyb5
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Development of image support for AI model confirmed
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Yes! Image support has be in development for more than a month already
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Discussion on Grok AI coding capabilities and system prompts
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Image upload yes. Maybe Grok for Coding can be out as well but it is not super different from Regular, it just has a different system prompt.
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Testing Grok’s Multimodal Capabilities with Text File Uploads
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It seems like Grok is learning how to work with text files Currently, the X version only accepts images while a standalone version can also read *.txt files. In the sample, I uploaded a text file with json content. The file upload feature is still hidden on both.
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Perplexity features custom prompts and model selection
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Nope. Perplexity offers collections with custom prompts applied to search tasks plus you can choose between different models on the go
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Model Collapse: Training AI on Synthetic Data Risks
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9/ Model Collapse on Synthetic Data – investigates the effects of training models on recursively generated data; finds that training on model-generated content can cause irreversible defects where the original content distribution disappears; shows that the effect, referred to as
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Training-Free Method Mitigates LLM Hallucination Via Generation Constraints
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10/ Mitigating Hallucination via Generation Constraint – proposes a new training-free approach to mitigate hallucination in LLMs; they scaled the readout vector that constrains generation in a memory-augmented LLM decoder; recent works claim that LLMs with explicit memory