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replied to csabakecskemeti's post 1 day ago

Testing Training on AMD/ROCm the first time! I've got my hands on an AMD Instinct MI100. It's about the same price used as a V100 but on paper has more TOPS (V100 14TOPS vs MI100 23TOPS) also the HBM has faster clock so the memory bandwidth is 1.2TB/s. For quantized inference it's a beast (MI50 was also surprisingly fast) For LORA training with this quick test I could not make the bnb config works so I'm running the FT on the fill size model. Will share all the install, setup and setting I've learned in a blog post, together with the cooling shroud 3D design.

reacted to mmhamdy's post with 🔥 3 days ago

🎉 We're excited to introduce MemoryCode, a novel synthetic dataset designed to rigorously evaluate LLMs' ability to track and execute coding instructions across multiple sessions. MemoryCode simulates realistic workplace scenarios where a mentee (the LLM) receives coding instructions from a mentor amidst a stream of both relevant and irrelevant information. 💡 But what makes MemoryCode unique?! The combination of the following: ✅ Multi-Session Dialogue Histories: MemoryCode consists of chronological sequences of dialogues between a mentor and a mentee, mirroring real-world interactions between coworkers. ✅ Interspersed Irrelevant Information: Critical instructions are deliberately interspersed with unrelated content, replicating the information overload common in office environments. ✅ Instruction Updates: Coding rules and conventions can be updated multiple times throughout the dialogue history, requiring LLMs to track and apply the most recent information. ✅ Prospective Memory: Unlike previous datasets that cue information retrieval, MemoryCode requires LLMs to spontaneously recall and apply relevant instructions without explicit prompts. ✅ Practical Task Execution: LLMs are evaluated on their ability to use the retrieved information to perform practical coding tasks, bridging the gap between information recall and real-world application. 📌 Our Findings 1️⃣ While even small models can handle isolated coding instructions, the performance of top-tier models like GPT-4o dramatically deteriorates when instructions are spread across multiple sessions. 2️⃣ This performance drop isn't simply due to the length of the context. Our analysis indicates that LLMs struggle to reason compositionally over sequences of instructions and updates. They have difficulty keeping track of which instructions are current and how to apply them. 🔗 Paper: https://huggingface.co/papers/2502.13791 📦 Code: https://github.com/for-ai/MemoryCode

new activity 4 days ago

gaia-benchmark/leaderboard:Unable to Load GAIA benchmark leaderboard

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