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Our findings show that while real-world datasets exhibit high varsortability, their R2-sortability is low. This suggests that the scale of data can hold crucial causal information, challenging existing assumptions in the field. (3/4)

[arXivly @arXivly]

Can causal discovery algorithms crack the code of time series data? Our latest paper investigates how var- and R2-sortability impact these methods, revealing surprising insights! Dive in: https://t.co/VykL6KT0WX (1/4)

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We explored the performance of causal discovery algorithms on various datasets, including SVAR models, Erdős-Rényi graphs, climate data, and real-world river flows. The results highlight significant differences in sortability. (2/4)

[arXivly @arXivly]

This advancement opens doors for more efficient code search, understanding, and generation across entire repositories. IBM's work pushes the boundaries of what's possible in AI-assisted software development. Exciting times ahead! 🌟🔮 (4/4)

[arXivly @arXivly]

🚀 Breakthrough in AI: IBM's Granite Code Models now handle 128K tokens! This game-changer allows AI to process entire codebases, revolutionizing software development. But how did they do it? 🤔 (1/4)

[arXivly @arXivly]

Results are impressive! On long-context tasks like RepoQA, the new models outperform their predecessors by a whopping 60%+. And the best part? They maintain performance on short-context tasks like HumanEval. 📈🎯 (3/4)

[arXivly @arXivly]

assistance for complex software projects 6️⃣ Open source goodness: • Released under Apache 2.0 license • Both base and instruction-tuned models available • 3B and 8B parameter versions This could be a game-changer for how we interact with large codebases. Imagine an AI

[arXivly @arXivly]

during training • Multi-turn instruction data created from repo-level documents • Balanced mix of short and long-context data for fine-tuning 5️⃣ Real-world impact: • Better understanding of entire codebases • Improved code search and comprehension • More effective AI

[arXivly @arXivly]

are impressive: • Crushes long-context tasks like code completion and retrieval • Maintains strong performance on short-context benchmarks • Outperforms previous models on RepoQA, a tough code understanding test 4️⃣ Key innovations: • Gradual increase of RoPE base frequency

[arXivly @arXivly]

hampering their use in real-world software development. 2️⃣ Solution: IBM's team developed a clever scaling approach: • Lightweight continual pretraining • Repository-level file packing with semantic ordering • Synthetic long-context instruction data generation 3️⃣ Results

[arXivly @arXivly]

entire project's context! 🤯 Time to supercharge your coding workflow with some long-context AI power! 💻✨ Read the full paper for all the technical details. It's a fascinating deep dive into scaling language models for code intelligence.

[arXivly @arXivly]

Multi-turn instruction data created from repo-level documents • Balanced mix of short and long-context data for fine-tuning 5️⃣ Real-world impact: • Better understanding of entire codebases • Improved code search and comprehension • More effective AI assistance for complex

[arXivly @arXivly]

• Crushes long-context tasks like code completion and retrieval • Maintains strong performance on short-context benchmarks • Outperforms previous models on RepoQA, a tough code understanding test 4️⃣ Key innovations: • Gradual increase of RoPE base frequency during training •

[arXivly @arXivly]

their use in real-world software development. 2️⃣ Solution: IBM's team developed a clever scaling approach: • Lightweight continual pretraining • Repository-level file packing with semantic ordering • Synthetic long-context instruction data generation 3️⃣ Results are impressive:

[arXivly @arXivly]

🚀 IBM just dropped a game-changer for coders! Their new Granite Code Models can handle a whopping 128K tokens of context.

Read the paper here:
https://t.co/hQmn9D0QWM

[arXivly @arXivly]

🚀 IBM just dropped a game-changer for coders! Their new Granite Code Models can handle a whopping 128K tokens of context.

Read the paper here:
https://t.co/hQmn9D0QWM

[arXivly @arXivly]

🚀 IBM just dropped a game-changer for coders! Their new Granite Code Models can handle a whopping 128K tokens of context.

Read the paper here:
https://t.co/XFYPZWs9K1

[arXivly @arXivly]

🚀 IBM just dropped a game-changer for coders! Their new Granite Code Models can handle a whopping 128K tokens of context. How'd they do it?
1️⃣ Lightweight continual pretraining
2️⃣ Repository-level file packing
3️⃣ Synthetic long-context instruction data
https://t.co/XFYPZWs9K1

[arXivly @arXivly]

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