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DJ Sri Vigneshwar
Sri-Vigneshwar-DJ
AI & ML interests
Co-Founder, CTO @Hawky.ai - Creative Intelligence for Performance Marketing
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🔥 SWE-Pruner can save up to 40% of your Claude Code cost without sacrificing performance. Try it out!
📚 SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents (2601.16746)
💻 https://github.com/Ayanami1314/swe-pruner
Drawing inspiration from how human programmers “selectively skim” source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts.
📚 SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents (2601.16746)
💻 https://github.com/Ayanami1314/swe-pruner
Drawing inspiration from how human programmers “selectively skim” source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts.
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From Pointers to Footnotes: Why Agents will be “Reference-First”
https://medium.com/@di-zhang-fdu/from-pointers-to-footnotes-why-agents-will-be-reference-first-88d0e9730e37
https://medium.com/@di-zhang-fdu/from-pointers-to-footnotes-why-agents-will-be-reference-first-88d0e9730e37
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🏙️ Hugging Face Community Post
Title: 🧬 Experimenting with "Dynamic Chaos" in Tamil SLMs
Hi everyone! I just published a new experimental study on Small Language Model (SLM) resilience.
I took the Qwen2.5-0.5B model and put it through a "Chaos Phase" to see how much weight data a tiny model can lose before its understanding of classical Tamil grammar breaks.
Key highlights of the study:
Target Data: Fine-tuned on the Thirukkural (1,330 couplets + modern explanations).
The Chaos Step: Applied 20% random weight pruning but implemented "Layer Protection" for the Token Embeddings and LM Head to keep the characters readable.
Compression: 4-bit (Q4_K_M) quantization for extreme efficiency.
Result: A surrealist classical Tamil model that is ultra-light (~300MB) and ultra-fast!
Check out the model and the experiment logic here: Sri-Vigneshwar-DJ/qwen-tamil-chaos-v1
Title: 🧬 Experimenting with "Dynamic Chaos" in Tamil SLMs
Hi everyone! I just published a new experimental study on Small Language Model (SLM) resilience.
I took the Qwen2.5-0.5B model and put it through a "Chaos Phase" to see how much weight data a tiny model can lose before its understanding of classical Tamil grammar breaks.
Key highlights of the study:
Target Data: Fine-tuned on the Thirukkural (1,330 couplets + modern explanations).
The Chaos Step: Applied 20% random weight pruning but implemented "Layer Protection" for the Token Embeddings and LM Head to keep the characters readable.
Compression: 4-bit (Q4_K_M) quantization for extreme efficiency.
Result: A surrealist classical Tamil model that is ultra-light (~300MB) and ultra-fast!
Check out the model and the experiment logic here: Sri-Vigneshwar-DJ/qwen-tamil-chaos-v1
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Diffusers welcomes FLUX-2
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Now also with LTX-2 Detailer (Lightricks/LTX-2-19b-IC-LoRA-Detailer)
Try it out: alexnasa/ltx-2-TURBO
Try it out: alexnasa/ltx-2-TURBO