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Dipankar Sarkar PRO

dipankarsarkar
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https://www.dipankar.cc

AI & ML interests

Building the AI-native stack. Agents as infrastructure, safety as architecture, performance as plumbing. I publish the receipts: papers, datasets, demos.

Recent Activity

reacted to satgeze's post with 🤗 about 1 hour ago
https://huggingface.co/satgeze/Ornith-1.0-35B-1M-GGUF Ornith-1.0 with a 1,048,576-token context window, tested instead of claimed 🦜 Ornith is Qwen3.5-family under the hood, so YaRN factor 4 extends it from 262K native to exactly 1M. I baked that into the GGUF metadata (no fine-tuning, weights bit-identical) so llama.cpp and Ollama apply it with zero flags, then ran full needle-in-a-haystack ladders on my own hardware: - satgeze/Ornith-1.0-35B-1M-GGUF: 10/10 needles at every rung from 32K through 1M, replicated with fresh seeds (M3 Max 128GB, ~6.8h cold 1M prefill) - satgeze/Ornith-1.0-9B-1M-GGUF: perfect through 524K, honest 7/10 at 1M under Q4 + q8_0 KV, failure band charted in the card - satgeze/Ornith-1.0-397B-1M-GGUF: IQ1_M through Q4_K_M as split GGUFs, coherence-gated Also in the repos: - Vision: Ornith kept the Qwen3.5 multimodal skeleton, so the VL vision tower (extracted by bartowski) attaches at runtime via llama-server --mmproj. OCR-tested on the 9B and 35B, mmproj files bundled. - A measured residency matrix: on a single RTX 5090, every 9B quant up to Q6_K holds the full 1M window at 100 percent GPU, 162 to 244 tok/s. - Quality gates: every low-bit quant passed a coherence test before upload. The 35B IQ1_S failed and was deleted rather than shipped. Harness, method writeup, and raw per-needle data: https://github.com/satindergrewal/ornith-1m All MIT. Credit to DeepReinforce for the models and bartowski for the imatrix quants and vision towers. If a config breaks retrieval for you, tell me and it goes in the card.
upvoted a paper about 1 hour ago
Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR
reacted to pranavupadhyaya52's post with 🤗 about 2 hours ago
After several weeks of experimenting, debugging, and iterating, I am excited to share WikiSmartBotLM. WikiSmartBotLM is a compact decoder only language model built from the ground up as an educational and practical project. The goal was not simply to train another language model, but to create one that is easy to understand, modify, and experiment with while following many of the architectural ideas used in modern LLMs. The model is built on a custom Transformer architecture featuring Rotary Positional Embeddings, RMSNorm, SwiGLU feed forward layers, grouped query attention, and an efficient autoregressive decoder optimized for local inference. The repository includes the complete model implementation, configuration files, tokenizer integration, training pipeline, inference scripts, checkpoint conversion utilities, and examples that demonstrate how each component works together. Whether you want to understand the forward pass, train your own model, or build applications on top of WikiSmartBotLM, everything is designed to be approachable. You can directly run the model via the models Huggingfaces space, which I've included in the post. Model Repository: https://huggingface.co/pranavupadhyaya52/Wiki-SmartBotLM-Instruct I hope WikiSmartBotLM becomes a useful resource for anyone who enjoys learning by building. Feedback, issues, feature requests, and contributions are always welcome. https://huggingface.co/spaces/pranavupadhyaya52/WikiSmartBot
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