Diffusion Language Models combining deep narrow networks, Canon layers (depthwise causal convolutions), and WSD (Warmup-Stable-Decay) training.
Asankhaya Sharma
codelion
AI & ML interests
Creator of OptiLLM, OpenEvolve, Adaptive Classifier, and Ellora. Pioneering a new category in AI infrastructure: inference-time compute for LLMs.
Recent Activity
reacted
to
their
post
with ❤️
about 16 hours ago
Reverse Engineering a $500M Mystery: From HashHop to Memory-Augmented Language Models
I wrote a deep dive into how Magic AI's 100M token context window might work, starting from their HashHop benchmark and building up to MALM - a Memory-Augmented Language Model.
Key insight: treating each key as a single token enables perfect retrieval at unlimited context lengths.
The article covers:
- How HashHop works and why its perfect accuracy is suspicious
- Building a tokenized solver that achieves 100% accuracy
- Scaling to MALM for real code search tasks
- Why this approach could handle 100M+ tokens
Read the full article: https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop
Try the model: https://huggingface.co/codelion/malm-165m
Code: https://github.com/codelion/hash-hop
reacted
to
their
post
with 👍
about 16 hours ago
Reverse Engineering a $500M Mystery: From HashHop to Memory-Augmented Language Models
I wrote a deep dive into how Magic AI's 100M token context window might work, starting from their HashHop benchmark and building up to MALM - a Memory-Augmented Language Model.
Key insight: treating each key as a single token enables perfect retrieval at unlimited context lengths.
The article covers:
- How HashHop works and why its perfect accuracy is suspicious
- Building a tokenized solver that achieves 100% accuracy
- Scaling to MALM for real code search tasks
- Why this approach could handle 100M+ tokens
Read the full article: https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop
Try the model: https://huggingface.co/codelion/malm-165m
Code: https://github.com/codelion/hash-hop
reacted
to
their
post
with 🚀
about 16 hours ago
Reverse Engineering a $500M Mystery: From HashHop to Memory-Augmented Language Models
I wrote a deep dive into how Magic AI's 100M token context window might work, starting from their HashHop benchmark and building up to MALM - a Memory-Augmented Language Model.
Key insight: treating each key as a single token enables perfect retrieval at unlimited context lengths.
The article covers:
- How HashHop works and why its perfect accuracy is suspicious
- Building a tokenized solver that achieves 100% accuracy
- Scaling to MALM for real code search tasks
- Why this approach could handle 100M+ tokens
Read the full article: https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop
Try the model: https://huggingface.co/codelion/malm-165m
Code: https://github.com/codelion/hash-hop
Organizations
Ellora: Enhancing LLMs with LoRA - Standardized Recipes for Capability Enhancement
Internal Coherence Maximization (ICM): A Label-Free, Unsupervised Training Framework for LLMs
A collection of pre-training datasets samples of sizes 10M, 100M and 1B tokens. Ideal for use in quick experimentation and ablations.
Pivotal Token Search (PTS) identifies tokens in a language model's generation that significantly impact the probability of success
All models, datasets and tools related to https://securade.ai
Diffusion Language Models combining deep narrow networks, Canon layers (depthwise causal convolutions), and WSD (Warmup-Stable-Decay) training.
A collection of pre-training datasets samples of sizes 10M, 100M and 1B tokens. Ideal for use in quick experimentation and ablations.
Ellora: Enhancing LLMs with LoRA - Standardized Recipes for Capability Enhancement
Pivotal Token Search (PTS) identifies tokens in a language model's generation that significantly impact the probability of success
Internal Coherence Maximization (ICM): A Label-Free, Unsupervised Training Framework for LLMs
All models, datasets and tools related to https://securade.ai