FLAMIA accelerates Transformer attention on NVIDIA Blackwell GPUs via MXFP8 and mixed-precision kernels that reduce quantization overhead, prevent causal leakage, and support variable-length packed ...

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

Image Classification model with 93.2M parameters. Download, explore files and run it on Hugging Face.

A Metalearned Neural Circuit for Nonparametric Bayesian Inference Most applications of machine learning to classification assume a closed set of balanced classes. This is at odds with the real world, ...

How to use Launch80/Qwopus3.8-27B-Coder-Flash-Int4-baked with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline ...

Welcome to Pico LM 👋, a research initiative dedicated to demystifying language model learning. We create two complementary frameworks (pico-train and pico-analyze) for training and analyzing small to ...

c_attachment_tables.md — table 1: the 12 text-only prompts and each arm's reply; table 2: files and environment; table 3: delegation lines. x12_prompts.json — the 12 prompts (a passage about a living ...

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How to use nytopop/Qwen3-14B-abliterated.w8a8 with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text ...

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "azampatti/Qwen3.8-Flash-Next-125B-A5B-INT4-AutoRound" # Call the server using curl ...

Standard abliteration methods often require dequantizing and re-quantizing model weights, which degrades the specialized Gumbel-Softmax Quantization (GSQ-RCO) calibration used by ISTA-DASLab.