quantization
qwen36-27b-gguf-bfcl-v4-quantization-pilot-corrected-v3
Qwen3.6-27B GGUF quantization on a bounded BFCL V4 pilot
Q4_K_M matched Q8_0 on both tested categories: each scored 94 of 100 selected cases correct. Q5_K_M also scored 94/100; Q3_K_M scored 92/100.
Read the results page · Inspect all 400 scored rows
This is a post-result-corrected exploratory analysis of two selected non-live BFCL V4 categories, not a full leaderboard result.
Inspect the scored rows without cloning
The Hub Dataset Viewer does not render this… See the full description on the dataset page: https://huggingface.co/datasets/CyberNative-AI/qwen36-27b-gguf-bfcl-v4-quantization-pilot-corrected-v3.tessera-quantization-research-evidence
Tessera Quantization Research Evidence
This dataset is the primary-source measurement evidence from an ongoing research
program studying calibrated low-bit quantization (ternary, int4, vector-quantized
codebooks) for LLM inference on heterogeneous AMD hardware (RDNA3 iGPU, XDNA1/2
NPU, Zen 4/5 CPU). The work is done in a fork of llama.cpp (project name
"Tessera") that adds calibrated per-tensor ternary/payload4/VQ quantization,
NPU offload, and RDNA3-native GPU kernels.
This is… See the full description on the dataset page: https://huggingface.co/datasets/Tribunus-dev/tessera-quantization-research-evidence.hemmingway-1-omlx-quantization-evidence-v2
Hemmingway-1 Quantization Evidence v2
This package records two local evidence lanes for the Hemmingway-1 oQ4e build: teacher-forced numerical fidelity against a BF16 reference, and controlled runtime telemetry on Apple Silicon. It complements the frozen blind-preference study in Hemmingway-1 oMLX Quantization Benchmark v1.
This dataset is sixstringzen/hemmingway-1-omlx-quantization-evidence-v2. The quality dataset remains unchanged because blind preference, distribution fidelity… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-evidence-v2.daily-paper-2026-09-17-tool-call-quantization-cliff
The Tool-Call Cliff: Measuring the Accuracy Decay of Agentic Structured Output Under Low-Bit Quantization in Self-Hosted H200 Serving
TL;DR — We formalize the tool-call cliff - the hypothesis that agentic structured tool calls decay faster than free-form prose under NVFP4/8-bit quantization - as an accuracy tax and a cliff ratio against a free-form control, derive two falsifiable predictions (a per-category failure-mode composition and a superlinear 8-to-4-bit tax jump), and… See the full description on the dataset page: https://huggingface.co/datasets/thaki-AI/daily-paper-2026-09-17-tool-call-quantization-cliff.hemmingway-1-omlx-quantization-benchmark-v1
Hemmingway-1 oMLX Quantization Benchmark
This is the public-safe benchmark package for the Hemmingway-1 oMLX
quantization study on Apple Silicon.
Altworld developed and published
Hemmingway-1. Bobby Pierce
published these quantizations and the evaluation package. The
collection
links the upstream model and all six builds.
Analysis revision 2, corrected on 2026-09-22, fixes A/B attribution and matching
across reversed packets. Read CORRECTION.md before using the
aggregate… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-benchmark-v1.gauge-orbit-quantization-results
Gauge equivalence does not survive quantisation: sweeps and results
Raw sweeps, end-to-end vectors and reading tables behind Gauge
equivalence does not survive quantisation: value-output orbit,
norm-product bound, and consequences for rotational methods (Manuel
Muñoz Plá, 2026, v2.1).
The layout mirrors the code repository, so this dataset can be dropped
over a clone of it and every reading script runs unchanged:
https://github.com/mmunozpl/gauge-orbit-quantization (tag v2.1).… See the full description on the dataset page: https://huggingface.co/datasets/ManPla/gauge-orbit-quantization-results.