datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glm52-fidelity-nvfp4-nvidia-v1
fidelity--glm52.malaiwah.quant.nvfp4-nvidia
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from nvidia/GLM-5.2-NVFP4.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same cut… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm52-fidelity-nvfp4-nvidia-v1.glm-5.2-nvfp4-agentic-traces
GLM-5.2 NVFP4 agentic software traces
This snapshot contains 1,989 completed Verifiers invocation records generated
with RedHatAI/GLM-5.2-NVFP4-FP8.
manifest.jsonl is a compact index for filtering and inspection.
data/<arm>.jsonl contains the exact full graph records emitted by Verifiers.
configs/<arm>.toml contains the resolved configuration for each arm.
The snapshot retains successes, failures, truncations, and scoring metadata.
Use solved, reward, has_error, failure_labels… See the full description on the dataset page: https://huggingface.co/datasets/synquid/glm-5.2-nvfp4-agentic-traces.nvfp4-mtp-survey
Do Qwen3.8-27B NVFP4 repos actually ship a working MTP draft head?
A static survey of every NVFP4 quantization of Qwen3.8-27B and its finetunes that I could find
on the Hugging Face Hub, last run on 2026-08-24 (Rev 4) with
nvfp4_mtp_audit.py. Raw output: results.json.
I ran this to check a claim I had made in public, and the claim did not survive. The correction
is the first section, because it is the most important result here.
Revision history — read this, it is… See the full description on the dataset page: https://huggingface.co/datasets/windowsxp811203/nvfp4-mtp-survey.glm53-fidelity-nvfp4-inferact-v1
fidelity--glm53.malaiwah.quant.nvfp4-inferact
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from Inferact/GLM-5.3-NVFP4.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-nvfp4-inferact-v1.glm53-fidelity-nvfp4-radixark-v1
fidelity--glm53.malaiwah.quant.nvfp4-radixark
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from RadixArk/GLM-5.3-NVFP4.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-nvfp4-radixark-v1.glm53-fidelity-nvfp4-incoai-v1
fidelity--glm53.malaiwah.quant.nvfp4-incoai
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from incoai/GLM-5.3-NVFP4.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same cut… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-nvfp4-incoai-v1.nvfp4_test_9Btokens