datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GLM-5.2-AgentThis dataset was generated using teich by TeichAI
GLM-5.2 Agent traces
This directory contains raw agent trace files generated by teich.
JSONL files: 319
Model metadata: glm-5.2
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.
Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP… See the full description on the dataset page: https://huggingface.co/datasets/AletheiaResearch/GLM-5.2-Agent.GLM-5.3-Flash-calibration-activations-v1
GLM-5.3-Flash calibration activations v1 (BF16, natural routing)
Per-layer block-input activations of zai-org/GLM-5.3-Flash-BF16 @ b1967181 over 92x2048
tokens of the exllamav3 standard_cal_data corpus (pinned): per context, layer_NNN.attn_in
and layer_NNN.mlp_in (bf16, post-norm linear inputs; mlp_in is the router + expert gate/up
input) and layer_NNN.router_logits (fp32, natural top-8 routing ground truth).
Per-expert Hessians E[xx^T], routing statistics and down-proj inputs… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-calibration-activations-v1.glm53-flash-fidelity-root-v1
fidelity--glm53flash.malaiwah.root.bf16
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from zai-org/GLM-5.3-Flash-BF16.
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-flash-fidelity-root-v1.glm53-fidelity-exl3-wrld-k4-v1
fidelity--glm53.malaiwah.quant.exl3-wrld-k4
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from wrldsuksgo2mars/GLM-5.3-EXL3-K4-v1.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-exl3-wrld-k4-v1.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.glm53-flash-fidelity-exl3-tr3-6bpw-v1
fidelity--glm53flash.malaiwah.quant.tr3-6bpw
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/GLM-5.3-Flash-TR3-6bpw.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-exl3-tr3-6bpw-v1.glm53-flash-fidelity-fp8-v1
fidelity--glm53flash.malaiwah.quant.official-fp8
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from zai-org/GLM-5.3-Flash.
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).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-fp8-v1.carnice-glm5-hermes-traces
Carnice GLM-5 Hermes Traces
This dataset is a merged release bundle of GLM-5 traces collected through the Hermes Agent harness.
It was generated by running the carnice_trace_prompt_bank_v4 prompt bank through Hermes Agent with:
z-ai/glm-5 via OpenRouter
local/file/terminal/code-execution tools for local tasks
Hermes browser tools plus Tavily-backed web_search / web_extract for web tasks
isolated disposable workspaces per prompt
This release is prepared for Hugging Face upload and… See the full description on the dataset page: https://huggingface.co/datasets/kai-os/carnice-glm5-hermes-traces.glm-5.2-kernelgym-rollouts
GLM-5.2 KernelGym Rollouts
This dataset contains 3,200 feedback-driven GPU-kernel optimization trajectories
generated by zai-org/GLM-5.2-FP8: 100 validation tasks, two backends (inline
CUDA and Triton), and 16 rollouts per task.
Each trajectory retains the prompt/feedback message history, model responses and
reasoning, extracted kernel code, KernelGym compilation and correctness results,
profiling metadata, token usage, and stopping decision. Every published record
ended with… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/glm-5.2-kernelgym-rollouts.glm52-fidelity-root-v1
fidelity--glm52.malaiwah.root.bf16
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from zai-org/GLM-5.2.
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 as… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm52-fidelity-root-v1.glm53-fidelity-exl3-tr3-3.42bpw-v1
fidelity--glm53.malaiwah.quant.exl3-tr3-342
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from davidsyoung/GLM-5.3-EXL3-TR3-3.42bpw.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-exl3-tr3-3.42bpw-v1.glm53-fidelity-exl3-tr3-3.0bpw-v1
fidelity--glm53.malaiwah.quant.exl3-tr3-30
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from davidsyoung/GLM-5.3-EXL3-TR3-3.0bpw.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-exl3-tr3-3.0bpw-v1.glm5-next-tiny-fidelity-root-v1
glm5_next random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/glm5-next-tiny-random-bf16.
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/glm5-next-tiny-fidelity-root-v1.glm53-fidelity-exl3-tr3-3.25bpw-v1
fidelity--glm53.malaiwah.quant.exl3-tr3-325
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from davidsyoung/GLM-5.3-EXL3-TR3-3.25bpw.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-exl3-tr3-3.25bpw-v1.glm53-fixture-0.1B-fidelity-quant-int4-v1
GLM-5.3-Flash-0.1B fixture — candidate fidelity dataset, toy RTN-int4 routed experts (hidden form)
The numbers in this dataset are meaningless as quantization quality.
The weights are random (inference-optimization/GLM-5.3-Flash-0.1B-A0.1B is an
architectural fixture), and the quantizer is deliberately crude. This exists so
that step 3 of the three-step fidelity architecture has two real datasets to
compare, and so that anyone can see what a candidate capture looks like
next to… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fixture-0.1B-fidelity-quant-int4-v1.glm52-fidelity-exl3-tr3-3.42bpw-willfalco-v1
fidelity--glm52.malaiwah.quant.exl3-tr3-3.42bpw-willfalco
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from willfalco/GLM-5.2-EXL3-TR3-3.42bpw.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm52-fidelity-exl3-tr3-3.42bpw-willfalco-v1.glm53-fixture-0.1B-fidelity-root-v1
GLM-5.3-Flash-0.1B fixture — root fidelity dataset (hidden form)
The numbers in this dataset are meaningless as model quality.
The weights it was captured from are random. inference-optimization/GLM-5.3-Flash-0.1B-A0.1B
is an architectural fixture: it has GLM-5.3-Flash's config shape and its exact
154,880-token vocabulary, and none of its training. Nothing here says anything
about GLM-5.3-Flash, about quantization quality, or about any model.
What it is for: being a small… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fixture-0.1B-fidelity-root-v1.glm52-fidelity-exl3-tr3-3.0bpw-brandonmusic-v1
fidelity--glm52.malaiwah.quant.exl3-tr3-3.0bpw-brandonmusic
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm52-fidelity-exl3-tr3-3.0bpw-brandonmusic-v1.glm53-flash-fidelity-exl3-tr3-4bpw-miaailab-v1
fidelity--glm53flash.malaiwah.quant.miaailab-4bpw
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from Mia-AiLab/GLM-5.3-Flash-EXL3-TR3-4bpw.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-exl3-tr3-4bpw-miaailab-v1.glm53-fidelity-exl3-drowzeys-v1
fidelity--glm53.malaiwah.quant.exl3-drowzeys
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from drowzeys/keys-GLM-5.3-EXL3.
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).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-exl3-drowzeys-v1.glm52-fidelity-exl3-tr3v4-3.5bpw-mtp78-brandonmusic-v1
fidelity--glm52.malaiwah.quant.exl3-tr3v4-3.5bpw-mtp78-brandonmusic
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from brandonmusic/GLM-5.2-EXL3-TR3v4-3.5bpw-MTP78.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm52-fidelity-exl3-tr3v4-3.5bpw-mtp78-brandonmusic-v1.glm52-fidelity-exl3-tr3-3.40bpw-jpsequeira-v1
fidelity--glm52.malaiwah.quant.exl3-tr3-3.40bpw-jpsequeira
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from jpsequeira/GLM-5.2-EXL3-TR3-3.40bpw-KVarN-K4V2.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm52-fidelity-exl3-tr3-3.40bpw-jpsequeira-v1.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-gguf-unsloth-udq4kxl-v1
fidelity--glm53.malaiwah.quant.gguf-unsloth-udq4kxl
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from unsloth/GLM-5.3-GGUF.
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).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-gguf-unsloth-udq4kxl-v1.glm52-fidelity-exl3-tr3-3.25bpw-willfalco-v1
fidelity--glm52.malaiwah.quant.exl3-tr3-3.25bpw-willfalco
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from willfalco/GLM-5.2-EXL3-TR3-3.25bpw.
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… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm52-fidelity-exl3-tr3-3.25bpw-willfalco-v1.glm51-kld-reference-logits-wikitext-ctx2048-s512-20260517 ---
license: other
pretty_name: GLM-5.1 KLD Reference Logits WikiText ctx2048 s512
tags:
- logits
- kld
- glm-5.1
- vllm
- b12x
---
# GLM-5.1 KLD Reference Logits
Public cache of the reference logits used for GLM-5.1 NVFP4 / mixed
FP8_PB_WO KLD evaluation. These files are generated logits, not model
weights. They are stored as `logits_*.safetensors` with one tensor named
`logits`, shape `(2047, 154880)`, dtype `float32`.
##… See the full description on the dataset page: https://huggingface.co/datasets/festr2/glm51-kld-reference-logits-wikitext-ctx2048-s512-20260517.carnice-glm5-hermes-traces
Carnice GLM-5 Hermes Traces
This dataset is a merged release bundle of GLM-5 traces collected through the Hermes Agent harness.
It was generated by running the carnice_trace_prompt_bank_v4 prompt bank through Hermes Agent with:
z-ai/glm-5 via OpenRouter
local/file/terminal/code-execution tools for local tasks
Hermes browser tools plus Tavily-backed web_search / web_extract for web tasks
isolated disposable workspaces per prompt
This release is prepared for Hugging Face upload and… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/carnice-glm5-hermes-traces.glm53-fidelity-fp8-v1
fidelity--glm53.malaiwah.quant.fp8
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from zai-org/GLM-5.3.
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 as… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-fp8-v1.