datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GLM-5.2-FP8-nemotron-codealpaca
GLM-5.2-FP8-nemotron-codealpaca
Training data for UCloud-org/GLM-5.2-FP8-DFlash,
a DFlash speculative-decoding drafter for
zai-org/GLM-5.2-FP8.
A mix of code / math / chat prompts from two public instruction datasets
(see Composition); all assistant responses are regenerated by GLM-5.2-FP8 so the targets match the
verifier's own output distribution — the data recipe specified in the
DFlash paper (Appendix A.1).
800,022 single-turn conversations, English-dominant
Generation:… See the full description on the dataset page: https://huggingface.co/datasets/JessieWei/GLM-5.2-FP8-nemotron-codealpaca.GLM-5.2-FP8-nemotron-codealpaca-thinking
GLM-5.2-FP8 Nemotron-CodeAlpaca Thinking Dataset
820,790 single-turn conversations generated by zai-org/GLM-5.2-FP8
with thinking enabled.
Prompt source
Rows (public)
Nemotron-Post-Training-Dataset-v2
800,944
CodeAlpaca-20k (corrected prompts, instruction + "\n\n" + input)
19,846
Total
820,790
Generation: temperature=1.0, top_p=0.95, max_tokens=24576, thinking
enabled. The CodeAlpaca prompts here include the input field.
Relationship to… See the full description on the dataset page: https://huggingface.co/datasets/JessieWei/GLM-5.2-FP8-nemotron-codealpaca-thinking.GLM-5.2-FP8-magpie-ultrachat
GLM-5.2-FP8 Regenerated Responses (Magpie + UltraChat mix)
A combined instruction-response dataset of 507,864 single-turn conversations. The
prompts are drawn from two public instruction datasets; the responses were freshly
regenerated with zai-org/GLM-5.2-FP8.
It was built as on-policy distillation data for training speculative-decoding drafts
(DFlash / DSpark) for GLM-5.2 — i.e. so the draft learns from GLM-5.2's own output
distribution — but it is a general-purpose GLM-5.2… See the full description on the dataset page: https://huggingface.co/datasets/mgoin/GLM-5.2-FP8-magpie-ultrachat.gsm8k-Llama-4-Maverick-17B-128E-Instruct-FP8
GSM8K with Llama-4-Maverick-17B-128E-Instruct-FP8 Responses
This dataset contains the GSM8K mathematical reasoning dataset with responses generated by Meta's Llama-4-Maverick-17B-128E-Instruct-FP8 model.
Dataset Structure
The dataset contains the following columns:
question: The original mathematical word problem
true_answer: The original correct answer with step-by-step solution
llama_answer: The model's generated response with step-by-step reasoning
true_final_answer:… See the full description on the dataset page: https://huggingface.co/datasets/PursuitOfDataScience/gsm8k-Llama-4-Maverick-17B-128E-Instruct-FP8.ClimateMBERT-syn-qwen3-30b-a3b-fp8-10k-seed42
ClimateMBERT Synthetic Qwen3 30B A3B FP8 10K Seed42
Synthetic continuation dataset generated from WxChat/ClimateMBERT_syn train split.
Source dataset: WxChat/ClimateMBERT_syn
Source split: train
Sampling: shuffled with random seed 42, ranks 0..9999
Rows: 10,000
Generator: Qwen/Qwen3-30B-A3B-Instruct-2507-FP8
Inference: vLLM on Clariden GH200 GPUs, tensor parallel size 2, non-eager mode
Max tokens: 4096
Generation config: temperature 0.7, top_p 0.8, top_k 20, min_p 0.0… See the full description on the dataset page: https://huggingface.co/datasets/JingweiNi/ClimateMBERT-syn-qwen3-30b-a3b-fp8-10k-seed42.opencode_reasoning2_hard_codeforces2000_pr03_qwen35_fp8_thinking_annotated_10k_seed20260513
Qwen3.5 FP8 Annotations for 10K K2-Think OCR2 Coding Steps
This dataset contains Qwen3.5 FP8 step-level correctness annotations for K2-Think reasoning traces on a hard Codeforces subset of OpenCodeReasoning-2.
Summary
Source trace dataset: opencode_reasoning2_hard_codeforces2000_pr03_k2_thinking_extracted_pilot10
Source rows: 10 hard coding problem traces
Candidate step rule: claim with non-empty aligned_token_ids
Candidate steps: 15,267
Manifest-selected annotated… See the full description on the dataset page: https://huggingface.co/datasets/JingweiNi/opencode_reasoning2_hard_codeforces2000_pr03_qwen35_fp8_thinking_annotated_10k_seed20260513.ClimateMBERT-syn-qwen35-122b-fp8-10k-seed42
ClimateMBERT Synthetic Qwen3.5 FP8 10K Seed42
Synthetic continuation dataset generated from WxChat/ClimateMBERT_syn train split.
Source dataset: WxChat/ClimateMBERT_syn
Source split: train
Sampling: shuffled with random seed 42, ranks 0..9999
Rows: 10,000
Generator: Qwen/Qwen3.5-122B-A10B-FP8
Inference: vLLM on Clariden GH200 GPUs, tensor parallel size 4, non-eager mode
Max tokens: 4096
No-thinking mode: chat_template_kwargs={"enable_thinking": false}
Generation config: temperature… See the full description on the dataset page: https://huggingface.co/datasets/JingweiNi/ClimateMBERT-syn-qwen35-122b-fp8-10k-seed42.browsecomp-plus-glm52-fp8-trie-event-replay
GLM-5.2 FP8 BrowseComp-Plus Trie Event Replay
This manually gated dataset contains a captured BrowseComp-Plus agent workload
served by GLM-5.2 FP8 on SGLang with CPU BM25 retrieval and evaluation
concurrency eight: all 830 benchmark queries, one closed terminal trajectory
each, recorded as Trie schema-v2 causal event streams and directly replayable
against any OpenAI-compatible inference endpoint.
Quick start: download, cd, replay
hf download… See the full description on the dataset page: https://huggingface.co/datasets/weili-0234/browsecomp-plus-glm52-fp8-trie-event-replay.ocr2_cf1900_k2_qwen35_fp8_10k_seed20260513
Qwen3.5 FP8 Annotations for 10K K2-Think OCR2 Coding Steps, Sample500
This dataset contains Qwen3.5 FP8 step-level correctness annotations for K2-Think reasoning traces on a diverse 500-problem hard Codeforces subset reconstructed from OpenCodeReasoning-2/code_contests.
Summary
Source trace dataset: opencode_reasoning2_hard_codeforces1900_pr10_reconq_k2_thinking_extracted_sample500_seed20260514
Source rows: 500 coding problem traces
Candidate step rule: claim with… See the full description on the dataset page: https://huggingface.co/datasets/JingweiNi/ocr2_cf1900_k2_qwen35_fp8_10k_seed20260513.