glm-5.1
Datasets
All datasets matching “glm-5.1”GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversations, input, output, domain, and meta fields.
This release was prepared from the original dataset published by Kassadin88.
Summary
Teacher model in the data: GLM-5.1… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GLM-5.1-Reasoning-1M-Cleaned.swe-rebench-v2-glm-5.1-pi-agent-successful-traces
Agentic coding trajectories of GLM-5.1
We collected 7,777 successful multi-turn trajectories by running
GLM-5.1 with the
PI agent on
nebius/SWE-rebench-V2.
Configuration
Each SWE-rebench-V2 task is an issue of a public repository. An agent has to
implement a fix that is then validated by unit tests.
The model was run on 3,837 issues from Python repositories, with 4 rollouts
per issue. The task list is available as the train split of… See the full description on the dataset page: https://huggingface.co/datasets/whitecircle/swe-rebench-v2-glm-5.1-pi-agent-successful-traces.GLM-5.1-Reasoning-Main-Sharded
GLM-5.1 reasoning main — sequential shards
Byte-preserving 100 MB JSONL shards of the main subset from Jackrong/GLM-5.1-Reasoning-1M-Cleaned, by Jackrong, derived upstream from Kassadin88/GLM-5.1-1000000x. All credit for the original data and cleaning belongs to those publishers.
Only main.jsonl is included. No filtering, shuffling, schema changes, tokenization or truncation. Original JSON fields and complete records are preserved. Shards retain upstream order; random shard… See the full description on the dataset page: https://huggingface.co/datasets/placeholderlabs/GLM-5.1-Reasoning-Main-Sharded.GLM-5.1-Reasoning
OctoMed/GLM-5.1-Reasoning
Single-turn instruction-following examples with explicit chain-of-thought reasoning,
converted to OctoMed format for SFT training.
Source
Derived from Jackrong/GLM-5.1-Reasoning-1M-Cleaned
by Jackrong. All credit for the original data collection,
distillation from GLM-5.1, and cleaning goes to the original authors.
Format
Each example contains:
question: the instruction / question text (from input field)
responses: the full model… See the full description on the dataset page: https://huggingface.co/datasets/OctoMed/GLM-5.1-Reasoning.GLM-5.1-1000000x
GLM-5.1-1000000x
1,003,589 reasoning traces distilled by GLM-5.1, using questions from KIMI-K2.5-1000000x.
Each entry contains a full chain-of-thought reasoning trace followed by the final answer, generated by GLM-5.1.
Complete! All 1,003,589 prompts distilled successfully.
████████████████████████████████ 100%
Data Distribution
Subset
Count
Proportion
Est. Tokens
Domain
main
598,366
59.6%
~3.04B
General reasoning & instruction-following
Math… See the full description on the dataset page: https://huggingface.co/datasets/clzoro/GLM-5.1-1000000x.combined-reasoning-opus-4.6-opus-4.7-kimi-k2.5-kimi-k2.6-glm-5.1
Combined Reasoning Distill — Multi-Model
A large-scale unified reasoning dataset combining thinking and chain-of-thought traces distilled from frontier models, normalized into a single consistent schema for fine-tuning. Includes data from Claude (Opus 4.5/4.6/4.7, Sonnet 4.5/4.6, Haiku 4.5), GPT (5.1/5.2), Gemini 3 Pro Preview, Kimi (K2/K2.5/K2.6), GLM (4.6/4.7/5.1), MiniMax M2.1, Grok Code Fast 1, and more.
Schema
Every row has a single field:
Field
Type… See the full description on the dataset page: https://huggingface.co/datasets/Avtrkrb/combined-reasoning-opus-4.6-opus-4.7-kimi-k2.5-kimi-k2.6-glm-5.1.