TokenBender/glm47-flash-pie-cpp-lora-r16-sft-h100
Text Generation • Updated • 18 • 1
Datasets:
GLM-4.7-Flash PIE C++ Post-Training Data
The exact prepared dataset used for the GLM-4.7-Flash C++ performance post-training runs.
Splits
| File | Rows | Purpose |
|---|---|---|
sft/train.jsonl |
7,864 | Supervised fine-tuning |
grpo/train.jsonl |
7,887 | GRPO prompt and reward evaluation |
eval/validation.jsonl |
1,259 | Full held-out evaluation |
eval/validation_mini126.jsonl |
126 | Fast evaluation |
eval/validation_mini4.jsonl |
4 | Smoke evaluation |
tasks.tar.gz |
9,146 task JSONs | Reward scoring and evaluation harness inputs |
Each row carries a stable task ID, problem ID, split, prompt, label, and task
metadata. SFT rows additionally contain the user and assistant messages used by
the trainer. The task archive contains every tasks/... path referenced by the
training and evaluation rows. The canonical repository downloader verifies and
extracts it into data/tasks.
Preparation
The source tasks were filtered by compiling and executing the oracle answer in
the C++ sandbox. Training rows were retained when the response format was valid,
all tests passed, and the reward result was correct.
The source preparation run scored 8,350 training tasks, retained 7,887, and
rejected 463. The complete preparation receipt is in manifest.json.
Provenance
This is a prepared derivative of the PIE C++ performance dataset, which is based on IBM Project CodeNet. Use and redistribution are subject to the applicable upstream dataset terms.
Training code: TokenBender/browser-is-all-you-need
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