Instructions to use Terrano09/generalized-cpp-kernel-GRPO20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Terrano09/generalized-cpp-kernel-GRPO20 with PEFT:
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| base_model: zai-org/GLM-4.7-Flash | |
| library_name: peft | |
| tags: | |
| - lora | |
| - grpo | |
| - code | |
| - cpp | |
| # Generalized C++ kernel GRPO20 | |
| Portable PEFT LoRA checkpoints from `generalized-cpp-kernel-grpo20-spot-20260829-083214-retry1`. | |
| ## Selected checkpoint | |
| The selected release checkpoint is `iter_0000014`. Selection basis: higher four-trial Fixed26 Pass@1 and turn-2 means than iter 19. | |
| | Field | Value | | |
| | --- | --- | | |
| | Base model | `zai-org/GLM-4.7-Flash@7dd20894a642a0aa287e9827cb1a1f7f91386b67` | | |
| | Adapter SHA-256 | `b4bb3a250e28696c597d84db459caa75978e160996818dbfce22b8896b2c794b` | | |
| | LoRA rank / alpha | 16 / 32 | | |
| | Target modules | `q_a_proj`, `kv_a_proj_with_mqa`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` | | |
| | Planned updates | 20 | | |
| | Run outcome | Retry completed all 20 updates and preserved checkpoints through iter 19. | | |
| | Training data | `Generalized_CPP_GRPO20_train.jsonl`, 6 rows | | |
| | Training-data SHA-256 | `566ea43aad4f6419b345e0295288c27b374575ce4db30cbd2d6b7510bf86ffbe` | | |
| | Training-manifest SHA-256 | `25d6165985c440e0bc79f3a3f2a6aeeac4943324f2289bc03fd1d775d30bf3a6` | | |
| ## Post-training evaluations | |
| Each row uses only its selected best four receipt-verified trials (26 tasks per trial, 104 task evaluations). Iterations are reported separately. | |
| | Checkpoint | Pass@1 trial scores | Pass@1 mean | Turn-2 trial scores | Turn-2 mean | | |
| | --- | --- | ---: | --- | ---: | | |
| | `iter_0000014` | 13, 13, 11, 10 | 11.75/26 | 17, 17, 14, 16 | 16/26 | | |
| | `iter_0000019` | 12, 10, 10, 9 | 10.25/26 | 17, 13, 13, 11 | 13.5/26 | | |
| | Checkpoint | Pass@1 SD; range; task-bootstrap 95% CI (out of 26) | Turn-2 SD; range; task-bootstrap 95% CI (out of 26) | Conditional turn-2 recovery | | |
| | --- | --- | --- | --- | | |
| | `iter_0000014` | 1.50; 10-13; 8-15.5 | 1.41; 14-17; 11.75-20 | 17/57 (29.8%; CI 14.5-48.9%) | | |
| | `iter_0000019` | 1.26; 9-12; 6.25-14.5 | 2.52; 11-17; 9.25-17.75 | 13/63 (20.6%; CI 9.5-35.0%) | | |
| Evaluation used `fixed26-contract-v2`, thinking enabled, temperature 0.7, top-p 1.0, and a 32,768-token response limit. The complete selected run IDs and byte-for-byte receipts are under `evaluations/`. | |
| ## Training data | |
| 6 executable shadow-task rows: clock, complex-numbers, crypto-square, grade-school, kindergarten-garden, and perfect-numbers. The manifest explicitly records overlap with six Fixed26 task IDs; reference answers are not packaged. | |
| The exact JSONL and its source manifest are included at the repository root. Their hashes are checked during release construction. | |
| ## Checkpoints | |
| Every checkpoint directory contains only the two portable inference artifacts: `adapter_config.json` and `adapter_model.bin`. Megatron tensor-parallel shards, optimizer state, and other training-only files are intentionally omitted. | |
| | Checkpoint | Adapter SHA-256 | | |
| | --- | --- | | |
| | `iter_0000004` | `cf25cdb386b4e9cbc372480768882dc9f828b5b68e9017868c0255945d30aec6` | | |
| | `iter_0000009` | `83eec62a120142917902355a641043cf1970658170056b6fce5ebf98dd1de7dd` | | |
| | `iter_0000014` | `b4bb3a250e28696c597d84db459caa75978e160996818dbfce22b8896b2c794b` | | |
| | `iter_0000019` | `e915a82a09a290cd4a6b587fe08fdad8acbbf58dea27e1a53e9e77f9effdf300` | | |
| ## Loading | |
| ```python | |
| from peft import PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| base = "zai-org/GLM-4.7-Flash" | |
| checkpoint = "Terrano09/generalized-cpp-kernel-GRPO20" | |
| subfolder = "checkpoints/iter_0000014/adapter" | |
| tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True) | |
| model = PeftModel.from_pretrained(model, checkpoint, subfolder=subfolder) | |
| ``` | |
| ## Reproduction and evidence | |
| The release includes the exact training JSONL and manifest plus four aggregate receipts and eight shard receipts for each reported evaluation row. Checksum files bind each evidence bundle. | |
| These are assisted Fixed26 regression results using selected best-four cohorts, not pristine held-out benchmark claims. The Generalized C++ dataset, where applicable, explicitly overlaps six Fixed26 task IDs; consult its included manifest before comparing results. | |