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Terrano09/midband-ten-kernel12-set2-GRPO30

sourceHugging Faceupdated 1mo agoView on Hugging Face

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Midband Ten kernel12 Set2 GRPO30

Portable PEFT LoRA checkpoints from midband-ten-kernel12-set2-grpo30-spot-20260828-143019.

Selected checkpoint

The selected release checkpoint is iter_0000014. Selection basis: best receipt-backed Fixed26 checkpoint among the preserved checkpoints.

Field	Value
Base model	`zai-org/GLM-4.7-Flash@7dd20894a642a0aa287e9827cb1a1f7f91386b67`
Adapter SHA-256	`ecc5b1e459a95032f06f4e10fde431ca28acd9ecfb35180ff6d70baa3305e402`
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	30
Run outcome	Spot-preempted after rollout 15; checkpoints through iter 14 were preserved.
Training data	`Midband_Ten_Set2_GRPO30_train.jsonl`, 80 rows
Training-data SHA-256	`36d031392e940514f8d7ce82a01663c7592cf7f505e2d455a159170324686360`
Training-manifest SHA-256	`e8ec90224ccc2e8b620c63c77e7a9ea01aff35d26863270ff5e5c6da62a899eb`

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`	14, 11, 10, 10	11.25/26	15, 14, 14, 13	14/26

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`	1.89; 10-14; 7.25-15.25	0.82; 13-15; 10-18	11/59 (18.6%; CI 9.6-29.6%)

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

80 Set2 rows: 8 each for allergies, circular-buffer, clock, complex-numbers, grade-school, parallel-letter-frequency, perfect-numbers, phone-number, robot-name, and spiral-matrix.

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`	`4b81db525690834cce03c4deb79a54a97aa06673a427f549a137b438657e8891`
`iter_0000009`	`aa883d245371afa09390f411535baadececde1e387c179155719123e08f078c9`
`iter_0000014`	`ecc5b1e459a95032f06f4e10fde431ca28acd9ecfb35180ff6d70baa3305e402`

Loading

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "zai-org/GLM-4.7-Flash"
checkpoint = "Terrano09/midband-ten-kernel12-set2-GRPO30"
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.