TokenBender/glm47-flash-pie-cpp-lora-r16-sft-h100

GLM-4.7-Flash PIE C++ SFT LoRA

LoRA rank-16 supervised fine-tuning adapter for zai-org/GLM-4.7-Flash, trained on the PIE C++ performance task.

Result

The full 1,259-task evaluation produced:

Metric	Result
Pass rate	90.79%
Valid format rate	97.70%
Correct and faster rate	28.36%
Mean speedup when correct and faster	1.43x
Mean reward	0.8980
Timeout rate	0.00%

The complete per-task records and generated responses are under evidence/eval/.

Training Profile

Setting	Value
Accelerators	8x NVIDIA H100
Parallelism	TP4 / PP1 / EP8
Sequence length	4,096
Max tokens per GPU	24,576
Global batch size	32
LoRA rank / alpha	16 / 32
Peak allocated memory	36,039 MiB per GPU
Checkpoint	Step 244

Files

• adapter_model.bin and adapter_config.json: loadable PEFT adapter.
• adapter_megatron_tp*_pp0.pt: four Megatron tensor-parallel shards.
• training_state_rank*.pt: per-rank training state.
• evidence/training/: run receipt and VRAM trace.
• evidence/eval/: complete evaluation summaries, records, and generations.

Loading

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained(
    "zai-org/GLM-4.7-Flash",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(
    base,
    "TokenBender/glm47-flash-pie-cpp-lora-r16-sft-h100",
)

Training code: TokenBender/browser-is-all-you-need