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TokenBender/glm47-flash-pie-cpp-lora-r16-sft-h100
glm47-flash-pie-cpp-lora-r16-sft-h100 is a text generation model from TokenBender. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as mit.
LoRA rank-16 supervised fine-tuning adapter for zai-org/GLM-4.7-Flash, trained on the PIE C++ performance task.
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From the Hugging Face model README
LoRA rank-16 supervised fine-tuning adapter for
zai-org/GLM-4.7-Flash,
trained on the PIE C++ performance task.
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/.
| 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 |
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.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