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phuctahong/
nanogpt-ddl2-gpt-mha-rope-cc-ec-lambda-weighted-accelerated-353m-job10890011-step100000

Name: nanogpt-ddl2-gpt-mha-rope-cc-ec-lambda-weighted-accelerated-353m-job10890011-step100000
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phuctahong/nanogpt-ddl2-gpt-mha-rope-cc-ec-lambda-weighted-accelerated-353m-job10890011-step100000

NanoGPT Pro checkpoint uploaded from the princeton-pli/Nanogpt W&B project.

Provenance

W&B run: M_DDL2-gpt-mha-rope-CC-EC-lambda-weighted-accelerated_353m_Opt_adamw_LR_0.001_D_fineweb-edu100B_T_49.15B_time_20260714_120342_jobid_10890011
W&B run id: mspj5jmo
Job id: 10890011
Checkpoint step: 100000
Local checkpoint: /scratch/gpfs/JHACKL/nanogpt-next_mod/output/out_DDL2-gpt-mha-rope-CC-EC-lambda-weighted-accelerated_353m_Opt_adamw_LR_0.001_D_fineweb-edu100B_T_49.15B_time_20260714_120342_jobid_10890011/checkpoint-100000

Files

This repo contains inference-ready weights only:

config.json
model.safetensors
generation_config.json

Optimizer and trainer state were intentionally omitted.

Load locally

python

from nanogptpro.model_loader import load_model_from_checkpoint

model = load_model_from_checkpoint(
    model_id="<your-model-id>",
    checkpoint="phuctahong/nanogpt-ddl2-gpt-mha-rope-cc-ec-lambda-weighted-accelerated-353m-job10890011-step100000",
)

Install nanogptpro and use the model id that matches the architecture recorded in config.json.