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phuctahong/
nanogpt-falcon-1-3-lambda-init-0-1-0-1-lambda-scale-0-95-beta-scale-by-2-f-job6149466-step100000

Name: nanogpt-falcon-1-3-lambda-init-0-1-0-1-lambda-scale-0-95-beta-scale-by-2-f-job6149466-step100000
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phuctahong/nanogpt-falcon-1-3-lambda-init-0-1-0-1-lambda-scale-0-95-beta-scale-by-2-f-job6149466-step100000

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

Provenance

W&B run: [Falcon-1.3-lambda_init-0.1-0.1-lambda_scale-0.95-beta_scale_by_2-false]M_gpt-mha-scaled_delta_net_parallel_ctxeta_ctxlambda_triton_130m_Opt_adamw_LR_0.001_D_fineweb-edu100B_T_49.15B_time_20260401_043034_jobid_6149466
W&B run id: 66g9umbj
Job id: 6149466
Checkpoint step: 100000
Local checkpoint: /scratch/gpfs/ARORA/st0490/nanogpt-next_mod/output/out_gpt-mha-scaled_delta_net_parallel_ctxeta_ctxlambda_triton_130m_Opt_adamw_LR_0.001_D_fineweb-edu100B_T_49.15B_time_20260401_043034_jobid_6149466/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-falcon-1-3-lambda-init-0-1-0-1-lambda-scale-0-95-beta-scale-by-2-f-job6149466-step100000",
)

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