smol_llama-220M-GQA-fineweb-edu-10BT
This model is a continously pretrained version of BEE-spoke-data/smol_llama-220M-GQA on the 10BT-sample subset of HuggingFaceFW/fineweb-edu
.
It achieves the following results on the evaluation set:
- Loss: 2.7416
- Accuracy: 0.4560
- Num Input Tokens Seen: 10810818560
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 80085
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Input Tokens Seen |
---|---|---|---|---|---|
2.8567 | 0.0145 | 300 | 2.8291 | 0.4450 | 157286400 |
2.8517 | 0.0291 | 600 | 2.8153 | 0.4465 | 314572800 |
2.8224 | 0.0436 | 900 | 2.8025 | 0.4481 | 471859200 |
2.8178 | 0.0582 | 1200 | 2.7912 | 0.4495 | 629145600 |
2.8001 | 0.0727 | 1500 | 2.7832 | 0.4505 | 786432000 |
2.8045 | 0.0873 | 1800 | 2.7772 | 0.4512 | 943718400 |
2.8019 | 0.1018 | 2100 | 2.7729 | 0.4516 | 1101004800 |
2.7995 | 0.1164 | 2400 | 2.7691 | 0.4522 | 1258291200 |
2.8006 | 0.1309 | 2700 | 2.7657 | 0.4526 | 1415577600 |
2.7886 | 0.1455 | 3000 | 2.7631 | 0.4528 | 1572864000 |
2.7907 | 0.1600 | 3300 | 2.7606 | 0.4532 | 1730150400 |
2.7907 | 0.1746 | 3600 | 2.7588 | 0.4536 | 1887436800 |
2.7788 | 0.1891 | 3900 | 2.7569 | 0.4537 | 2044723200 |
2.7942 | 0.2037 | 4200 | 2.7552 | 0.4540 | 2202009600 |
2.793 | 0.2182 | 4500 | 2.7538 | 0.4543 | 2359296000 |
2.7958 | 0.2328 | 4800 | 2.7526 | 0.4544 | 2516582400 |
2.78 | 0.2473 | 5100 | 2.7515 | 0.4547 | 2673868800 |
2.7937 | 0.2619 | 5400 | 2.7506 | 0.4548 | 2831155200 |
2.7717 | 0.2764 | 5700 | 2.7498 | 0.4548 | 2988441600 |
2.7832 | 0.2910 | 6000 | 2.7490 | 0.4548 | 3145728000 |
2.768 | 0.3055 | 6300 | 2.7482 | 0.4550 | 3303014400 |
2.7653 | 0.3201 | 6600 | 2.7476 | 0.4551 | 3460300800 |
2.7843 | 0.3346 | 6900 | 2.7470 | 0.4551 | 3617587200 |
2.7765 | 0.3492 | 7200 | 2.7464 | 0.4550 | 3774873600 |
2.7778 | 0.3637 | 7500 | 2.7460 | 0.4552 | 3932160000 |
2.7655 | 0.3783 | 7800 | 2.7455 | 0.4553 | 4089446400 |
2.7943 | 0.3928 | 8100 | 2.7449 | 0.4554 | 4246732800 |
2.7715 | 0.4074 | 8400 | 2.7447 | 0.4552 | 4404019200 |
2.7828 | 0.4219 | 8700 | 2.7443 | 0.4554 | 4561305600 |
2.7883 | 0.4365 | 9000 | 2.7440 | 0.4556 | 4718592000 |
2.7627 | 0.4510 | 9300 | 2.7437 | 0.4556 | 4875878400 |
2.7841 | 0.4656 | 9600 | 2.7435 | 0.4557 | 5033164800 |
2.7734 | 0.4801 | 9900 | 2.7433 | 0.4557 | 5190451200 |
2.7829 | 0.4947 | 10200 | 2.7430 | 0.4557 | 5347737600 |
2.781 | 0.5092 | 10500 | 2.7429 | 0.4557 | 5505024000 |
2.7757 | 0.5238 | 10800 | 2.7428 | 0.4557 | 5662310400 |
2.779 | 0.5383 | 11100 | 2.7426 | 0.4559 | 5819596800 |
2.7771 | 0.5529 | 11400 | 2.7425 | 0.4559 | 5976883200 |
2.7828 | 0.5674 | 11700 | 2.7424 | 0.4560 | 6134169600 |
2.7814 | 0.5820 | 12000 | 2.7423 | 0.4558 | 6291456000 |
2.7735 | 0.5965 | 12300 | 2.7422 | 0.4559 | 6448742400 |
2.7848 | 0.6111 | 12600 | 2.7420 | 0.4559 | 6606028800 |
2.7748 | 0.6256 | 12900 | 2.7420 | 0.4559 | 6763315200 |
2.7697 | 0.6402 | 13200 | 2.7419 | 0.4560 | 6920601600 |
2.7689 | 0.6547 | 13500 | 2.7419 | 0.4560 | 7077888000 |
2.7747 | 0.6692 | 13800 | 2.7419 | 0.4559 | 7235174400 |
2.786 | 0.6838 | 14100 | 2.7418 | 0.4561 | 7392460800 |
2.7801 | 0.6983 | 14400 | 2.7417 | 0.4560 | 7549747200 |
2.7658 | 0.7129 | 14700 | 2.7417 | 0.4561 | 7707033600 |
2.7717 | 0.7274 | 15000 | 2.7417 | 0.4560 | 7864320000 |
2.7717 | 0.7420 | 15300 | 2.7417 | 0.4560 | 8021606400 |
2.777 | 0.7565 | 15600 | 2.7417 | 0.4559 | 8178892800 |
2.7793 | 0.7711 | 15900 | 2.7416 | 0.4560 | 8336179200 |
2.7718 | 0.7856 | 16200 | 2.7416 | 0.4559 | 8493465600 |
2.7757 | 0.8002 | 16500 | 2.7416 | 0.4560 | 8650752000 |
2.7763 | 0.8147 | 16800 | 2.7416 | 0.4559 | 8808038400 |
2.7581 | 0.8293 | 17100 | 2.7416 | 0.4559 | 8965324800 |
2.7719 | 0.8438 | 17400 | 2.7416 | 0.4560 | 9122611200 |
2.7609 | 0.8584 | 17700 | 2.7416 | 0.4560 | 9279897600 |
2.7753 | 0.8729 | 18000 | 2.7416 | 0.4559 | 9437184000 |
2.7674 | 0.8875 | 18300 | 2.7415 | 0.4560 | 9594470400 |
2.7601 | 0.9020 | 18600 | 2.7416 | 0.4560 | 9751756800 |
2.7823 | 0.9166 | 18900 | 2.7416 | 0.4560 | 9909043200 |
2.7767 | 0.9311 | 19200 | 2.7416 | 0.4560 | 10066329600 |
2.7759 | 0.9457 | 19500 | 2.7416 | 0.4560 | 10223616000 |
2.7722 | 0.9602 | 19800 | 2.7415 | 0.4560 | 10380902400 |
2.7764 | 0.9748 | 20100 | 2.7416 | 0.4560 | 10538188800 |
2.7724 | 0.9893 | 20400 | 2.7416 | 0.4559 | 10695475200 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.1+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 6.52 |
IFEval (0-Shot) | 19.88 |
BBH (3-Shot) | 2.31 |
MATH Lvl 5 (4-Shot) | 0.00 |
GPQA (0-shot) | 1.23 |
MuSR (0-shot) | 14.26 |
MMLU-PRO (5-shot) | 1.41 |
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Dataset used to train BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard19.880
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard2.310
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard0.000
- acc_norm on GPQA (0-shot)Open LLM Leaderboard1.230
- acc_norm on MuSR (0-shot)Open LLM Leaderboard14.260
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard1.410