Quantization made by Richard Erkhov.
smol_llama-220M-GQA-fineweb_edu - GGUF
- Model creator: https://huggingface.co/BEE-spoke-data/
- Original model: https://huggingface.co/BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu/
Original model description:
license: apache-2.0 base_model: BEE-spoke-data/smol_llama-220M-GQA tags:
edu
continual pretraining metrics:
accuracy inference: parameters: max_new_tokens: 64 do_sample: true temperature: 0.8 repetition_penalty: 1.05 no_repeat_ngram_size: 4 eta_cutoff: 0.0006 renormalize_logits: true widget:
text: My name is El Microondas the Wise, and example_title: El Microondas
text: Kennesaw State University is a public example_title: Kennesaw State University
text: >- Bungie Studios is an American video game developer. They are most famous for developing the award winning Halo series of video games. They also made Destiny. The studio was founded example_title: Bungie
text: The Mona Lisa is a world-renowned painting created by example_title: Mona Lisa
text: >- The Harry Potter series, written by J.K. Rowling, begins with the book titled example_title: Harry Potter Series
text: >- Question: I have cities, but no houses. I have mountains, but no trees. I have water, but no fish. What am I?
Answer: example_title: Riddle
text: The process of photosynthesis involves the conversion of example_title: Photosynthesis
text: >- Jane went to the store to buy some groceries. She picked up apples, oranges, and a loaf of bread. When she got home, she realized she forgot example_title: Story Continuation
text: >- Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and another train leaves Station B at 10:00 AM and travels at 80 mph, when will they meet if the distance between the stations is 300 miles?
To determine example_title: Math Problem
text: In the context of computer programming, an algorithm is example_title: Algorithm Definition pipeline_tag: text-generation datasets:
HuggingFaceFW/fineweb-edu language:
en
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
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