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--- |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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inference: |
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parameters: |
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max_new_tokens: 64 |
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do_sample: true |
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temperature: 0.7 |
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repetition_penalty: 1.10 |
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no_repeat_ngram_size: 6 |
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eta_cutoff: 0.0008 |
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renormalize_logits: true |
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widget: |
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- text: My name is El Microondas the Wise, and |
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example_title: El Microondas |
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- text: Kennesaw State University is a public |
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example_title: Kennesaw State University |
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- text: >- |
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Bungie Studios is an American video game developer. They are most famous for |
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developing the award winning Halo series of video games. They also made |
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Destiny. The studio was founded |
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example_title: Bungie |
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- text: The Mona Lisa is a world-renowned painting created by |
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example_title: Mona Lisa |
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- text: >- |
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The Harry Potter series, written by J.K. Rowling, begins with the book |
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titled |
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example_title: Harry Potter Series |
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- text: >- |
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Question: I have cities, but no houses. I have mountains, but no trees. I |
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have water, but no fish. What am I? |
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Answer: |
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example_title: Riddle |
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- text: The process of photosynthesis involves the conversion of |
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example_title: Photosynthesis |
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- text: >- |
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Jane went to the store to buy some groceries. She picked up apples, oranges, |
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and a loaf of bread. When she got home, she realized she forgot |
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example_title: Story Continuation |
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- text: >- |
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Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and |
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another train leaves Station B at 10:00 AM and travels at 80 mph, when will |
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they meet if the distance between the stations is 300 miles? |
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To determine |
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example_title: Math Problem |
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- text: In the context of computer programming, an algorithm is |
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example_title: Algorithm Definition |
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pipeline_tag: text-generation |
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datasets: |
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- BEE-spoke-data/UltraTextbooks-2.1-fw_mix |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mega-ar-350m-L3t-v0.08-ultraTBfw |
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## Model description |
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This is a pretraining experiment most recently trained on the BEE-spoke-data/UltraTextbooks-2.1-fw_mix dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0787 |
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- Accuracy: 0.5746 |
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- Num Input Tokens Seen: 3492282368 |
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## Quick eval |
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Quick eval for: pszemraj/mega-ar-350m-L3t-v0.08-ultraTBfw |
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hf (pretrained=pszemraj/mega-ar-350m-L3t-v0.08-ultraTBfw,trust_remote_code=True,dtype=float), gen_kwargs: (None), limit: 0.99999, num_fewshot: None, batch_size: 8 |
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| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr| |
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|--------------|------:|------|-----:|----------|------:|---|-----:| |
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|arc_easy | 1|none | 0|acc | 0.4246|± |0.0139| |
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| | |none | 0|acc_norm | 0.4002|± |0.0138| |
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|boolq | 2|none | 0|acc | 0.5762|± |0.0139| |
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|lambada_openai| 1|none | 0|perplexity|76.7162|± |6.3531| |
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| | |none | 0|acc | 0.2605|± |0.0123| |
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|openbookqa | 1|none | 0|acc | 0.1840|± |0.0173| |
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| | |none | 0|acc_norm | 0.2720|± |0.0199| |
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|piqa | 1|none | 0|acc | 0.6377|± |0.0135| |
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| | |none | 0|acc_norm | 0.6172|± |0.0137| |
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|winogrande | 1|none | 0|acc | 0.5020|± |0.0141| |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 4e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 80085 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 |
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- lr_scheduler_type: inverse_sqrt |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Input Tokens Seen | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:| |
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| 2.2572 | 0.0600 | 400 | 2.2462 | 0.5491 | 209715200 | |
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| 2.2173 | 0.1201 | 800 | 2.1939 | 0.5564 | 419430400 | |
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| 2.1992 | 0.1801 | 1200 | 2.1689 | 0.5604 | 629145600 | |
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| 2.1543 | 0.2402 | 1600 | 2.1521 | 0.5632 | 838860800 | |
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| 2.1532 | 0.3002 | 2000 | 2.1401 | 0.5650 | 1048576000 | |
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| 2.1688 | 0.3603 | 2400 | 2.1307 | 0.5663 | 1258291200 | |
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| 2.1443 | 0.4203 | 2800 | 2.1227 | 0.5676 | 1468006400 | |
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| 2.1105 | 0.4804 | 3200 | 2.1158 | 0.5689 | 1677721600 | |
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| 2.1045 | 0.5404 | 3600 | 2.1090 | 0.5700 | 1887436800 | |
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| 2.1181 | 0.6004 | 4000 | 2.1045 | 0.5708 | 2097152000 | |
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| 2.127 | 0.6605 | 4400 | 2.0994 | 0.5716 | 2306867200 | |
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| 2.1265 | 0.7205 | 4800 | 2.0958 | 0.5719 | 2516582400 | |
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| 2.0951 | 0.7806 | 5200 | 2.0909 | 0.5728 | 2726297600 | |
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| 2.0951 | 0.8406 | 5600 | 2.0876 | 0.5733 | 2936012800 | |
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| 2.1335 | 0.9007 | 6000 | 2.0838 | 0.5739 | 3145728000 | |
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| 2.0731 | 0.9607 | 6400 | 2.0802 | 0.5744 | 3355443200 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
