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--- |
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license: apache-2.0 |
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tags: |
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- jamba |
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- smol MoE |
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- smol |
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metrics: |
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- accuracy |
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datasets: |
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- BEE-spoke-data/knowledge-inoc-concat-v1 |
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- BEE-spoke-data/wikipedia-20230901.en-deduped |
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- BEE-spoke-data/fineweb-100k_en-med |
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- BEE-spoke-data/fineweb-1M_en-med |
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- BEE-spoke-data/fineweb-1M_longish |
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language: |
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- en |
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inference: false |
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--- |
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# jamba-900M-v0.13-KIx2 |
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<a href="https://colab.research.google.com/gist/pszemraj/62d037d0d93656ef2101d7e29e3b7220/jamba-test-sandbox.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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> The API widget is off as it isn't supported by hf yet - try the Colab |
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This is a pretraining experiment on the `jamba` arch as a "smol MoE". |
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Details: |
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- pretrained at context length 16384 |
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- seen approx 20b tokens |
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- uses Claude3 tokenizer (as hf GPT2 tokenizer) |
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- hidden size 1024, 12 layers, 8 experts |
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achieves the following results on the evaluation set (_most recent dataset_): |
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- Loss: 3.0366 |
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- Accuracy: 0.4514 |
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- Num Input Tokens Seen: 1975517184 |
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if I pretrain it further, other versions will be in new repos with incremented version (this is v0.13) |
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## Quick eval |
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Quick eval for: pszemraj/jamba-H1024_L12-v0.13-KIx2 |
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hf (pretrained=pszemraj/jamba-H1024_L12-v0.13-KIx2,trust_remote_code=True,dtype=float), gen_kwargs: (None), limit: 0.9999, 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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|winogrande | 1|none | 0|acc | 0.5067|± |0.0141| |
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|piqa | 1|none | 0|acc | 0.5912|± |0.0138| |
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| | |none | 0|acc_norm | 0.5951|± |0.0138| |
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|openbookqa | 1|none | 0|acc | 0.1800|± |0.0172| |
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| | |none | 0|acc_norm | 0.2920|± |0.0204| |
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|lambada_openai| 1|none | 0|perplexity|103.1241|± |8.5843| |
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| | |none | 0|acc | 0.2502|± |0.0122| |
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|boolq | 2|none | 0|acc | 0.6196|± |0.0136| |
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|arc_easy | 1|none | 0|acc | 0.3836|± |0.0137| |
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| | |none | 0|acc_norm | 0.3694|± |0.0136| |
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## example outputs |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 80085 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 2.0 |
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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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| 3.2013 | 0.4241 | 200 | 3.0653 | 0.4479 | 419430400 | |
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| 3.1976 | 0.8481 | 400 | 3.0434 | 0.4506 | 838860800 | |
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| 3.1485 | 1.2722 | 600 | 3.0375 | 0.4513 | 1258291200 | |
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| 3.1871 | 1.6963 | 800 | 3.0366 | 0.4514 | 1677721600 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
