metadata
license: apache-2.0
tags:
- jamba
- smol MoE
- smol
metrics:
- accuracy
datasets:
- BEE-spoke-data/knowledge-inoc-concat-v1
- BEE-spoke-data/wikipedia-20230901.en-deduped
- BEE-spoke-data/fineweb-100k_en-med
- BEE-spoke-data/fineweb-1M_en-med
- BEE-spoke-data/fineweb-1M_longish
language:
- en
inference: false
jamba-900M-v0.13-KIx2
The API widget is off as it isn't supported by hf yet - try the Colab
This is a pretraining experiment on the jamba arch as a "smol MoE".
Details:
- pretrained at context length 16384
- seen approx 20b tokens
- uses Claude3 tokenizer (as hf GPT2 tokenizer)
- hidden size 1024, 12 layers, 8 experts
achieves the following results on the evaluation set (most recent dataset):
- Loss: 3.0366
- Accuracy: 0.4514
- Num Input Tokens Seen: 1975517184
if I pretrain it further, other versions will be in new repos with incremented version (this is v0.13)
Quick eval
Quick eval for: pszemraj/jamba-H1024_L12-v0.13-KIx2
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
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| winogrande | 1 | none | 0 | acc | 0.5067 | ± | 0.0141 |
| piqa | 1 | none | 0 | acc | 0.5912 | ± | 0.0138 |
| none | 0 | acc_norm | 0.5951 | ± | 0.0138 | ||
| openbookqa | 1 | none | 0 | acc | 0.1800 | ± | 0.0172 |
| none | 0 | acc_norm | 0.2920 | ± | 0.0204 | ||
| lambada_openai | 1 | none | 0 | perplexity | 103.1241 | ± | 8.5843 |
| none | 0 | acc | 0.2502 | ± | 0.0122 | ||
| boolq | 2 | none | 0 | acc | 0.6196 | ± | 0.0136 |
| arc_easy | 1 | none | 0 | acc | 0.3836 | ± | 0.0137 |
| none | 0 | acc_norm | 0.3694 | ± | 0.0136 |
example outputs
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 80085
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Input Tokens Seen |
|---|---|---|---|---|---|
| 3.2013 | 0.4241 | 200 | 3.0653 | 0.4479 | 419430400 |
| 3.1976 | 0.8481 | 400 | 3.0434 | 0.4506 | 838860800 |
| 3.1485 | 1.2722 | 600 | 3.0375 | 0.4513 | 1258291200 |
| 3.1871 | 1.6963 | 800 | 3.0366 | 0.4514 | 1677721600 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1