---
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

<a href="https://colab.research.google.com/gist/pszemraj/62d037d0d93656ef2101d7e29e3b7220/jamba-test-sandbox.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

> 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



![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/wky-qjUtS0AJ6YtIsJh3T.png)

## 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