Silo-PD is a 1.3B parameter, decoder-only language model trained on data in the public domain from the Open License Corpus (OLC).
The model is based on the LLaMA architecture as implemented in (OpenLM)[].
The model is trained with 128 A100 GPUs across 16 nodes.
Model and Training Hyperparameters
We follow the model architecture of LLaMa, and we use the GPT-NeoX-20B tokenizer, with 50432 BPE types.
During training, we use 2,048 token sequences that are packed across document boundaries, and we pre-pend a beginning-of-text token to every document.
We use weight decay of 0.1, the Adam optimizer with beta_2 of 0.95, 2,000 steps of warmup, with a cosine learning rate scheduler.
Model
#L
#H
d_model
LR
Batch
1.3B
24
16
2048
1e-3
2.6M
Training data
Specifically, it was trained on the following domain proportions (please see the OLC repository for more details on the data sources for each domain):
Domain
Tokens (B)
%
Legal
27.1
86.2
Books
2.9
9.3
Science
1.2
3.8
News
0.2
0.7
Total
31.4
100.0
We train with early stopping for 60B tokens in total, for a total of 2 epochs of training over this subset
Since the distribution of OLC is highly skewed, we perform a simple upweighting scheme where we upsample all data that accounts for less than 5% of the corpus by a factor of 3x, which we found to work well after a sweep of different settings.
Intended Uses and Limitations
This model can be used for prompting for evaluation of downstream tasks as well as text generation.
How to use
You can use this model directly with a pipeline for text generation.
python
1from transformers import pipeline
2generator = pipeline('text-generation', model="kernelmachine/silo-pd-1.3b", device='cuda')3generator("Hello")4[{'generated_text':'Hello, my dear," said the old man, "I have been waiting for you\na long'}]
By default, generation is deterministic. In order to use the top-k sampling, please set do_sample to True.
python
1from transformers import pipeline, set_seed
2set_seed(42)3generator = pipeline('text-generation', model="kernelmachine/silo-pd-1.3b", device='cuda', do_sample=True)4generator("Hello")5[{'generated_text':'Hello, Mother," he called.\n\n"Hello, Son. Have you got a car'}]
Limitations and Bias
Silo-PD inherits the biases and limitations of public domain data, which carry risks of toxic or otherwise unfair output, due to the prevalence of older copyright-expired text.
Silo-PD may also output personally identifiable information, because we did not filter that out of training data.
