Update README.md

README.md

@@ -9,23 +9,101 @@ tags:
 - silo
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-
 # Silo Language Models: Isolating Legal Risk in a Datastore
 
 This is Silo-PDSW, first introduced in [Silo Language Models]() by researchers at University of Washington, UC Berkeley, and the Allen Institute for AI.
 
+### NOTE: Dependencies
+
+To use the model, you need to install a specific transformers fork:
+
+```
+pip install git+https://github.com/kernelmachine/transformers@openlm#egg=transformers
+```
 
+The model also depends on `xformers`, install via 
+
+```
+pip install xformers
+```
 
 ### Model Description
 
+
+Silo-PDSW is a 1.3B parameter, decoder-only language model trained on data in the public domain and under permissive software licenses from [the Open License Corpus (OLC)](https://huggingface.co/datasets/kernelmachine/open-license-corpus). 
+
+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
+
+Silo-PDSW was trained on data in the public domain and under permissive software licenses from [the Open License Corpus (OLC)](https://huggingface.co/datasets/kernelmachine/open-license-corpus). 
+
+The model 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) | %     |
+|-----------------|------------|-------|
+| Code              | 58.9       | 59.1  |
+| Legal             | 27.1       | 27.2  | 
+| Conversation      | 5.9        | 5.9   |
+| Math               | 3.5        | 3.5   |
+| Books              | 2.9        | 2.9   |
+| Science          | 1.2        | 1.2   | 
+| News              | 0.2        | 0.2   |
+| Total           | 99.6       | 100.0 |
+
+We train with early stopping for 250B tokens in total, or a little more than two 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
 
-### Limitations and Bias
 
-### Training data
+You can use this model directly with a pipeline for text generation.
+
+
+```python
+from transformers import pipeline
+generator = pipeline('text-generation', model="kernelmachine/silo-pdsw-1.3b", device='cuda')
+generator("Hello")
+[{'generated_text': "Hello, I'm a new user of Ubuntu. I'm trying to install the latest version of Ubuntu"}]
+```
+
+By default, generation is deterministic. In order to use the top-k sampling, please set do_sample to True.
+
+
+```python
+from transformers import pipeline, set_seed
+set_seed(32)
+generator = pipeline('text-generation', model="kernelmachine/silo-pdsw-1.3b", device='cuda', do_sample=True)
+generator("Hello")
+[{'generated_text': 'Hello: Hello World;", ""));\n        }\n\n        [Test]\n        public void'}]
+```
+
+### Limitations and Bias
 
-#### Collection Process
+Silo-PDSW 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.
 
-### Training procedure
+Silo-PDSW may also output personally identifiable information, because we did not filter that out of training data.