Create README.md (#3)

- Create README.md (b4cfab95b8700725c41ebeed13fdcd194aea0124)


Co-authored-by: Jonathan Frankle <jfrankle@users.noreply.huggingface.co>

README.md

@@ -0,0 +1,65 @@
+---
+license: apache-2.0
+datasets:
+- togethercomputer/RedPajama-Data-1T
+---
+
+# Mosaic-1b-RedPajama-200b
+
+Mosaic-1b-RedPajama-200b is a 1.4 billion parameter decoder-only transformer trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).
+The model was trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions as were used by the [Llama series of models](https://arxiv.org/abs/2302.13971).
+This model was trained by [MosaicML](https://www.mosaicml.com) and follows the a modified decoder-only transformer architecture.
+
+## Model Date
+
+April 19, 2023
+
+## How to Use
+
+Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
+This is because we train using [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), which is not part of the `transformers` library and depends on [Triton](https://github.com/openai/triton) and some custom PyTorch code.
+
+```python
+import transformers
+model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mosaic-llama-redpajama-final-candidate', trust_remote_code=True)```
+```
+
+## Model Description
+
+This model uses the MosaicML LLM codebase, which can be found in the [MosaicML Examples Repository](https://github.com/mosaicml/examples/tree/v0.0.4/examples/llm).
+The architecture is a modification of a standard decoder-only transformer.
+The transformer has 24 layers, 16 attention heads, and width 2048.
+The model has been modified from a standard transformer in the following ways:
+* It uses FlashAttention.
+* It uses ALiBi position encodings.
+* It does not use biases.
+
+## Training Data
+
+The model was trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix:
+* 67% RedPajama Common Crawl
+* 15% [C4](https://huggingface.co/datasets/c4)
+* 4.5% RedPajama GitHub
+* 4.5% RedPajama Wikipedia
+* 4.5% RedPajama Books
+* 2.5% RedPajama Arxiv
+* 2% RedPajama StackExchange
+
+This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971).
+
+Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above.
+The examples were shuffled within each dataset.
+Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
+
+The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
+
+## Training Configuration
+
+This model was trained on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained in a data parallel manner using FSDP.
+
+## Acknowledgements
+
+This model builds on the work of [Together](https://www.together.xyz), which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models.
+We gratefully acknowledge the hard work of the team that put together this dataset, and we hope this model serves as a useful companion to that work.
+
+We also gratefully acknowledge the work of the researchers who created the Llama series of models, which was the impetus for our efforts and those who worked on the RedPajama project.