Create README.md

README.md

@@ -0,0 +1,108 @@
+---
+license: cc-by-sa-3.0
+datasets:
+- mosaicml/dolly_hhrlhf
+tags:
+- Composer
+- MosaicML
+- llm-foundry
+---
+
+# MPT-7B-Instruct
+
+MPT-7B-Instruct is a model for short-form instruction following.
+It is built by finetuning [MPT-7B (Base)](https://huggingface.co/spaces/mosaicml/mpt-7b) on a [dataset](https://huggingface.co/datasets/sam-mosaic/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. 
+  * License: _CC-By-SA-3.0_ (commercial use permitted)
+  * [Online Demo](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct)
+
+
+This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
+
+## Model Date
+
+May 5, 2023
+
+## Model License
+
+Apache-2.0 (commercial use permitted)
+
+## Documentation
+
+* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](www.mosaicml.com/blog/mpt-7b)
+* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
+* Questions: Feel free to contact us via the [MosaicML Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-w0tiddn9-WGTlRpfjcO9J5jyrMub1dg)!
+
+### Example Dialogue
+
+**SamIAm85**:
+I want you to come up with a tweet based on this summary of the article:
+
+"Introducing MPT-7B, the latest entry in our MosaicML Foundation Series.
+MPT-7B is a transformer trained from scratch on IT tokens of text and code.
+It is open source, available for commercial use, and it matches the quality of LLaMA-7B.
+MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k.
+Starting today, you can train, finetune, and deploy your own private MPT models, either starting from one of our checkpoints or training from scratch.
+For inspiration, we are also releasing three finetuned models in addition to the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-Story Writer-65k+, the last of which uses a context length of 65k tokens!
+
+**MPT-7B-Instruct**:
+>Here's my suggestion for a tweet:
+>"Exciting news! @MosaicML has launched #MPT_7B - a new standard for open-source, commercially usable LLMs. With training on 17 tokens of text & code, it matches the quality of LLaMA-7B. Now you can train, fine-tune, and deploy your own private MPT models via their platform. Check out the full blog post here: https://bit.ly/3j6UJzZ"
+
+**SamIAm85**:
+Is there anything you want to say to MosaicML about this exciting news?
+
+**MPT-7B-Instruct**:
+> As an Al language model myself, I am very impressed with the advancements made by MosaicML in developing such high-quality open-source LLMs.
+> This new version of MPT-7B is truly impressive and I look forward to seeing what innovative applications developers will create using these powerful tools.
+> Thank you for your hard work and dedication to advancing Al research and development.
+
+## How to Use
+
+Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture that is not yet part of the `transformers` package.
+
+It includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
+
+```python
+import transformers
+model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b-instruct', trust_remote_code=True, torch_dtype=torch.bfloat16)
+```
+
+To use the optimized triton implementation of FlashAttention, you can load with `attn_impl='triton'` and move the model to `bfloat16` like so:
+
+```python
+model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b-instruct', trust_remote_code=True, torch_dtype=torch.bfloat16, attn_impl='triton')
+model.to(device='cuda:0', dtype=torch.bfloat16)
+```
+
+## Model Description
+
+The architecture is a modification of a standard decoder-only transformer.
+
+The model has been modified from a standard transformer in the following ways:
+* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
+* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
+* It does not use biases
+
+
+| Hyperparameter | Value |
+|----------------|-------|
+|n_parameters | 6.7B |
+|n_layers | 32 |
+| n_heads | 32 |
+| d_model | 4096 |
+| vocab size | 50432 |
+| sequence length | 2048 |
+
+## PreTraining Data
+
+For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b).
+
+The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
+
+## Training Configuration
+
+This model was finetuned on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using FSDP.
+
+## Acknowledgements
+
+