Bump version to 2.1.0 (#474)

* Bump version to 2.1.0
* Suggest using resharded repo
* LLaMA -> Llama in readme

README.md

@@ -8,15 +8,15 @@
     <br>
 </p>
 
-Generate text with distributed **LLaMA 2 (70B)**, **Stable Beluga 2**, **Guanaco-65B** or **BLOOM-176B** and fine‑tune them for your own tasks &mdash; right from your desktop computer or Google Colab:
+Generate text with distributed **Llama 2 (70B)**, **Stable Beluga 2**, **Guanaco-65B** or **BLOOM-176B** and fine‑tune them for your own tasks &mdash; right from your desktop computer or Google Colab:
 
 ```python
 from transformers import AutoTokenizer
 from petals import AutoDistributedModelForCausalLM
 
-model_name = "stabilityai/StableBeluga2"
+model_name = "petals-team/StableBeluga2"
 # You can also use "meta-llama/Llama-2-70b-hf", "meta-llama/Llama-2-70b-chat-hf",
-# repos with LLaMA-65B, "bigscience/bloom", or "bigscience/bloomz"
+# repos with Llama-65B, "bigscience/bloom", or "bigscience/bloomz"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoDistributedModelForCausalLM.from_pretrained(model_name)
@@ -31,9 +31,9 @@ print(tokenizer.decode(outputs[0]))  # A cat sat on a mat...
     🚀 &nbsp;<b><a href="https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing">Try now in Colab</a></b>
 </p>
 
-🦙 **Want to run LLaMA 2?** Request access to its weights at the ♾️ [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and 🤗 [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), then run `huggingface-cli login` in the terminal before loading the model. Or just try it in our [chatbot app](https://chat.petals.dev).
+🦙 **Want to run Llama 2?** Request access to its weights at the ♾️ [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and 🤗 [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), then run `huggingface-cli login` in the terminal before loading the model. Or just try it in our [chatbot app](https://chat.petals.dev).
 
-📋 **Terms of use.** Make sure you follow the model license (see [LLaMA 2](https://bit.ly/llama2-license), [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2/blob/main/LICENSE.txt), [LLaMA](https://bit.ly/llama-license), and [BLOOM](https://bit.ly/bloom-license)).
+📋 **Terms of use.** Make sure you follow the model license (see [Llama 2](https://bit.ly/llama2-license), [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2/blob/main/LICENSE.txt), [Llama](https://bit.ly/llama-license), and [BLOOM](https://bit.ly/bloom-license)).
 
 🔏 **Privacy.** Your data will be processed by other people in the public swarm. Learn more about privacy [here](https://github.com/bigscience-workshop/petals/wiki/Security,-privacy,-and-AI-safety). For sensitive data, you can set up a [private swarm](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm) among people you trust.
 
@@ -48,7 +48,7 @@ Petals is a community-run system &mdash; we rely on people sharing their GPUs. Y
 ```bash
 conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
 pip install git+https://github.com/bigscience-workshop/petals
-python -m petals.cli.run_server stabilityai/StableBeluga2
+python -m petals.cli.run_server petals-team/StableBeluga2
 ```
 
 🪟 **Windows + WSL.** Follow the guide on our [Wiki](https://github.com/bigscience-workshop/petals/wiki/Run-Petals-server-on-Windows).
@@ -57,12 +57,12 @@ python -m petals.cli.run_server stabilityai/StableBeluga2
 
 ```bash
 sudo docker run -p 31330:31330 --ipc host --gpus all --volume petals-cache:/cache --rm learningathome/petals:main \
-    python -m petals.cli.run_server --port 31330 stabilityai/StableBeluga2
+    python -m petals.cli.run_server --port 31330 petals-team/StableBeluga2
 ```
 
-These commands will host a part of [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2) on your machine. You can also host `meta-llama/Llama-2-70b-hf`, `meta-llama/Llama-2-70b-chat-hf`, repos with LLaMA-65B, `bigscience/bloom`, `bigscience/bloomz`, and other compatible models from 🤗 [Model Hub](https://huggingface.co/models), or [add support](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals) for new model architectures.
+These commands will host a part of [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2) on your machine. You can also host `meta-llama/Llama-2-70b-hf`, `meta-llama/Llama-2-70b-chat-hf`, repos with Llama-65B, `bigscience/bloom`, `bigscience/bloomz`, and other compatible models from 🤗 [Model Hub](https://huggingface.co/models), or [add support](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals) for new model architectures.
 
-🦙 **Want to host LLaMA 2?** Request access to its weights at the ♾️ [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and 🤗 [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), generate an 🔑 [access token](https://huggingface.co/settings/tokens), then use this command for `petals.cli.run_server`:
+🦙 **Want to host Llama 2?** Request access to its weights at the ♾️ [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and 🤗 [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), generate an 🔑 [access token](https://huggingface.co/settings/tokens), then use this command for `petals.cli.run_server`:
 
 ```bash
 python -m petals.cli.run_server meta-llama/Llama-2-70b-chat-hf --token YOUR_TOKEN_HERE
@@ -79,7 +79,7 @@ python -m petals.cli.run_server meta-llama/Llama-2-70b-chat-hf --token YOUR_TOKE
 Basic tutorials:
 
 - Getting started: [tutorial](https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing)
-- Prompt-tune LLaMA-65B for text semantic classification: [tutorial](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb)
+- Prompt-tune Llama-65B for text semantic classification: [tutorial](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb)
 - Prompt-tune BLOOM to create a personified chatbot: [tutorial](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-personachat.ipynb)
 
 Useful tools and advanced guides:
@@ -96,8 +96,8 @@ Learning more:
 
 ## How does it work?
 
-- Petals runs large language models like [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) and [BLOOM](https://huggingface.co/bigscience/bloom) **collaboratively** — you load a small part of the model, then join people serving the other parts to run inference or fine-tuning.
-- Single-batch inference runs at **up to 6 steps/sec** for **LLaMA 2** (70B) and &approx; 1 step/sec for BLOOM-176B. This is [up to 10x faster](https://github.com/bigscience-workshop/petals#benchmarks) than offloading, enough to build [chatbots](https://chat.petals.dev) and other interactive apps. Parallel inference reaches hundreds of tokens/sec.
+- Petals runs large language models like [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) and [BLOOM](https://huggingface.co/bigscience/bloom) **collaboratively** — you load a small part of the model, then join people serving the other parts to run inference or fine-tuning.
+- Single-batch inference runs at **up to 6 steps/sec** for **Llama 2** (70B) and &approx; 1 step/sec for BLOOM-176B. This is [up to 10x faster](https://github.com/bigscience-workshop/petals#benchmarks) than offloading, enough to build [chatbots](https://chat.petals.dev) and other interactive apps. Parallel inference reaches hundreds of tokens/sec.
 - Beyond classic language model APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch.
 
 <p align="center">

src/petals/__init__.py

@@ -11,7 +11,7 @@ from petals.models import *
 from petals.utils import *
 from petals.utils.logging import initialize_logs as _initialize_logs
 
-__version__ = "2.0.1.post2"
+__version__ = "2.1.0"
 
 
 if not os.getenv("PETALS_IGNORE_DEPENDENCY_VERSION"):