Running is simple to get started. If using OpenAI, set `export OPENAI_API_KEY=<OPENAIKEY>` (or pass key in through variable `client_connection`) then run
We have example notebook and python scripts located at [examples](examples). These show how to use different models, model types (i.e. text, diffusers, or embedding models), and async running.
Manifest provides model clients for [OpenAI](https://openai.com/), [AI21](https://studio.ai21.com/), [Cohere](https://cohere.ai/), [Together](https://together.xyz/), and HuggingFace (see [below](#huggingface-models) for how to use locally hosted HuggingFace models). You can toggle between the models by changing `client_name` and `client_connection`. For example, if a HuggingFace model is loaded locally, run
Manifest supports querying multiple models with different schedulers. This is very much a work in progress effort, but Manifest will round robin select (or randomly select) the clients you want. You can use the same client multiple times with different connection strings (e.g. different API keys), or you can mix and match. The only requirement is that all clients are the same request type. I.e. you can't have a pool of generation models and embedding models.
To query between a local model and OpenAI,
```python
from manifest.connections.client_pool import ClientConnection
The speed benefit also comes in with async batched runs. When calling `arun_batch` with a list of prompts, Manifest supports a `chunk_size` param. This will break the prompts into `chunk_size` chunks to send across all client in the pool asynchronously. By default `chunk_size` is `-1` which means only one client will get a chunk of prompts. You must set `chunk_size > 1` to distribute across the pool. There is a further `batch_size` param which control the individual client `batch_size` to send to the model.
We support having queries and results stored in a global cache that can be shared across users. We treat inputs and outputs as key value pairs and support SQLite or Redis backends. To start with global caching using SQLite, run
By default, we do not truncate results based on a stop token. You can change this by either passing a new stop token to a Manifest session or to a `run`.
You will see the Flask session start and output a URL `http://127.0.0.1:5000`. Pass this in to Manifest. If you want to use a different port, set the `FLASK_PORT` environment variable.
To help load larger models, we also support using `parallelize()` from HF, [accelerate](https://huggingface.co/docs/accelerate/index), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), and [deepspeed](https://github.com/microsoft/DeepSpeed). You will need to install these packages first via `pip install manifest-ml[api]`. We list the commands to load larger models below.
Manifest also supports getting embeddings from models and available APIs. We do this all through changing the `client_name` argument. You still use `run` and `abatch_run`.
embedding_as_np = manifest.run("Get me an embedding for a bunny")
```
As explained above, you can load local HuggingFace models that give you embeddings, too. If you want to use a standard generative model, load the model as above use use `client_name="huggingfaceembedding"`. If you want to use a standard embedding model, like those from SentenceTransformers, load your local model via