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updated available google models and anthropic (#27)
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* updated available google models and anthropic

* updated available google models and anthropic

* updated available google models and anthropic

* minot

* minor revert

* added haiku
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@vaibagra
vaibagra authored Mar 15, 2024
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44 changes: 27 additions & 17 deletions docs/autolabel/guide/llms/llms.md
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Expand Up @@ -157,10 +157,16 @@ These parameters can be passed in via the `params` dictionary under `model`. Her

To use models from [Anthropic](https://www.anthropic.com/index/introducing-claude), you can set the `provider` to `anthropic` when creating a labeling configuration. The specific model that will be queried can be specified using the `name` key. Autolabel currently supports the following models from Anthropic:

- `claude-instant-v1`
- `claude-v1`
- `claude-instant-1.2`
- `claude-2.0`
- `claude-2.1`
- `claude-3-opus-20240229`
- `claude-3-sonnet-20240229`
- `claude-3-haiku-20240307`

`claude-v1` is a state-of-the-art high-performance model, while `claude-instant-v1` is a lighter, less expensive, and much faster option. `claude-instant-v1` is ~6.7 times cheaper than `claude-v1`, at $1.63/1 million tokens. On the other hand `claude-v1` costs $11.02/1 million tokens.
`claude-3-opus-20240229` is a state-of-the-art high-performance model, while `claude-3-sonnet-20240229` is a lighter, less expensive, and much faster option. `claude-3-sonnet-20240229` is 5 times cheaper than `claude-3-opus-20240229`, at $3/1 million tokens. On the other hand `claude-3-opus-20240229` costs $15/1 million tokens. `claude-3-haiku-20240307` is the lightest and fastest model, and is 12 times cheaper than `claude-3-sonnet-20240229`, at $0.25/1 million tokens.

`claude-instant-1.2`, `claude-2.0`, and `claude-2.1` are older legacy models and are not recommended for new tasks. Detailed pricing for these models is available [here](https://www.anthropic.com/api#pricing). One may use `claude-instant-1.2` for a cheaper alternative to any model and `claude-2.0` and `claude-2.1` for a cheaper alternative to `claude-3-opus-20240229`.

### Setup

Expand Down Expand Up @@ -214,7 +220,7 @@ These parameters can be passed in via the `params` dictionary under `model`. Her
```python
"model": {
"provider": "anthropic",
"name": "claude-instant-v1",
"name": "claude-2.0",
"params": {
"max_tokens_to_sample": 512,
"temperature": 0.1
Expand Down Expand Up @@ -303,24 +309,25 @@ To use Llama 2, you can use the following model configuration:
}
```

## Google PaLM
## Google Gemini

To use models from [Google](https://developers.generativeai.google/products/palm), you can set the `provider` to `google` when creating a labeling configuration. The specific model that will be queried can be specified using the `name` key. Autolabel currently supports the following models from Google:

- `text-bison@001`
- `chat-bison@001`
- `gemini-pro`

`text-bison@001` is often more suitable for labeling tasks due to its ability to follow natural language instructions. `chat-bison@001` is fine-tuned for multi-turn conversations. `text-bison@001` costs $0.001/1K characters and `chat-bison@001` costs half that at $0.0005/1K characters. Detailed pricing for these models is available [here](https://cloud.google.com/vertex-ai/pricing#generative_ai_models)
`gemini-pro` is the most capable (and most expensive) from Google. Catch the announcement of the model [here](https://deepmind.google/technologies/gemini/#introduction).

### Setup

To use Google models with Autolabel, make sure to first install the relevant packages by running:

```bash
pip install 'refuel-autolabel[google]'
and also setting the following environment variable:

```
export GOOGLE_API_KEY=<your-google-key>
```

and also setting up [Google authentication](https://cloud.google.com/docs/authentication/application-default-credentials) locally.
replacing `<your-google-key>` with your API key, which you can get from [here](https://ai.google.dev).

### Example usage

Expand All @@ -336,7 +343,7 @@ config = {
},
"model": {
"provider": "google",
"name": "text-bison@001",
"name": "gemini-pro",
"params": {}
},
"prompt": {
Expand All @@ -353,12 +360,13 @@ A few parameters can be passed in alongside `google` models to tweak their behav
- `max_output_tokens` (int): Maximum number of tokens that can be generated in the response.
- `temperature` (float): A float between 0 and 1 which indicates the diversity you want in the output. 0 uses greedy sampling (picks the most likely outcome).

All the parameters that can be passed are given [here](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini).
These parameters can be passed in via the `params` dictionary under `model`. Here is an example:

```python
"model": {
"provider": "google",
"name": "text-bison@001",
"name": "gemini-pro",
"params": {
"max_output_tokens": 512,
"temperature": 0.1
Expand Down Expand Up @@ -454,12 +462,14 @@ The table lists out all the provider, model combinations that Autolabel supports
| openai | text-davinci-003 |
| openai | [gpt-3.5-turbo models](https://platform.openai.com/docs/models/gpt-3-5) |
| openai | [gpt-4 models](https://platform.openai.com/docs/models/gpt-4) |
| anthropic | claude-v1 |
| anthropic | claude-instant-v1 |
| anthropic | claude-instant-1.2 |
| anthropic | claude-2.0 |
| anthropic | claude-2.1 |
| anthropic | claude-opus-20240229 |
| anthropic | claude-sonnet-20240229 |
| huggingface_pipeline | [seq2seq models](https://huggingface.co/learn/nlp-course/chapter1/7?fw=pt#sequencetosequence-modelssequencetosequencemodels) and [causalLM models](https://huggingface.co/docs/transformers/tasks/language_modeling) |
| refuel | flan-t5-xxl |
| google | text-bison@001 |
| google | chat-bison@001 |
| google | gemini-pro |
| cohere | command |
| cohere | command-light |
| cohere | base |
Expand Down

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