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add support for all gpt-3.5 and gpt-4 model versions #346

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Jun 19, 2023
Merged

add support for all gpt-3.5 and gpt-4 model versions #346

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8 changes: 5 additions & 3 deletions docs/guide/llms/llms.md
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Expand Up @@ -10,10 +10,12 @@ Autolabel makes it easy to try out different LLMs for your task and this page wi
To use models from [OpenAI](https://platform.openai.com/docs/models), you can set `provider` to `openai` 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 OpenAI:

* `text-davinci-003`
* `gpt-3.5-turbo`
* `gpt-4` (8K Context)
* `gpt-3.5-turbo` and `gpt-3.5-turbo-0613` (4,096 max tokens)
* `gpt-3.5-turbo-16k` and `gpt-3.5-turbo-16k--613` (16,384 max tokens)
* `gpt-4` and `gpt-4-0613` (8,192 max tokens)
* `gpt-4-32k` and `gpt-4-32k-0613` (32,768 max tokens)

`gpt-4` (8K Context) is the most capable (and most expensive) model from OpenAI, while `gpt-3.5-turbo` is the cheapest (but still quite capable). `gpt-4` costs 15 times `gpt-3.5-turbo`, at $0.03/1K input tokens and $0.06/1K output tokens while `gpt-3.5-turbo` costs only $0.0015/1K input tokens and $0.002/1K output tokens. `text-davinci-003` model on the other hand costs $0.02/1K tokens. Detailed pricing for these models is available [here](https://openai.com/pricing).
`gpt-4` set of models are the most capable (and most expensive) from OpenAI, while `gpt-3.5-turbo` set of models are cheap (but still quite capable). Detailed pricing for these models is available [here](https://openai.com/pricing).

### Setup
To use OpenAI models with Autolabel, make sure to first install the relevant packages by running:
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2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -22,7 +22,7 @@ dependencies = [
"numpy >= 1.23.0",
"requests >= 2.27.0",
"datasets >= 2.7.0",
"langchain >= 0.0.190",
"langchain >= 0.0.194",
"nervaluate >= 0.1.8",
"pandas >= 1.3.0",
"scikit-learn >= 1.0.0",
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14 changes: 13 additions & 1 deletion src/autolabel/models/openai.py
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Expand Up @@ -16,7 +16,11 @@ class OpenAILLM(BaseModel):
"gpt-3.5-turbo",
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k",
"gpt-3.5-turbo-16k-0613",
"gpt-4",
"gpt-4-0613",
"gpt-4-32k",
"gpt-4-32k-0613",
]
MODELS_WITH_TOKEN_PROBS = ["text-curie-001", "text-davinci-003"]

Expand All @@ -39,15 +43,23 @@ class OpenAILLM(BaseModel):
"gpt-3.5-turbo": 0.0015 / 1000,
"gpt-3.5-turbo-0613": 0.0015 / 1000,
"gpt-3.5-turbo-16k": 0.003 / 1000,
"gpt-3.5-turbo-16k-0613": 0.003 / 1000,
"gpt-4": 0.03 / 1000,
"gpt-4-0613": 0.03 / 1000,
"gpt-4-32k": 0.06 / 1000,
"gpt-4-32k-0613": 0.06 / 1000,
}
COST_PER_COMPLETION_TOKEN = {
"text-davinci-003": 0.02 / 1000,
"text-curie-001": 0.002 / 1000,
"gpt-3.5-turbo": 0.002 / 1000,
"gpt-3.5-turbo-0613": 0.002 / 1000,
"gpt-3.5-turbo-16k": 0.004 / 1000,
"gpt-4": 0.06 / 1000, # $0.06 per 1000 tokens in response
"gpt-3.5-turbo-16k-0613": 0.004 / 1000,
"gpt-4": 0.06 / 1000,
"gpt-4-0613": 0.06 / 1000,
"gpt-4-32k": 0.12 / 1000,
"gpt-4-32k-0613": 0.12 / 1000,
}

@cached_property
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