pipeline/models.py

import os
from dataclasses import dataclass

import pandas as pd


@dataclass
class Model(object):
    model_display_name: str
    model_name: str
    api_url: str
    provider: str
    hourly_cost: int = None
    cost: str = None
    supports_functions: str = False
    size_billion_parameters: int = None  # in billion paramters
    cost_per_million_tokens: int = None
    cost_per_million_input_tokens: int = None
    cost_per_million_output_tokens: int = None

    def __post_init__(self):
        self.cost_per_million_input_tokens = self.cost_per_million_input_tokens or self.cost_per_million_tokens
        self.cost_per_million_output_tokens = self.cost_per_million_output_tokens or self.cost_per_million_tokens
        if not self.cost and self.hourly_cost:
            self.cost = f"${self.hourly_cost} / hour"
        if not self.cost and self.cost_per_million_tokens:
            self.cost = f"${self.cost_per_million_tokens} / 1M tokens"
        elif not self.cost and self.cost_per_million_input_tokens and self.cost_per_million_output_tokens:
            self.cost = f"${self.cost_per_million_input_tokens} / 1M input tokens, ${self.cost_per_million_output_tokens} / 1M output tokens"


env = os.environ.get

MODELS = [
    # source: https://openai.com/pricing
    # converted costs from dollar/1K tokens to dollar/1M for readability and together_ai comparability
    Model(
        "gpt-3.5-turbo",
        "gpt-3.5-turbo",
        None,
        "OpenAI",
        supports_functions=True,
        cost_per_million_input_tokens=1,
        cost_per_million_output_tokens=2,
    ),
    Model(
        "gpt-4-turbo",
        "gpt-4-1106-preview",
        None,
        "OpenAI",
        supports_functions=True,
        cost_per_million_input_tokens=10,
        cost_per_million_output_tokens=30,
    ),
    Model(
        "gpt-4",
        "gpt-4",
        None,
        "OpenAI",
        supports_functions=True,
        cost_per_million_input_tokens=30,
        cost_per_million_output_tokens=60,
    ),
    # we don't test gpt-4-32k because the tasks don't reach gpt-4 limitations
    Model(
        "gpt-3.5-turbo",
        "gpt-3.5-turbo",
        None,
        "OpenAI",
        supports_functions=True,
        cost_per_million_input_tokens=1,
        cost_per_million_output_tokens=2,
    ),
    # source: https://www.together.ai/pricing
    Model(
        "llama-2-70b-chat",
        "together_ai/togethercomputer/llama-2-70b-chat",
        None,
        "Together AI",
        cost_per_million_tokens=0.2,
    ),
    Model(
        "Mixtral-8x7B-Instruct-v0.1",
        "together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1",
        None,
        "Together AI",
        size_billion_parameters=8 * 7,
        cost_per_million_tokens=0.9,
    ),
    # taken from endpoint pages
    Model(
        "zephyr-7b-beta",
        "huggingface/HuggingFaceH4/zephyr-7b-beta",
        env("ZEPHYR_7B_BETA_URL"),
        "Hugging Face Inference Endpoint",
        hourly_cost=1.30,
        size_billion_parameters=7,
    ),
    Model(
        "Mistral-7B-Instruct-v0.2",
        "huggingface/mistralai/Mistral-7B-Instruct-v0.2",
        env("MISTRAL_7B_BETA_URL"),
        "Hugging Face Inference Endpoint",
        hourly_cost=1.30,
        size_billion_parameters=7,
    ),
    Model(
        "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        "huggingface/TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        env("TINY_LLAMA_URL"),
        "Hugging Face Inference Endpoint",
        hourly_cost=0.60,
        size_billion_parameters=1.1,
    ),
    Model(
        "gemini-pro",
        "gemini-pro",
        None,
        "Google VertexAI",
        # https://ai.google.dev/pricing
        cost="$0.25 / 1M input characters, $0.5 / 1K output characters (60 queries per minute are free)",
        cost_per_million_input_tokens=0.25,
        cost_per_million_output_tokens=0.5,
    ),
    Model(
        "chat-bison",
        "chat-bison",
        None,
        "Google VertexAI",
        # https://cloud.google.com/vertex-ai/docs/generative-ai/pricing
        cost_per_million_input_tokens=0.25,
        cost_per_million_output_tokens=0.5,
    ),
    Model(
        "chat-bison-32k",
        "chat-bison-32k",
        None,
        "Google VertexAI",
        # https://cloud.google.com/vertex-ai/docs/generative-ai/pricing
        cost_per_million_input_tokens=0.25,
        cost_per_million_output_tokens=0.5,
    ),
]


def models_costs():
    return pd.DataFrame(
        [(model.model_display_name, model.provider, model.cost) for model in MODELS],
        columns=["Model", "Provider", "Cost"],
    )