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331 lines
9.4 KiB
Python
331 lines
9.4 KiB
Python
import base64, json, uuid, quickjs
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from curl_cffi import requests
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from ..typing import Any, CreateResult, TypedDict
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from .base_provider import BaseProvider
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class Vercel(BaseProvider):
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url = "https://play.vercel.ai"
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working = True
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supports_gpt_35_turbo = True
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@staticmethod
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def create_completion(
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model: str,
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messages: list[dict[str, str]],
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stream: bool, **kwargs: Any) -> CreateResult:
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if model in ["gpt-3.5-turbo", "gpt-4"]:
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model = "openai:" + model
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yield _chat(model_id=model, messages=messages)
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def _chat(model_id: str, messages: list[dict[str, str]]) -> str:
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session = requests.Session(impersonate="chrome107")
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url = "https://sdk.vercel.ai/api/generate"
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header = _create_header(session)
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payload = _create_payload(model_id, messages)
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response = session.post(url=url, headers=header, json=payload)
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response.raise_for_status()
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return response.text
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def _create_payload(model_id: str, messages: list[dict[str, str]]) -> dict[str, Any]:
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default_params = model_info[model_id]["default_params"]
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return {
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"messages": messages,
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"playgroundId": str(uuid.uuid4()),
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"chatIndex": 0,
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"model": model_id} | default_params
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def _create_header(session: requests.Session):
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custom_encoding = _get_custom_encoding(session)
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return {"custom-encoding": custom_encoding}
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# based on https://github.com/ading2210/vercel-llm-api
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def _get_custom_encoding(session: requests.Session):
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url = "https://sdk.vercel.ai/openai.jpeg"
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response = session.get(url=url)
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data = json.loads(base64.b64decode(response.text, validate=True))
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script = """
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String.prototype.fontcolor = function() {{
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return `<font>${{this}}</font>`
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}}
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var globalThis = {{marker: "mark"}};
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({script})({key})
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""".format(
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script=data["c"], key=data["a"]
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)
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context = quickjs.Context() # type: ignore
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token_data = json.loads(context.eval(script).json()) # type: ignore
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token_data[2] = "mark"
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token = {"r": token_data, "t": data["t"]}
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token_str = json.dumps(token, separators=(",", ":")).encode("utf-16le")
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return base64.b64encode(token_str).decode()
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class ModelInfo(TypedDict):
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id: str
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default_params: dict[str, Any]
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model_info: dict[str, ModelInfo] = {
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"anthropic:claude-instant-v1": {
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"id": "anthropic:claude-instant-v1",
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"default_params": {
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"temperature": 1,
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"maxTokens": 200,
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"topP": 1,
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"topK": 1,
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"presencePenalty": 1,
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"frequencyPenalty": 1,
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"stopSequences": ["\n\nHuman:"],
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},
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},
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"anthropic:claude-v1": {
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"id": "anthropic:claude-v1",
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"default_params": {
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"temperature": 1,
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"maxTokens": 200,
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"topP": 1,
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"topK": 1,
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"presencePenalty": 1,
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"frequencyPenalty": 1,
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"stopSequences": ["\n\nHuman:"],
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},
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},
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"anthropic:claude-v2": {
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"id": "anthropic:claude-v2",
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"default_params": {
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"temperature": 1,
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"maxTokens": 200,
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"topP": 1,
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"topK": 1,
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"presencePenalty": 1,
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"frequencyPenalty": 1,
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"stopSequences": ["\n\nHuman:"],
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},
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},
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"replicate:a16z-infra/llama7b-v2-chat": {
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"id": "replicate:a16z-infra/llama7b-v2-chat",
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"default_params": {
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"temperature": 0.75,
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"maxTokens": 500,
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"topP": 1,
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"repetitionPenalty": 1,
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},
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},
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"replicate:a16z-infra/llama13b-v2-chat": {
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"id": "replicate:a16z-infra/llama13b-v2-chat",
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"default_params": {
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"temperature": 0.75,
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"maxTokens": 500,
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"topP": 1,
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"repetitionPenalty": 1,
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},
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},
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"huggingface:bigscience/bloom": {
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"id": "huggingface:bigscience/bloom",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 0.95,
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"topK": 4,
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"repetitionPenalty": 1.03,
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},
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},
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"huggingface:google/flan-t5-xxl": {
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"id": "huggingface:google/flan-t5-xxl",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 0.95,
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"topK": 4,
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"repetitionPenalty": 1.03,
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},
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},
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"huggingface:EleutherAI/gpt-neox-20b": {
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"id": "huggingface:EleutherAI/gpt-neox-20b",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 0.95,
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"topK": 4,
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"repetitionPenalty": 1.03,
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"stopSequences": [],
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},
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},
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"huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5": {
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"id": "huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
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"default_params": {"maxTokens": 200, "typicalP": 0.2, "repetitionPenalty": 1},
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},
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"huggingface:OpenAssistant/oasst-sft-1-pythia-12b": {
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"id": "huggingface:OpenAssistant/oasst-sft-1-pythia-12b",
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"default_params": {"maxTokens": 200, "typicalP": 0.2, "repetitionPenalty": 1},
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},
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"huggingface:bigcode/santacoder": {
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"id": "huggingface:bigcode/santacoder",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 0.95,
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"topK": 4,
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"repetitionPenalty": 1.03,
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},
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},
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"cohere:command-light-nightly": {
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"id": "cohere:command-light-nightly",
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"default_params": {
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"temperature": 0.9,
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"maxTokens": 200,
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"topP": 1,
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"topK": 0,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"cohere:command-nightly": {
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"id": "cohere:command-nightly",
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"default_params": {
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"temperature": 0.9,
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"maxTokens": 200,
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"topP": 1,
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"topK": 0,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"openai:gpt-4": {
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"id": "openai:gpt-4",
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"default_params": {
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"temperature": 0.7,
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"maxTokens": 500,
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"topP": 1,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"openai:gpt-4-0613": {
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"id": "openai:gpt-4-0613",
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"default_params": {
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"temperature": 0.7,
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"maxTokens": 500,
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"topP": 1,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"openai:code-davinci-002": {
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"id": "openai:code-davinci-002",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 1,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"openai:gpt-3.5-turbo": {
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"id": "openai:gpt-3.5-turbo",
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"default_params": {
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"temperature": 0.7,
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"maxTokens": 500,
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"topP": 1,
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"topK": 1,
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"presencePenalty": 1,
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"frequencyPenalty": 1,
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"stopSequences": [],
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},
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},
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"openai:gpt-3.5-turbo-16k": {
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"id": "openai:gpt-3.5-turbo-16k",
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"default_params": {
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"temperature": 0.7,
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"maxTokens": 500,
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"topP": 1,
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"topK": 1,
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"presencePenalty": 1,
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"frequencyPenalty": 1,
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"stopSequences": [],
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},
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},
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"openai:gpt-3.5-turbo-16k-0613": {
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"id": "openai:gpt-3.5-turbo-16k-0613",
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"default_params": {
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"temperature": 0.7,
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"maxTokens": 500,
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"topP": 1,
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"topK": 1,
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"presencePenalty": 1,
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"frequencyPenalty": 1,
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"stopSequences": [],
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},
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},
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"openai:text-ada-001": {
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"id": "openai:text-ada-001",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 1,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"openai:text-babbage-001": {
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"id": "openai:text-babbage-001",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 1,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"openai:text-curie-001": {
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"id": "openai:text-curie-001",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 1,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"openai:text-davinci-002": {
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"id": "openai:text-davinci-002",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 1,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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"openai:text-davinci-003": {
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"id": "openai:text-davinci-003",
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"default_params": {
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"temperature": 0.5,
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"maxTokens": 200,
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"topP": 1,
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"presencePenalty": 0,
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"frequencyPenalty": 0,
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"stopSequences": [],
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},
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},
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}
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