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This commit is contained in:
Tekky
GitHub
parent
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commit
73ec30a3f0
@ -1,353 +0,0 @@
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from __future__ import annotations
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import base64, json, uuid, quickjs, random
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from curl_cffi.requests import AsyncSession
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from ..typing import Any, TypedDict
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from .base_provider import AsyncProvider
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class Vercel(AsyncProvider):
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url = "https://sdk.vercel.ai"
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working = True
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supports_gpt_35_turbo = True
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model = "replicate:replicate/llama-2-70b-chat"
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@classmethod
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async def create_async(
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cls,
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model: str,
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messages: list[dict[str, str]],
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proxy: str = None,
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**kwargs
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) -> str:
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if model in ["gpt-3.5-turbo", "gpt-4"]:
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model = "openai:" + model
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model = model if model else cls.model
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proxies = None
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if proxy:
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if "://" not in proxy:
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proxy = "http://" + proxy
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proxies = {"http": proxy, "https": proxy}
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.{rand1}.{rand2} Safari/537.36".format(
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rand1=random.randint(0,9999),
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rand2=random.randint(0,9999)
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),
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"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
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"Accept-Encoding": "gzip, deflate, br",
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"Accept-Language": "en-US,en;q=0.5",
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"TE": "trailers",
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}
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async with AsyncSession(headers=headers, proxies=proxies, impersonate="chrome107") as session:
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response = await session.get(cls.url + "/openai.jpeg")
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response.raise_for_status()
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custom_encoding = _get_custom_encoding(response.text)
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headers = {
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"Content-Type": "application/json",
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"Custom-Encoding": custom_encoding,
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}
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data = _create_payload(model, messages)
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response = await session.post(cls.url + "/api/generate", json=data, headers=headers)
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response.raise_for_status()
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return response.text
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def _create_payload(model: str, messages: list[dict[str, str]]) -> dict[str, Any]:
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if model not in model_info:
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raise ValueError(f'Model are not supported: {model}')
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default_params = model_info[model]["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
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} | default_params
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# based on https://github.com/ading2210/vercel-llm-api
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def _get_custom_encoding(text: str) -> str:
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data = json.loads(base64.b64decode(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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"replicate:replicate/llama-2-70b-chat": {
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"id": "replicate:replicate/llama-2-70b-chat",
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"default_params": {
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"temperature": 0.75,
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"maxTokens": 1000,
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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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