1 files changed, 0 insertions, 74 deletions
diff --git a/server/continuedev/libs/llm/anthropic.py b/server/continuedev/libs/llm/anthropic.py
deleted file mode 100644
index 7d0708f1..00000000
--- a/server/continuedev/libs/llm/anthropic.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from typing import Any, Callable, Coroutine
-
-from anthropic import AI_PROMPT, HUMAN_PROMPT, AsyncAnthropic
-
-from .base import LLM, CompletionOptions
-from .prompts.chat import anthropic_template_messages
-
-
-class AnthropicLLM(LLM):
-    """
-    Import the `AnthropicLLM` class and set it as the default model:
-
-    ```python title="~/.continue/config.py"
-    from continuedev.libs.llm.anthropic import AnthropicLLM
-
-    config = ContinueConfig(
-        ...
-        models=Models(
-            default=AnthropicLLM(api_key="<API_KEY>", model="claude-2")
-        )
-    )
-    ```
-
-    Claude 2 is not yet publicly released. You can request early access [here](https://www.anthropic.com/earlyaccess).
-
-    """
-
-    api_key: str
-    "Anthropic API key"
-
-    model: str = "claude-2"
-
-    _async_client: AsyncAnthropic = None
-
-    template_messages: Callable = anthropic_template_messages
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    async def start(self, **kwargs):
-        await super().start(**kwargs)
-        self._async_client = AsyncAnthropic(api_key=self.api_key)
-
-        if self.model == "claude-2":
-            self.context_length = 100_000
-
-    def collect_args(self, options: CompletionOptions):
-        options.stop = None
-        args = super().collect_args(options)
-
-        if "max_tokens" in args:
-            args["max_tokens_to_sample"] = args["max_tokens"]
-            del args["max_tokens"]
-        if "frequency_penalty" in args:
-            del args["frequency_penalty"]
-        if "presence_penalty" in args:
-            del args["presence_penalty"]
-        return args
-
-    async def _stream_complete(self, prompt: str, options):
-        args = self.collect_args(options)
-        prompt = f"{HUMAN_PROMPT} {prompt} {AI_PROMPT}"
-
-        async for chunk in await self._async_client.completions.create(
-            prompt=prompt, stream=True, **args
-        ):
-            yield chunk.completion
-
-    async def _complete(self, prompt: str, options) -> Coroutine[Any, Any, str]:
-        args = self.collect_args(options)
-        prompt = f"{HUMAN_PROMPT} {prompt} {AI_PROMPT}"
-        return (
-            await self._async_client.completions.create(prompt=prompt, **args)
-        ).completion