cleanup server

1 files changed, 0 insertions, 78 deletions

diff --git a/server/continuedev/libs/llm/hf_inference_api.py b/server/continuedev/libs/llm/hf_inference_api.py
deleted file mode 100644
index 990ec7c8..00000000
--- a/server/continuedev/libs/llm/hf_inference_api.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from typing import Callable, Dict, List, Union
-
-from huggingface_hub import InferenceClient
-from pydantic import Field
-
-from .base import LLM, CompletionOptions
-from .prompts.chat import llama2_template_messages
-from .prompts.edit import simplified_edit_prompt
-
-
-class HuggingFaceInferenceAPI(LLM):
-    """
-    Hugging Face Inference API is a great option for newly released language models. Sign up for an account and add billing [here](https://huggingface.co/settings/billing), access the Inference Endpoints [here](https://ui.endpoints.huggingface.co), click on “New endpoint”, and fill out the form (e.g. select a model like [WizardCoder-Python-34B-V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0)), and then deploy your model by clicking “Create Endpoint”. Change `~/.continue/config.py` to look like this:
-
-    ```python title="~/.continue/config.py"
-    from continuedev.core.models import Models
-    from continuedev.libs.llm.hf_inference_api import HuggingFaceInferenceAPI
-
-    config = ContinueConfig(
-        ...
-        models=Models(
-            default=HuggingFaceInferenceAPI(
-                endpoint_url="<INFERENCE_API_ENDPOINT_URL>",
-                hf_token="<HUGGING_FACE_TOKEN>",
-        )
-    )
-    ```
-    """
-
-    model: str = Field(
-        "Hugging Face Inference API",
-        description="The name of the model to use (optional for the HuggingFaceInferenceAPI class)",
-    )
-    hf_token: str = Field(..., description="Your Hugging Face API token")
-    endpoint_url: str = Field(
-        None, description="Your Hugging Face Inference API endpoint URL"
-    )
-
-    template_messages: Union[
-        Callable[[List[Dict[str, str]]], str], None
-    ] = llama2_template_messages
-
-    prompt_templates = {
-        "edit": simplified_edit_prompt,
-    }
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    def collect_args(self, options: CompletionOptions):
-        options.stop = None
-        args = super().collect_args(options)
-
-        if "max_tokens" in args:
-            args["max_new_tokens"] = args["max_tokens"]
-            del args["max_tokens"]
-        if "stop" in args:
-            args["stop_sequences"] = args["stop"]
-            del args["stop"]
-
-        return args
-
-    async def _stream_complete(self, prompt, options):
-        args = self.collect_args(options)
-
-        client = InferenceClient(self.endpoint_url, token=self.hf_token)
-
-        stream = client.text_generation(prompt, stream=True, details=True, **args)
-
-        for r in stream:
-            # skip special tokens
-            if r.token.special:
-                continue
-            # stop if we encounter a stop sequence
-            if options.stop is not None:
-                if r.token.text in options.stop:
-                    break
-            yield r.token.text