1 files changed, 78 insertions, 0 deletions
diff --git a/server/continuedev/libs/llm/hf_inference_api.py b/server/continuedev/libs/llm/hf_inference_api.py
new file mode 100644
index 00000000..990ec7c8
--- /dev/null
+++ b/server/continuedev/libs/llm/hf_inference_api.py
@@ -0,0 +1,78 @@
+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