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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
|
