Huggingface

HuggingFaceInferenceAPI

Bases: CustomLLM

Wrapper on the Hugging Face's Inference API.

Overview of the design: - Synchronous uses InferenceClient, asynchronous uses AsyncInferenceClient - chat uses the conversational task: https://huggingface.co/tasks/conversational - complete uses the text generation task: https://huggingface.co/tasks/text-generation

Note: some models that support the text generation task can leverage Hugging Face's optimized deployment toolkit called text-generation-inference (TGI). Use InferenceClient.get_model_status to check if TGI is being used.

Relevant links: - General Docs: https://huggingface.co/docs/api-inference/index - API Docs: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client - Source: https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub/inference

Source code in llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py

@deprecated(
    "Deprecated in favor of `HuggingFaceInferenceAPI` from `llama-index-llms-huggingface-api` which should be used instead.",
    action="always",
)
class HuggingFaceInferenceAPI(CustomLLM):
    """
    Wrapper on the Hugging Face's Inference API.

    Overview of the design:
    - Synchronous uses InferenceClient, asynchronous uses AsyncInferenceClient
    - chat uses the conversational task: https://huggingface.co/tasks/conversational
    - complete uses the text generation task: https://huggingface.co/tasks/text-generation

    Note: some models that support the text generation task can leverage Hugging
    Face's optimized deployment toolkit called text-generation-inference (TGI).
    Use InferenceClient.get_model_status to check if TGI is being used.

    Relevant links:
    - General Docs: https://huggingface.co/docs/api-inference/index
    - API Docs: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client
    - Source: https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub/inference
    """

    @classmethod
    def class_name(cls) -> str:
        return "HuggingFaceInferenceAPI"

    # Corresponds with huggingface_hub.InferenceClient
    model_name: Optional[str] = Field(
        default=None,
        description=(
            "The model to run inference with. Can be a model id hosted on the Hugging"
            " Face Hub, e.g. bigcode/starcoder or a URL to a deployed Inference"
            " Endpoint. Defaults to None, in which case a recommended model is"
            " automatically selected for the task (see Field below)."
        ),
    )
    token: Union[str, bool, None] = Field(
        default=None,
        description=(
            "Hugging Face token. Will default to the locally saved token. Pass "
            "token=False if you don’t want to send your token to the server."
        ),
    )
    timeout: Optional[float] = Field(
        default=None,
        description=(
            "The maximum number of seconds to wait for a response from the server."
            " Loading a new model in Inference API can take up to several minutes."
            " Defaults to None, meaning it will loop until the server is available."
        ),
    )
    headers: Dict[str, str] = Field(
        default=None,
        description=(
            "Additional headers to send to the server. By default only the"
            " authorization and user-agent headers are sent. Values in this dictionary"
            " will override the default values."
        ),
    )
    cookies: Dict[str, str] = Field(
        default=None, description="Additional cookies to send to the server."
    )
    task: Optional[str] = Field(
        default=None,
        description=(
            "Optional task to pick Hugging Face's recommended model, used when"
            " model_name is left as default of None."
        ),
    )

    _sync_client: "InferenceClient" = PrivateAttr()
    _async_client: "AsyncInferenceClient" = PrivateAttr()
    _get_model_info: "Callable[..., ModelInfo]" = PrivateAttr()

    context_window: int = Field(
        default=DEFAULT_CONTEXT_WINDOW,
        description=(
            LLMMetadata.model_fields["context_window"].description
            + " This may be looked up in a model's `config.json`."
        ),
    )
    num_output: int = Field(
        default=DEFAULT_NUM_OUTPUTS,
        description=LLMMetadata.model_fields["num_output"].description,
    )
    is_chat_model: bool = Field(
        default=False,
        description=(
            LLMMetadata.model_fields["is_chat_model"].description
            + " Unless chat templating is intentionally applied, Hugging Face models"
            " are not chat models."
        ),
    )
    is_function_calling_model: bool = Field(
        default=False,
        description=(
            LLMMetadata.model_fields["is_function_calling_model"].description
            + " As of 10/17/2023, Hugging Face doesn't support function calling"
            " messages."
        ),
    )

    def _get_inference_client_kwargs(self) -> Dict[str, Any]:
        """Extract the Hugging Face InferenceClient construction parameters."""
        return {
            "model": self.model_name,
            "token": self.token,
            "timeout": self.timeout,
            "headers": self.headers,
            "cookies": self.cookies,
        }

    def __init__(self, **kwargs: Any) -> None:
        """Initialize.

        Args:
            kwargs: See the class-level Fields.
        """
        if kwargs.get("model_name") is None:
            task = kwargs.get("task", "")
            # NOTE: task being None or empty string leads to ValueError,
            # which ensures model is present
            kwargs["model_name"] = InferenceClient.get_recommended_model(task=task)
            logger.debug(
                f"Using Hugging Face's recommended model {kwargs['model_name']}"
                f" given task {task}."
            )
        if kwargs.get("task") is None:
            task = "conversational"
        else:
            task = kwargs["task"].lower()

        super().__init__(**kwargs)  # Populate pydantic Fields
        self._sync_client = InferenceClient(**self._get_inference_client_kwargs())
        self._async_client = AsyncInferenceClient(**self._get_inference_client_kwargs())
        self._get_model_info = model_info

    def validate_supported(self, task: str) -> None:
        """
        Confirm the contained model_name is deployed on the Inference API service.

        Args:
            task: Hugging Face task to check within. A list of all tasks can be
                found here: https://huggingface.co/tasks
        """
        all_models = self._sync_client.list_deployed_models(frameworks="all")
        try:
            if self.model_name not in all_models[task]:
                raise ValueError(
                    "The Inference API service doesn't have the model"
                    f" {self.model_name!r} deployed."
                )
        except KeyError as exc:
            raise KeyError(
                f"Input task {task!r} not in possible tasks {list(all_models.keys())}."
            ) from exc

    def get_model_info(self, **kwargs: Any) -> "ModelInfo":
        """Get metadata on the current model from Hugging Face."""
        return self._get_model_info(self.model_name, **kwargs)

    @property
    def metadata(self) -> LLMMetadata:
        return LLMMetadata(
            context_window=self.context_window,
            num_output=self.num_output,
            is_chat_model=self.is_chat_model,
            is_function_calling_model=self.is_function_calling_model,
            model_name=self.model_name,
        )

    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        # default to conversational task as that was the previous functionality
        if self.task == "conversational" or self.task is None:
            output: "ConversationalOutput" = self._sync_client.conversational(
                **{**chat_messages_to_conversational_kwargs(messages), **kwargs}
            )
            return ChatResponse(
                message=ChatMessage(
                    role=MessageRole.ASSISTANT, content=output["generated_text"]
                )
            )
        else:
            # try and use text generation
            prompt = self.messages_to_prompt(messages)
            completion = self.complete(prompt)
            return ChatResponse(
                message=ChatMessage(role=MessageRole.ASSISTANT, content=completion.text)
            )

    def complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        return CompletionResponse(
            text=self._sync_client.text_generation(
                prompt, **{**{"max_new_tokens": self.num_output}, **kwargs}
            )
        )

    def stream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseGen:
        raise NotImplementedError

    def stream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseGen:
        raise NotImplementedError

    async def achat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponse:
        raise NotImplementedError

    async def acomplete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        response = await self._async_client.text_generation(
            prompt, **{**{"max_new_tokens": self.num_output}, **kwargs}
        )
        return CompletionResponse(text=response)

    async def astream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseAsyncGen:
        raise NotImplementedError

    async def astream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseAsyncGen:
        raise NotImplementedError

validate_supported

validate_supported(task: str) -> None

Confirm the contained model_name is deployed on the Inference API service.

Parameters:

Name	Type	Description	Default
task	str	Hugging Face task to check within. A list of all tasks can be found here: https://huggingface.co/tasks	required

Source code in llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py

def validate_supported(self, task: str) -> None:
    """
    Confirm the contained model_name is deployed on the Inference API service.

    Args:
        task: Hugging Face task to check within. A list of all tasks can be
            found here: https://huggingface.co/tasks
    """
    all_models = self._sync_client.list_deployed_models(frameworks="all")
    try:
        if self.model_name not in all_models[task]:
            raise ValueError(
                "The Inference API service doesn't have the model"
                f" {self.model_name!r} deployed."
            )
    except KeyError as exc:
        raise KeyError(
            f"Input task {task!r} not in possible tasks {list(all_models.keys())}."
        ) from exc

get_model_info

get_model_info(**kwargs: Any) -> ModelInfo

Get metadata on the current model from Hugging Face.

Source code in llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py

def get_model_info(self, **kwargs: Any) -> "ModelInfo":
    """Get metadata on the current model from Hugging Face."""
    return self._get_model_info(self.model_name, **kwargs)

HuggingFaceLLM

Bases: CustomLLM

HuggingFace LLM.

Examples:

pip install llama-index-llms-huggingface

from llama_index.llms.huggingface import HuggingFaceLLM

def messages_to_prompt(messages):
    prompt = ""
    for message in messages:
        if message.role == 'system':
        prompt += f"<|system|>\n{message.content}</s>\n"
        elif message.role == 'user':
        prompt += f"<|user|>\n{message.content}</s>\n"
        elif message.role == 'assistant':
        prompt += f"<|assistant|>\n{message.content}</s>\n"

    # ensure we start with a system prompt, insert blank if needed
    if not prompt.startswith("<|system|>\n"):
        prompt = "<|system|>\n</s>\n" + prompt

    # add final assistant prompt
    prompt = prompt + "<|assistant|>\n"

    return prompt

def completion_to_prompt(completion):
    return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"

import torch
from transformers import BitsAndBytesConfig
from llama_index.core.prompts import PromptTemplate
from llama_index.llms.huggingface import HuggingFaceLLM

# quantize to save memory
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)

llm = HuggingFaceLLM(
    model_name="HuggingFaceH4/zephyr-7b-beta",
    tokenizer_name="HuggingFaceH4/zephyr-7b-beta",
    context_window=3900,
    max_new_tokens=256,
    model_kwargs={"quantization_config": quantization_config},
    generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95},
    messages_to_prompt=messages_to_prompt,
    completion_to_prompt=completion_to_prompt,
    device_map="auto",
)

response = llm.complete("What is the meaning of life?")
print(str(response))

Source code in llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py

class HuggingFaceLLM(CustomLLM):
    r"""HuggingFace LLM.

    Examples:
        `pip install llama-index-llms-huggingface`

        ```python
        from llama_index.llms.huggingface import HuggingFaceLLM

        def messages_to_prompt(messages):
            prompt = ""
            for message in messages:
                if message.role == 'system':
                prompt += f"<|system|>\n{message.content}</s>\n"
                elif message.role == 'user':
                prompt += f"<|user|>\n{message.content}</s>\n"
                elif message.role == 'assistant':
                prompt += f"<|assistant|>\n{message.content}</s>\n"

            # ensure we start with a system prompt, insert blank if needed
            if not prompt.startswith("<|system|>\n"):
                prompt = "<|system|>\n</s>\n" + prompt

            # add final assistant prompt
            prompt = prompt + "<|assistant|>\n"

            return prompt

        def completion_to_prompt(completion):
            return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"

        import torch
        from transformers import BitsAndBytesConfig
        from llama_index.core.prompts import PromptTemplate
        from llama_index.llms.huggingface import HuggingFaceLLM

        # quantize to save memory
        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_use_double_quant=True,
        )

        llm = HuggingFaceLLM(
            model_name="HuggingFaceH4/zephyr-7b-beta",
            tokenizer_name="HuggingFaceH4/zephyr-7b-beta",
            context_window=3900,
            max_new_tokens=256,
            model_kwargs={"quantization_config": quantization_config},
            generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95},
            messages_to_prompt=messages_to_prompt,
            completion_to_prompt=completion_to_prompt,
            device_map="auto",
        )

        response = llm.complete("What is the meaning of life?")
        print(str(response))
        ```
    """

    model_name: str = Field(
        default=DEFAULT_HUGGINGFACE_MODEL,
        description=(
            "The model name to use from HuggingFace. "
            "Unused if `model` is passed in directly."
        ),
    )
    context_window: int = Field(
        default=DEFAULT_CONTEXT_WINDOW,
        description="The maximum number of tokens available for input.",
        gt=0,
    )
    max_new_tokens: int = Field(
        default=DEFAULT_NUM_OUTPUTS,
        description="The maximum number of tokens to generate.",
        gt=0,
    )
    system_prompt: str = Field(
        default="",
        description=(
            "The system prompt, containing any extra instructions or context. "
            "The model card on HuggingFace should specify if this is needed."
        ),
    )
    query_wrapper_prompt: PromptTemplate = Field(
        default=PromptTemplate("{query_str}"),
        description=(
            "The query wrapper prompt, containing the query placeholder. "
            "The model card on HuggingFace should specify if this is needed. "
            "Should contain a `{query_str}` placeholder."
        ),
    )
    tokenizer_name: str = Field(
        default=DEFAULT_HUGGINGFACE_MODEL,
        description=(
            "The name of the tokenizer to use from HuggingFace. "
            "Unused if `tokenizer` is passed in directly."
        ),
    )
    device_map: str = Field(
        default="auto", description="The device_map to use. Defaults to 'auto'."
    )
    stopping_ids: List[int] = Field(
        default_factory=list,
        description=(
            "The stopping ids to use. "
            "Generation stops when these token IDs are predicted."
        ),
    )
    tokenizer_outputs_to_remove: list = Field(
        default_factory=list,
        description=(
            "The outputs to remove from the tokenizer. "
            "Sometimes huggingface tokenizers return extra inputs that cause errors."
        ),
    )
    tokenizer_kwargs: dict = Field(
        default_factory=dict, description="The kwargs to pass to the tokenizer."
    )
    model_kwargs: dict = Field(
        default_factory=dict,
        description="The kwargs to pass to the model during initialization.",
    )
    generate_kwargs: dict = Field(
        default_factory=dict,
        description="The kwargs to pass to the model during generation.",
    )
    is_chat_model: bool = Field(
        default=False,
        description=(
            LLMMetadata.model_fields["is_chat_model"].description
            + " Be sure to verify that you either pass an appropriate tokenizer "
            "that can convert prompts to properly formatted chat messages or a "
            "`messages_to_prompt` that does so."
        ),
    )

    _model: Any = PrivateAttr()
    _tokenizer: Any = PrivateAttr()
    _stopping_criteria: Any = PrivateAttr()

    def __init__(
        self,
        context_window: int = DEFAULT_CONTEXT_WINDOW,
        max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
        query_wrapper_prompt: Union[str, PromptTemplate] = "{query_str}",
        tokenizer_name: str = DEFAULT_HUGGINGFACE_MODEL,
        model_name: str = DEFAULT_HUGGINGFACE_MODEL,
        model: Optional[Any] = None,
        tokenizer: Optional[Any] = None,
        device_map: Optional[str] = "auto",
        stopping_ids: Optional[List[int]] = None,
        tokenizer_kwargs: Optional[dict] = None,
        tokenizer_outputs_to_remove: Optional[list] = None,
        model_kwargs: Optional[dict] = None,
        generate_kwargs: Optional[dict] = None,
        is_chat_model: Optional[bool] = False,
        callback_manager: Optional[CallbackManager] = None,
        system_prompt: str = "",
        messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
        completion_to_prompt: Optional[Callable[[str], str]] = None,
        pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
        output_parser: Optional[BaseOutputParser] = None,
    ) -> None:
        """Initialize params."""
        model_kwargs = model_kwargs or {}
        model = model or AutoModelForCausalLM.from_pretrained(
            model_name, device_map=device_map, **model_kwargs
        )

        # check context_window
        config_dict = model.config.to_dict()
        model_context_window = int(
            config_dict.get("max_position_embeddings", context_window)
        )
        if model_context_window and model_context_window < context_window:
            logger.warning(
                f"Supplied context_window {context_window} is greater "
                f"than the model's max input size {model_context_window}. "
                "Disable this warning by setting a lower context_window."
            )
            context_window = model_context_window

        tokenizer_kwargs = tokenizer_kwargs or {}
        if "max_length" not in tokenizer_kwargs:
            tokenizer_kwargs["max_length"] = context_window

        tokenizer = tokenizer or AutoTokenizer.from_pretrained(
            tokenizer_name, **tokenizer_kwargs
        )

        if tokenizer.name_or_path != model_name:
            logger.warning(
                f"The model `{model_name}` and tokenizer `{self._tokenizer.name_or_path}` "
                f"are different, please ensure that they are compatible."
            )

        # setup stopping criteria
        stopping_ids_list = stopping_ids or []

        class StopOnTokens(StoppingCriteria):
            def __call__(
                self,
                input_ids: torch.LongTensor,
                scores: torch.FloatTensor,
                **kwargs: Any,
            ) -> bool:
                for stop_id in stopping_ids_list:
                    if input_ids[0][-1] == stop_id:
                        return True
                return False

        stopping_criteria = StoppingCriteriaList([StopOnTokens()])

        if isinstance(query_wrapper_prompt, str):
            query_wrapper_prompt = PromptTemplate(query_wrapper_prompt)

        messages_to_prompt = messages_to_prompt or self._tokenizer_messages_to_prompt

        super().__init__(
            context_window=context_window,
            max_new_tokens=max_new_tokens,
            query_wrapper_prompt=query_wrapper_prompt,
            tokenizer_name=tokenizer_name,
            model_name=model_name,
            device_map=device_map,
            stopping_ids=stopping_ids or [],
            tokenizer_kwargs=tokenizer_kwargs or {},
            tokenizer_outputs_to_remove=tokenizer_outputs_to_remove or [],
            model_kwargs=model_kwargs or {},
            generate_kwargs=generate_kwargs or {},
            is_chat_model=is_chat_model,
            callback_manager=callback_manager,
            system_prompt=system_prompt,
            messages_to_prompt=messages_to_prompt,
            completion_to_prompt=completion_to_prompt,
            pydantic_program_mode=pydantic_program_mode,
            output_parser=output_parser,
        )

        self._model = model
        self._tokenizer = tokenizer
        self._stopping_criteria = stopping_criteria

    @classmethod
    def class_name(cls) -> str:
        return "HuggingFace_LLM"

    @property
    def metadata(self) -> LLMMetadata:
        """LLM metadata."""
        return LLMMetadata(
            context_window=self.context_window,
            num_output=self.max_new_tokens,
            model_name=self.model_name,
            is_chat_model=self.is_chat_model,
        )

    def _tokenizer_messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
        """Use the tokenizer to convert messages to prompt. Fallback to generic."""
        if hasattr(self._tokenizer, "apply_chat_template"):
            messages_dict = [
                {"role": message.role.value, "content": message.content}
                for message in messages
            ]
            return self._tokenizer.apply_chat_template(
                messages_dict, tokenize=False, add_generation_prompt=True
            )

        return generic_messages_to_prompt(messages)

    @llm_completion_callback()
    def complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        """Completion endpoint."""
        full_prompt = prompt
        if not formatted:
            if self.query_wrapper_prompt:
                full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
            if self.completion_to_prompt:
                full_prompt = self.completion_to_prompt(full_prompt)
            elif self.system_prompt:
                full_prompt = f"{self.system_prompt} {full_prompt}"

        inputs = self._tokenizer(full_prompt, return_tensors="pt")
        inputs = inputs.to(self._model.device)

        # remove keys from the tokenizer if needed, to avoid HF errors
        for key in self.tokenizer_outputs_to_remove:
            if key in inputs:
                inputs.pop(key, None)

        tokens = self._model.generate(
            **inputs,
            max_new_tokens=self.max_new_tokens,
            stopping_criteria=self._stopping_criteria,
            **self.generate_kwargs,
        )
        completion_tokens = tokens[0][inputs["input_ids"].size(1) :]
        completion = self._tokenizer.decode(completion_tokens, skip_special_tokens=True)

        return CompletionResponse(text=completion, raw={"model_output": tokens})

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseGen:
        """Streaming completion endpoint."""
        from transformers import TextIteratorStreamer

        full_prompt = prompt
        if not formatted:
            if self.query_wrapper_prompt:
                full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
            if self.system_prompt:
                full_prompt = f"{self.system_prompt} {full_prompt}"

        inputs = self._tokenizer(full_prompt, return_tensors="pt")
        inputs = inputs.to(self._model.device)

        # remove keys from the tokenizer if needed, to avoid HF errors
        for key in self.tokenizer_outputs_to_remove:
            if key in inputs:
                inputs.pop(key, None)

        streamer = TextIteratorStreamer(
            self._tokenizer, skip_prompt=True, skip_special_tokens=True
        )
        generation_kwargs = dict(
            inputs,
            streamer=streamer,
            max_new_tokens=self.max_new_tokens,
            stopping_criteria=self._stopping_criteria,
            **self.generate_kwargs,
        )

        # generate in background thread
        # NOTE/TODO: token counting doesn't work with streaming
        thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
        thread.start()

        # create generator based off of streamer
        def gen() -> CompletionResponseGen:
            text = ""
            for x in streamer:
                text += x
                yield CompletionResponse(text=text, delta=x)

        return gen()

    @llm_chat_callback()
    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        prompt = self.messages_to_prompt(messages)
        completion_response = self.complete(prompt, formatted=True, **kwargs)
        return completion_response_to_chat_response(completion_response)

    @llm_chat_callback()
    def stream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseGen:
        prompt = self.messages_to_prompt(messages)
        completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
        return stream_completion_response_to_chat_response(completion_response)

metadata property

metadata: LLMMetadata

LLM metadata.

complete

complete(prompt: str, formatted: bool = False, **kwargs: Any) -> CompletionResponse

Completion endpoint.

Source code in llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py

@llm_completion_callback()
def complete(
    self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
    """Completion endpoint."""
    full_prompt = prompt
    if not formatted:
        if self.query_wrapper_prompt:
            full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
        if self.completion_to_prompt:
            full_prompt = self.completion_to_prompt(full_prompt)
        elif self.system_prompt:
            full_prompt = f"{self.system_prompt} {full_prompt}"

    inputs = self._tokenizer(full_prompt, return_tensors="pt")
    inputs = inputs.to(self._model.device)

    # remove keys from the tokenizer if needed, to avoid HF errors
    for key in self.tokenizer_outputs_to_remove:
        if key in inputs:
            inputs.pop(key, None)

    tokens = self._model.generate(
        **inputs,
        max_new_tokens=self.max_new_tokens,
        stopping_criteria=self._stopping_criteria,
        **self.generate_kwargs,
    )
    completion_tokens = tokens[0][inputs["input_ids"].size(1) :]
    completion = self._tokenizer.decode(completion_tokens, skip_special_tokens=True)

    return CompletionResponse(text=completion, raw={"model_output": tokens})

stream_complete

stream_complete(prompt: str, formatted: bool = False, **kwargs: Any) -> CompletionResponseGen

Streaming completion endpoint.

Source code in llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py

@llm_completion_callback()
def stream_complete(
    self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
    """Streaming completion endpoint."""
    from transformers import TextIteratorStreamer

    full_prompt = prompt
    if not formatted:
        if self.query_wrapper_prompt:
            full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
        if self.system_prompt:
            full_prompt = f"{self.system_prompt} {full_prompt}"

    inputs = self._tokenizer(full_prompt, return_tensors="pt")
    inputs = inputs.to(self._model.device)

    # remove keys from the tokenizer if needed, to avoid HF errors
    for key in self.tokenizer_outputs_to_remove:
        if key in inputs:
            inputs.pop(key, None)

    streamer = TextIteratorStreamer(
        self._tokenizer, skip_prompt=True, skip_special_tokens=True
    )
    generation_kwargs = dict(
        inputs,
        streamer=streamer,
        max_new_tokens=self.max_new_tokens,
        stopping_criteria=self._stopping_criteria,
        **self.generate_kwargs,
    )

    # generate in background thread
    # NOTE/TODO: token counting doesn't work with streaming
    thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
    thread.start()

    # create generator based off of streamer
    def gen() -> CompletionResponseGen:
        text = ""
        for x in streamer:
            text += x
            yield CompletionResponse(text=text, delta=x)

    return gen()