Gradient

GradientBaseModelLLM

Bases: _BaseGradientLLM

基于梯度的基础模型LLM。

示例

pip install llama-index-llms-gradient

import os
from llama_index.llms.gradient import GradientBaseModelLLM

# 设置梯度访问令牌和工作空间ID
os.environ["GRADIENT_ACCESS_TOKEN"] = "{GRADIENT_ACCESS_TOKEN}"
os.environ["GRADIENT_WORKSPACE_ID"] = "{GRADIENT_WORKSPACE_ID}"

# 创建GradientBaseModelLLM的实例
llm = GradientBaseModelLLM(
    base_model_slug="llama2-7b-chat",
    max_tokens=400,
)

Source code in llama_index/llms/gradient/base.py

class GradientBaseModelLLM(_BaseGradientLLM):
    """基于梯度的基础模型LLM。

    示例:
        `pip install llama-index-llms-gradient`

        ```python
        import os
        from llama_index.llms.gradient import GradientBaseModelLLM

        # 设置梯度访问令牌和工作空间ID
        os.environ["GRADIENT_ACCESS_TOKEN"] = "{GRADIENT_ACCESS_TOKEN}"
        os.environ["GRADIENT_WORKSPACE_ID"] = "{GRADIENT_WORKSPACE_ID}"

        # 创建GradientBaseModelLLM的实例
        llm = GradientBaseModelLLM(
            base_model_slug="llama2-7b-chat",
            max_tokens=400,
        )
        ```"""

    base_model_slug: str = Field(
        description="The slug of the base model to use.",
    )

    def __init__(
        self,
        *,
        access_token: Optional[str] = None,
        base_model_slug: str,
        host: Optional[str] = None,
        max_tokens: Optional[int] = None,
        workspace_id: Optional[str] = None,
        callback_manager: Optional[CallbackManager] = None,
        is_chat_model: bool = False,
    ) -> None:
        super().__init__(
            access_token=access_token,
            base_model_slug=base_model_slug,
            host=host,
            max_tokens=max_tokens,
            workspace_id=workspace_id,
            callback_manager=callback_manager,
            is_chat_model=is_chat_model,
        )

        self._model = self._gradient.get_base_model(
            base_model_slug=base_model_slug,
        )

GradientModelAdapterLLM

Bases: _BaseGradientLLM

模型适配器 LLM 的梯度。

示例

pip install llama-index-llms-gradient

import os

os.environ["GRADIENT_ACCESS_TOKEN"] = "{GRADIENT_ACCESS_TOKEN}"
os.environ["GRADIENT_WORKSPACE_ID"] = "{GRADIENT_WORKSPACE_ID}"

from llama_index.llms.gradient import GradientModelAdapterLLM

llm = GradientModelAdapterLLM(
    model_adapter_id="{YOUR_MODEL_ADAPTER_ID}",
    max_tokens=400,
)

result = llm.complete("Can you tell me about large language models?")
print(result)

Source code in llama_index/llms/gradient/base.py

class GradientModelAdapterLLM(_BaseGradientLLM):
    """模型适配器 LLM 的梯度。

    示例:
        `pip install llama-index-llms-gradient`

        ```python
        import os

        os.environ["GRADIENT_ACCESS_TOKEN"] = "{GRADIENT_ACCESS_TOKEN}"
        os.environ["GRADIENT_WORKSPACE_ID"] = "{GRADIENT_WORKSPACE_ID}"

        from llama_index.llms.gradient import GradientModelAdapterLLM

        llm = GradientModelAdapterLLM(
            model_adapter_id="{YOUR_MODEL_ADAPTER_ID}",
            max_tokens=400,
        )

        result = llm.complete("Can you tell me about large language models?")
        print(result)
        ```"""

    model_adapter_id: str = Field(
        description="The id of the model adapter to use.",
    )

    def __init__(
        self,
        *,
        access_token: Optional[str] = None,
        host: Optional[str] = None,
        max_tokens: Optional[int] = None,
        model_adapter_id: str,
        workspace_id: Optional[str] = None,
        callback_manager: Optional[CallbackManager] = None,
        is_chat_model: bool = False,
    ) -> None:
        super().__init__(
            access_token=access_token,
            host=host,
            max_tokens=max_tokens,
            model_adapter_id=model_adapter_id,
            workspace_id=workspace_id,
            callback_manager=callback_manager,
            is_chat_model=is_chat_model,
        )
        self._model = self._gradient.get_model_adapter(
            model_adapter_id=model_adapter_id
        )