Closed ntvuongg closed 5 months ago
🤖
Hey @ntvuongg, nice to see you diving into new adventures with Dify! How's everything going?
Based on the information you've provided and the context from the Dify repository, it seems like you want to use your custom LLM model in a self-hosted Dify instead of the existing model accessed through API.
To achieve this, you would need to modify the _invoke
method in the HuggingfaceHubLargeLanguageModel
class. This method currently uses the InferenceClient
from the huggingface_hub
package to make requests to the Hugging Face API. If you want to use your own model, you need to replace this client with your own implementation that loads and uses your model.
Here's a simplified example of how you might do this:
def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None, stream: bool = True,
user: Optional[str] = None) -> Union[LLMResult, Generator]:
# Load your custom model here
my_model = load_my_model(model)
# Convert the prompt messages to a single string
prompt = self._convert_messages_to_prompt(prompt_messages)
# Use your model to generate a response
response = my_model.generate(prompt, **model_parameters)
# Handle the response
if stream:
return self._handle_generate_stream_response(model, credentials, prompt_messages, response)
return self._handle_generate_response(model, credentials, prompt_messages, response)
In this example, load_my_model
is a function that loads your custom model, and my_model.generate
is a method that generates a response from your model. You'll need to replace these with your actual model loading and generation code.
Please note that this is a simplified example and you might need to adjust it to fit your specific needs, especially if your model's generation method requires different parameters or returns a different type of response.
You can find more details about the HuggingfaceHubLargeLanguageModel
class and its methods in the api/core/model_runtime/model_providers/huggingface_hub/llm/llm.py file.
I hope this helps! If you have any other questions or need further clarification, feel free to ask.
Please refer the page https://github.com/langgenius/dify/tree/main/api/core/model_runtime to see how to implement your own provider. Or you can use xinference and localai to deploy yours. See this https://docs.dify.ai/tutorials/model-configuration/xinference for details.
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Hi everyone, I have successfully self-hosted Dify. I want to use my custom llm model instead of using existing model which access through API. Any help?