Errors with Custom Local Model

[ss2342]

I am trying to build a custom Local Model Client as per the following documentation . This is how I how I have set it up:

from dsp import LM
from huggingface_hub import InferenceClient

class CustomLMClient(LM):
    def __init__(self, model_endpoint):
        self.provider = "default"
        self.client = InferenceClient(model=model_endpoint)

        self.history = []

    def basic_request(self, prompt, **kwargs):
        output_text = self.client.text_generation(prompt=prompt, **kwargs)
        self.history.append({
            "prompt": prompt,
            "response": output_text, 
            "kwargs": kwargs
        })
        return output_text

    def __call__(self, prompt, only_completed=True, return_sorted=False, **kwargs):
        response = self.basic_request(prompt, **kwargs)
        completions = [response]
        return completions

I am not using the built-in dspy.HFClientTGI as it requires a model argument which is the model name on HuggingFace. However, this model is not on the huggingface hub and is already deployed on a TGI endpoint. However, I encounter the following error when trying to use it with dspy.ChainOfThought.

This is the code I tried:

llm = CustomLMClient(model_endpoint)
dspy.configure(lm=llm)

#Example DSPy CoT QA program
qa = dspy.ChainOfThought('question -> answer')

response = qa(question="What is the capital of Paris?", max_new_tokens=20) #Prompted to llm
print(response.answer)

This is my traceback:

AttributeError                            Traceback (most recent call last)
Cell In[22], line 6
      3 #Example DSPy CoT QA program
      4 qa = dspy.ChainOfThought('question -> answer')
----> 6 response = qa(question="What is the capital of Paris?", max_new_tokens=20) #Prompted to llm
      7 print(response.answer)

File ~/env/lib/python3.10/site-packages/dspy/predict/predict.py:49, in Predict.__call__(self, **kwargs)
     48 def __call__(self, **kwargs):
---> 49     return self.forward(**kwargs)

File ~/env/lib/python3.10/site-packages/dspy/predict/chain_of_thought.py:59, in ChainOfThought.forward(self, **kwargs)
     57     signature = new_signature
     58     # template = dsp.Template(self.signature.instructions, **new_signature)
---> 59 return super().forward(signature=signature, **kwargs)

File ~/env/lib/python3.10/site-packages/dspy/predict/predict.py:64, in Predict.forward(self, **kwargs)
     62 # If temperature is 0.0 but its n > 1, set temperature to 0.7.
     63 temperature = config.get("temperature")
---> 64 temperature = lm.kwargs["temperature"] if temperature is None else temperature
     66 num_generations = config.get("n")
     67 if num_generations is None:

AttributeError: 'CustomLMClient' object has no attribute 'kwargs'

I am a bit confused since it doesn't mention in the docs to pass in kwargs to the __init__ function. I did try also doing that but that did not work either.

I am using dspy-ai==2.4.0

In addition to this, I have a model in which I have added in custom tags (similar to the [INST][/INST] for mistral instruct). How do I ensure that the optimization prompt takes these special tags into account?

[krypticmouse]

@ss2342 can you try adding the following to init and check?

self.kwargs = {
            "temperature": 0.0,
            "max_tokens": 150,
            "top_p": 1,
            "frequency_penalty": 0,
            "presence_penalty": 0,
            "n": 1,
            **kwargs,
        }

or make it like this:

from dsp import LM
from huggingface_hub import InferenceClient

class CustomLMClient(LM):
    def __init__(self, model_endpoint, **kwargs):
        self.provider = "default"
        self.client = InferenceClient(model=model_endpoint)

        self.kwargs = {
            "temperature": 0.0,
            "max_tokens": 150,
            "top_p": 1,
            "frequency_penalty": 0,
            "presence_penalty": 0,
            "n": 1,
            **kwargs,
        }

        self.history = []

    def basic_request(self, prompt, **kwargs):
        kwargs = {**self.kwargs, **kwargs}
        output_text = self.client.text_generation(prompt=prompt, **kwargs)
        self.history.append({
            "prompt": prompt,
            "response": output_text, 
            "kwargs": kwargs
        })
        return output_text

    def __call__(self, prompt, only_completed=True, return_sorted=False, **kwargs):
        response = self.basic_request(prompt, **kwargs)
        completions = [response]
        return completions

and I know you mentioned when you added it in init it gave error, can you provide the error traceback for it?

[leocnj]

another solution could be adding super().__init__(model=model) in your __init__ since LM base class already has a default kwargs inside. https://github.com/stanfordnlp/dspy/blob/4c6a2ffb6e9dd64dd6e5a587b468c695487f9136/dsp/modules/lm.py#L8

[ardakdemir]

I had the same issue would be great to update the documentations here: https://dspy-docs.vercel.app/docs/deep-dive/language_model_clients/custom-lm-client