GPT4All LangChain Community model trims model output

[Luca-Pozzi]

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Example Code

LANGCHAIN = True

model_path = "C:\\Users\\user\\path\\to\\model\\GPT4All\\Meta-Llama-3-8B-Instruct.Q4_0.gguf"

query="Please write me an exhaustive story of the history of Italy, from the foundation of Rome until today."

model_kwargs = {"max_tokens":4096,
                "temp":0.7,
                "top_p":0.4,
                "top_k":40,
                "repeat_last_n":64,
                "repeat_penalty":1.18
                }

if LANGCHAIN:
    from langchain_community.llms import GPT4All
    from langchain_core.prompts import PromptTemplate
    model = GPT4All(model=model_path,
                  device='gpu')
    call_to_model = model.invoke
else:
    from gpt4all import GPT4All
    model = GPT4All(model_path, 
                    device='gpu'
                    )
    call_to_model = model.generate
ans = call_to_model(query,
                    **model_kwargs
                    )
print(and)

Description

When I try to use Llama3 via the GPT4All. When I ask for a long answer to the model directly via the Python GPT4All SDK (i.e. LANGCHAIN = False in code), everything works as expected. In the attached file output_SDK.txt you can see a sample response with >700 words.

If I do (what I expect to be) the same calling GPT4All from LangChain, my output is limited to ~200 words. With this, I mean that the response is not just shorter, but interrupted abruptly at a certain length. You can see a sample in the attached file output_LangChain.txt.

Is this behavior expected? How can I modify it to get long answers from Llama3 via LangChain?

I tried to inspect LangChain classes LLM and BaseLLM to no avail.

System Info

• Output of pip freeze | FINDSTR langchain

langchain==0.3.1
langchain-community==0.3.1
langchain-core==0.3.6
langchain-text-splitters==0.3.0

• Platform: Windows 11
• Python version: 3.11.9

[dosubot[bot]]

Hey @Luca-Pozzi! I'm here to help you with any bugs, questions, or contributions you have.

A similar issue was discussed in this thread, where the response length was limited. The solution involves adjusting the max_tokens parameter, which defaults to 200 in LangChain's GPT4All integration ^[1].

To get longer responses, ensure you set max_tokens to a higher value when initializing the model. Here's how you can modify your code:

from langchain_community.llms import GPT4All

model = GPT4All(
    model=model_path,
    device='gpu',
    max_tokens=4096,  # Increase this value to allow for longer outputs
)

Additionally, check other parameters like n_predict, stop, and temperature, as they can also affect the output length ^[1][2].

To continue talking to Dosu, mention @dosu.

[Luca-Pozzi]

Thanks @dosu ! The answer was helpful, though inaccurate.
In langchain-community.llms.gpt4all the max_tokens is stated to regulate the length of the context rather than the output.

    max_tokens: int = Field(200, alias="max_tokens")
    """Token context window."""

Nevertheless, few lines below I have spotted

    n_predict: Optional[int] = 256
    """The maximum number of tokens to generate."""

Indeed, passing a larger value to n_predict (i.e. appending n_predict=4096 to model_kwargs in the above code) did the trick.

What confused me is that in the source of GPT4All Python SDK the same n_predict arg is said to be Equivalent to max_tokens, exists for backwards compatibility.

Should the behavior of max_tokens and n_predict be corrected in LangChain?

[dosubot[bot]]

In LangChain's GPT4All, the max_tokens parameter is indeed intended for the context window, while n_predict controls the maximum number of tokens to generate. This distinction is important, as you've discovered.

The confusion arises because in the GPT4All Python SDK, n_predict is described as equivalent to max_tokens for backward compatibility. However, in LangChain, they serve different purposes.

If the behavior in LangChain is inconsistent with the documentation or expected usage, it might be worth considering an update to clarify these parameters. This could help prevent similar confusion in the future.

To continue talking to Dosu, mention @dosu.