Why does my LLM response include extra trailing `<|endoftext|>` text?

The issue is that LM‑Studio isn’t automatically treating the <|endoftext|> token as a stop sequence in your integrated flow. In many model setups, that token acts as a sentinel indicating the end of a generated sequence, but it only triggers a cut‑off if the system (or API call) is configured to recognize it as a stop condition.

Why the Marker Isn’t Cutting Off the Response

• No Automatic Truncation: When you test directly with the model, your internal prompt structure or testing interface might be set up to ignore or remove the <|endoftext|> marker. However, in the integrated flow, LM‑Studio outputs the token as part of the full text because the stop sequence isn’t being enforced.
• Stop Sequence Configuration: If the API or integration call doesn’t provide <|endoftext|> in its list of stop tokens, the model continues generating additional text. That extra material (like the subsequent "Human: ..." text) is a continuation of the prompt rather than a separate context.

How to Prevent the Spurious Trailing Text

1. Configure Stop Tokens in LM‑Studio:
   Ensure that your integration call includes <|endoftext|> as one of the stop tokens. Depending on your setup, this might mean modifying the API parameters or the prompting options so that generation will cease once that token is encountered.

   PYTHONCopied
   # Example pseudocode (adjust based on your actual LM Studio API)
   response = lm_studio.generate(
       prompt=your_prompt,
       stop=["<|endoftext|>"],
       # Other parameters like temperature, max tokens, etc.
   )

2. Post‑Processing the Output:
   If you cannot modify the stop tokens setting or if the model still outputs extra text, implement a post‑processing step. Simply look for the <|endoftext|> marker in the returned string and trim it (and anything following) from the final message sent to Telegram.

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   # Example in Python:
   marker = "<|endoftext|>"
   if marker in response_text:
       response_text = response_text.split(marker)[0]

3. Review Prompt and Flow Configuration:
   Verify that the prompt provided in the flow exactly matches the prompt you used during testing. Sometimes, even minor differences in formatting or additional context can cause the model to generate extra text, including continuation prompts.

By ensuring that the <|endoftext|> marker is properly designated as a stop sequence either directly in the API call or via post‑processing, you can prevent the trailing text from being included in your Telegram messages.