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Tricks for FAQ
Sometimes, your application with LLM and LLamaSharp may have strange behaviours. Before opening an issue to report the BUG, the following tricks may worth a try.
Carefully set the anti-prompts
Anti-prompt can also be called as "Stop-keyword", which decides when to stop the response generation. Under interactive mode, the maximum tokens count is always not set, which makes the LLM generates responses infinitively. Therefore, setting anti-prompt correctly helps a lot to avoid the strange behaviours. For example, the prompt file chat-with-bob.txt has the following content:
Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
User: Hello, Bob.
Bob: Hello. How may I help you today?
User: Please tell me the largest city in Europe.
Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
User:
Therefore, the anti-prompt should be set as "User:". If the last line of the prompt is removed, LLM will automatically generate a question (user) and a response (bob) for one time when running the chat session. Therefore, the antiprompt is suggested to be appended to the prompt when starting a chat session.
What if an extra line is appended? The string "User:" in the prompt will be followed with a char "\n". Thus when running the model, the automatic generation of a pair of question and response may appear because the anti-prompt is "User:" but the last token is "User:\n". As for whether it will appear, it's an undefined behaviour, which depends on the implementation inside the LLamaExecutor. Anyway, since it may leads to unexpected behaviors, it's recommended to trim your prompt or carefully keep consistent with your anti-prompt.
Pay attention to the length of prompt
Sometimes we want to input a long prompt to execute a task. However, the context size may limit the inference of LLama model. Please ensure the inequality below holds.
len(prompt) + len(response) < len(context) In this inequality, len(response) refers to the expected tokens for LLM to generate.
Try different executors with a prompt
Some prompt works well under interactive mode, such as chat-with-bob, some others may work well with instruct mode, such as alpaca. Besides, if your input is quite simple and one-time job, such as "Q: what is the satellite of the earth? A: ", stateless mode will be a good choice.
If your chat bot has bad performance, trying different executor will possibly make it work well.
Choose models weight depending on you task
The differences between modes may lead to much different behaviours under the same task. For example, if you're building a chat bot with non-English, a fine-tuned model specially for the language you want to use will have huge effect on the performance.
Set the layer count you want to offload to GPU
Currently, the GpuLayerCount parameter, which decides the number of layer loaded into GPU, is set to 20 by default. However, if you have some efficient GPUs, setting it as a larger number will attain faster inference.