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<h1 id="tricks-for-faq">Tricks for FAQ</h1>
<p>Sometimes, your application with LLM and LLamaSharp may have strange behaviors. Before opening an issue to report the BUG, the following tricks may worth a try.</p>
<h2 id="carefully-set-the-anti-prompts">Carefully set the anti-prompts</h2>
<p>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 behaviors. For example, the prompt file <code>chat-with-bob.txt</code> has the following content:</p>
<pre><code>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:
</code></pre>
<p>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.</p>
<p>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 behavior, which depends on the implementation inside the <code>LLamaExecutor</code>. Anyway, since it may leads to unexpected behaviors, it's recommended to trim your prompt or carefully keep consistent with your anti-prompt.</p>
<h2 id="pay-attention-to-the-length-of-prompt">Pay attention to the length of prompt</h2>
<p>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.</p>
<p>$$ len(prompt) + len(response) &lt; len(context) $$</p>
<p>In this inequality, <code>len(response)</code> refers to the expected tokens for LLM to generate.</p>
<h2 id="try-differenct-executors-with-a-prompt">Try differenct executors with a prompt</h2>
<p>Some prompt works well under interactive mode, such as <code>chat-with-bob</code>, some others may work well with instruct mode, such as <code>alpaca</code>. 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.</p>
<p>If your chat bot has bad performance, trying different executor will possibly make it work well.</p>
<h2 id="choose-models-weight-depending-on-you-task">Choose models weight depending on you task</h2>
<p>The differences between modes may lead to much different behaviors 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.</p>
<h2 id="set-the-layer-count-you-want-to-offload-to-gpu">Set the layer count you want to offload to GPU</h2>
<p>Currently, the <code>GpuLayerCount</code> param, 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.</p>


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