vbatts/llama.cpp
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add tooltips to the parameters with comprehensible explanations

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Yazan Agha-Schrader 2024-05-27 23:46:18 +02:00
parent c7803876ce
commit 0f077968c0
1 changed files with 19 additions and 19 deletions
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examples/server/themes/mount-ai/index.html
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@ -559,7 +559,7 @@ function updateSystemPrompt(e) {
} }
} }
const FloatField = ({ label, max, min, name, step, value }) => { const FloatField = ({ label, title, max, min, name, step, value }) => {
return html` return html`
<div> <div>
<label for="${name}">${label}</label> <label for="${name}">${label}</label>
@ -579,7 +579,7 @@ function updateSystemPrompt(e) {
` `
}; };
const BoolField = ({ label, name, value }) => { const BoolField = ({ label, title, name, value }) => {
return html` return html`
<div> <div>
<label for="${name}">${label}</label> <label for="${name}">${label}</label>
@ -793,41 +793,41 @@ function updateSystemPrompt(e) {
${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()} ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()}
<fieldset class="params"> <fieldset class="params">
${IntField({ label: "Prediction", max: 2048, min: -1, step: 16, name: "n_predict", value: params.value.n_predict, })} ${IntField({ label: "Prediction", title: "Set the maximum number of tokens to predict when generating text. Note: May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. The value -1 means infinity. Default is 0.8", max: 2048, min: -1, step: 16, name: "n_predict", value: params.value.n_predict, })}
${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })} ${FloatField({ label: "Min-P sampling", title: "The minimum probability for a token to be considered, relative to the probability of the most likely token. Note that it's good practice to disable all other samplers aside from temperature when using min-p. It is also recommenend to go this approach. Default is 0.05 – But consider higher values like ~ 0.4 for non-English text generation. The value 1.0 means disabled", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
${FloatField({ label: "Repetition Penalty", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} ${FloatField({ label: "Repetition Penalty", title: "Control the repetition of token sequences in the generated text. Default is 1.1", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} ${FloatField({ label: "Temperature", title: "This will adjust the overall randomness of the generated text. It is the most common sampler. Default is 0.8 but consider using lower values for more factual texts or for non-English text generation", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
</fieldset> </fieldset>
<details> <details>
<summary><span class="summary-title">Further Options</span></summary> <summary><span class="summary-title">Further Options</span></summary>
<fieldset class="params"> <fieldset class="params">
${IntField({ label: "Top-K", max: 100, min: 0, step: 1, name: "top_k", value: params.value.top_k })} ${IntField({ label: "Top-K", title: "Limits the selection of the next token to the K most probable tokens. 1 means no randomness = greedy sampling. If set to 0, it means the entire vocabulary size is considered.", max: 100, min: 0, step: 1, name: "top_k", value: params.value.top_k })}
${IntField({ label: "Penalize Last N", max: 2048, min: 0, step: 16, name: "repeat_last_n", value: params.value.repeat_last_n })} ${IntField({ label: "Penalize Last N", title: "The last n tokens that are taken into account to penalise repetitions. A value of 0 means that this function is deactivated and -1 means that the entire size of the context is taken into account.", max: 2048, min: 0, step: 16, name: "repeat_last_n", value: params.value.repeat_last_n })}
${FloatField({ label: "Top-P", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} ${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Presence Penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} ${FloatField({ label: "Presence Penalty", title: "A penalty that is applied if certain tokens appear repeatedly in the generated text. A higher value leads to fewer repetitions.", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${FloatField({ label: "Frequency Penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} ${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
${FloatField({ label: "Typical-P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })} ${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
</fieldset> </fieldset>
<hr style="height: 1px; background-color: #ececf1; border: none;" /> <hr style="height: 1px; background-color: #ececf1; border: none;" />
<fieldset class="three"> <fieldset class="three">
<label><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> Mirostat off</label> <label title="The Mirostat sampling method is an algorithm used in natural language processing to improve the quality and coherence of the generated texts. It is an at-runtime-adaptive method that aims to keep the entropy or surprise of a text within a desired range."><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> Mirostat off</label>
<label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label> <label title="Mirostat version 1 was developed to adjust the probability of predictions so that the surprise in the text remains constant. This means that the algorithm tries to maintain a balance between predictable and surprising words so that the text is neither too monotonous nor too chaotic. V1 is recommended for longer writings, creative texts, etc."><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label>
<label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label> <label title="Mirostat version 2 builds on the idea of V1 but brings some improvements. V2 is recommended as a general purpose algorithm since it offers more precise control over entropy and reacts more quickly to unwanted deviations. As a result, the generated texts appear even more consistent and coherent, especially for everday life conversations."><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label>
</fieldset> </fieldset>
<fieldset class="params"> <fieldset class="params">
${FloatField({ label: "Entropy tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })} ${FloatField({ label: "Entropy tau", title: "Tau controls the desired level of entropy (or 'surprise') in the text. A low tau (e.g. 0.5) would mean that a text is very predictable, but will also be very coherent. A high tau (e.g. 8.0) would mean that the text is very creative and surprising, but may also be difficult to follow because unlikely words will occur frequently.", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })}
${FloatField({ label: "Learning-rate eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })} ${FloatField({ label: "Learning-rate eta", title: "Eta determines how quickly the Mirostat algorithm adjusts its predictions to achieve the desired entropy. A learning rate that is too high can cause the algorithm to react too quickly and possibly become unstable, because the algorithm will try to maintain a balance between surprises and precision in the context of only a few words. In this way, 'the common thread' could be lost. Whereas a learning rate that is too low means that the algorithm reacts too slowly and a red thread becomes a heavy goods train that takes a long time to come to a halt and change a 'topic station'.", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })}
</fieldset> </fieldset>
<hr style="height: 1px; background-color: #ececf1; border: none;" /> <hr style="height: 1px; background-color: #ececf1; border: none;" />
<fieldset class="params"> <fieldset class="params">
${IntField({ label: "Show Probabilities", title: "Tooltip-Text hier", max: 10, min: 0, step: 1, name: "n_probs", value: params.value.n_probs })} ${IntField({ label: "Show Probabilities", title: "If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. The tokens will be colored in gradient from green to red depending on their probabilities. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Defaults to 0", max: 10, min: 0, step: 1, name: "n_probs", title: "", value: params.value.n_probs })}
</fieldset> </fieldset>
</details> </details>
</form> </form>