docs/03 - Parameters Tab.md

Generation

Contains parameters that control the text generation.

Quick rundown

LLMs work by generating one token at a time. Given your prompt, the model calculates the probabilities for every possible next token. The actual token generation is done after that.

• In greedy decoding, the most likely token is always picked.
• Most commonly, sampling techniques are used to choose from the next-token distribution in a more non-trivial way with the goal of improving the quality of the generated text.

Preset menu

Can be used to save and load combinations of parameters for reuse.

• 🎲 button: creates a random yet interpretable preset. Only 1 parameter of each category is included for the categories: removing tail tokens, avoiding repetition, and flattening the distribution. That is, top_p and top_k are not mixed, and neither are repetition_penalty and frequency_penalty. You can use this button to break out of a loop of bad generations after multiple "Regenerate" attempts.

Built-in presets

These were obtained after a blind contest called "Preset Arena" where hundreds of people voted. The full results can be found here.

A key takeaway is that the best presets are:

• For Instruct: Divine Intellect, Big O, simple-1.
• For Chat: Midnight Enigma, Yara, Shortwave.

The other presets are:

• Mirostat: a special decoding technique first implemented in llama.cpp and then adapted into this repository for all loaders. Many people have obtained positive results with it for chat.
• LLaMA-Precise: a legacy preset that was the default for the web UI before the Preset Arena.
• Debug-deterministic: disables sampling. It is useful for debugging, or if you intentionally want to use greedy decoding.

Parameters description

For more information about the parameters, the transformers documentation is a good reference.

• max_new_tokens: Maximum number of tokens to generate. Don't set it higher than necessary: it is used in the truncation calculation through the formula (prompt_length) = min(truncation_length - max_new_tokens, prompt_length), so your prompt will get truncated if you set it too high.
• temperature: Primary factor to control the randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.
• top_p: If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.
• min_p: Tokens with probability smaller than (min_p) * (probability of the most likely token) are discarded. This is the same as top_a but without squaring the probability.
• top_k: Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.
• repetition_penalty: Penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.
• presence_penalty: Similar to repetition_penalty, but with an additive offset on the raw token scores instead of a multiplicative factor. It may generate better results. 0 means no penalty, higher value = less repetition, lower value = more repetition. Previously called "additive_repetition_penalty".
• frequency_penalty: Repetition penalty that scales based on how many times the token has appeared in the context. Be careful with this; there's no limit to how much a token can be penalized.
• repetition_penalty_range: The number of most recent tokens to consider for repetition penalty. 0 makes all tokens be used.
• typical_p: If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.
• tfs: Tries to detect a tail of low-probability tokens in the distribution and removes those tokens. See this blog post for details. The closer to 0, the more discarded tokens.
• top_a: Tokens with probability smaller than (top_a) * (probability of the most likely token)^2 are discarded.
• epsilon_cutoff: In units of 1e-4; a reasonable value is 3. This sets a probability floor below which tokens are excluded from being sampled.
• eta_cutoff: In units of 1e-4; a reasonable value is 3. The main parameter of the special Eta Sampling technique. See this paper for a description.
• guidance_scale: The main parameter for Classifier-Free Guidance (CFG). The paper suggests that 1.5 is a good value. It can be used in conjunction with a negative prompt or not.
• Negative prompt: Only used when guidance_scale != 1. It is most useful for instruct models and custom system messages. You place your full prompt in this field with the system message replaced with the default one for the model (like "You are Llama, a helpful assistant...") to make the model pay more attention to your custom system message.
• penalty_alpha: Contrastive Search is enabled by setting this to greater than zero and unchecking "do_sample". It should be used with a low value of top_k, for instance, top_k = 4.
• mirostat_mode: Activates the Mirostat sampling technique. It aims to control perplexity during sampling. See the paper.
• mirostat_tau: No idea, see the paper for details. According to the Preset Arena, 8 is a good value.
• mirostat_eta: No idea, see the paper for details. According to the Preset Arena, 0.1 is a good value.
• dynamic_temperature: Activates Dynamic Temperature. This modifies temperature to range between "dynatemp_low" (minimum) and "dynatemp_high" (maximum), with an entropy-based scaling. The steepness of the curve is controlled by "dynatemp_exponent".
• smoothing_factor: Activates Quadratic Sampling. When 0 < smoothing_factor < 1, the logits distribution becomes flatter. When smoothing_factor > 1, it becomes more peaked.
• temperature_last: Makes temperature the last sampler instead of the first. With this, you can remove low probability tokens with a sampler like min_p and then use a high temperature to make the model creative without losing coherency. Note: this parameter takes precedence over "Sampler priority". That means that temperature/dynamic_temperature/quadratic_sampling will be removed from wherever they are and moved to the end of the stack.
• do_sample: When unchecked, sampling is entirely disabled, and greedy decoding is used instead (the most likely token is always picked).
• Seed: Set the Pytorch seed to this number. Note that some loaders do not use Pytorch (notably llama.cpp), and others are not deterministic (ExLlamaV2). For these loaders, the seed has no effect.
• encoder_repetition_penalty: Also known as the "Hallucinations filter". Used to penalize tokens that are not in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.
• no_repeat_ngram_size: If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.

To the right (or below if you are on mobile), the following parameters are present:

• Truncate the prompt up to this length: Used to prevent the prompt from getting bigger than the model's context length. In the case of the transformers loader, which allocates memory dynamically, this parameter can also be used to set a VRAM ceiling and prevent out-of-memory errors. This parameter is automatically updated with the model's context length (from "n_ctx" or "max_seq_len" for loaders that use these parameters, and from the model metadata directly for loaders that do not) when you load a model.
• Maximum number of tokens/second: to make text readable in real-time in case the model is generating too fast. Good if you want to flex and tell everyone how good your GPU is.
• Custom stopping strings: The model stops generating as soon as any of the strings set in this field is generated. Note that when generating text in the Chat tab, some default stopping strings are set regardless of this parameter, like "\nYour Name:" and "\nBot name:" for chat mode. That's why this parameter has a "Custom" in its name.
• Custom token bans: Allows you to ban the model from generating certain tokens altogether. You need to find the token IDs under "Default" > "Tokens" or "Notebook" > "Tokens", or by looking at the tokenizer.json for the model directly.
• auto_max_new_tokens: When checked, the max_new_tokens parameter is expanded in the backend to the available context length. The maximum length is given by the "truncation_length" parameter. This is useful for getting long replies in the Chat tab without having to click on "Continue" many times.
• Ban the eos_token: One of the possible tokens that a model can generate is the EOS (End of Sequence) token. When it is generated, the generation stops prematurely. When this parameter is checked, that token is banned from being generated, and the generation will always generate "max_new_tokens" tokens.
• Add the bos_token to the beginning of prompts: By default, the tokenizer will add a BOS (Beginning of Sequence) token to your prompt. During training, BOS tokens are used to separate different documents. If unchecked, no BOS token will be added, and the model will interpret your prompt as being in the middle of a document instead of at the start of one. This significantly changes the output and can make it more creative.
• Skip special tokens: When decoding the generated tokens, skip special tokens from being converted to their text representation. Otherwise, BOS appears as <s>, EOS as </s>, etc.
• Activate text streaming: When unchecked, the full response is outputted at once, without streaming the words one at a time. I recommend unchecking this parameter on high latency networks like running the webui on Google Colab or using --share.
• Sampler priority: Allows you to customize the order in which the different samplers are applied. The first sampler on the list gets applied first. With this, custom orders like top_p -> temperature -> top_k can be defined.
• Load grammar from file: Loads a GBNF grammar from a file under text-generation-webui/grammars. The output is written to the "Grammar" box below. You can also save and delete custom grammars using this menu.
• Grammar: Allows you to constrain the model output to a particular format. For instance, you can make the model generate lists, JSON, specific words, etc. Grammar is extremely powerful and I highly recommend it. The syntax looks a bit daunting at first sight, but it gets very easy once you understand it. See the GBNF Guide for details.

Character

Parameters that define the character that is used in the Chat tab when "chat" or "chat-instruct" are selected under "Mode".

• Character: A dropdown menu where you can select from saved characters, save a new character (💾 button), and delete the selected character (🗑️).
• Your name: Your name as it appears in the prompt.
• Character's name: The bot name as it appears in the prompt.
• Context: A string that is always at the top of the prompt. It never gets truncated. It usually defines the bot's personality and some key elements of the conversation.
• Greeting: An opening message for the bot. When set, it appears whenever you start a new chat.
• Character picture: A profile picture for the bot. To make it apply, you need to save the bot by clicking on 💾.
• Your picture: Your profile picture. It will be used in all conversations.

Note: the following replacements take place in the context and greeting fields when the chat prompt is generated:

• {{char}} and <BOT> get replaced with "Character's name".
• {{user}} and <USER> get replaced with "Your name".

So you can use those special placeholders in your character definitions. They are commonly found in TavernAI character cards.

Instruction template

Defines the instruction template that is used in the Chat tab when "instruct" or "chat-instruct" are selected under "Mode".

• Saved instruction templates: A dropdown menu where you can load a saved template, save a new template (💾 button), and delete the currently selected template (🗑️).
• Custom system message: A message that defines the personality of the chatbot, replacing its default "System message" string. Example: "You are a duck."
• Instruction template: A Jinja2 template that defines the prompt format for the instruction-following conversation.
• Send to default: Send the full instruction template in string format to the Default tab.
• Send to notebook: Send the full instruction template in string format to the Notebook tab.
• Send to negative prompt: Send the full instruction template in string format to the "Negative prompt" field under "Parameters" > "Generation".
• Chat template: A Jinja2 template that defines the prompt format for regular chat conversations with characters.
• Command for chat-instruct mode: The command that is used in chat-instruct mode to query the model to generate a reply on behalf of the character. Can be used creatively to generate specific kinds of responses.

Chat history

In this tab, you can download the current chat history in JSON format and upload a previously saved chat history.

When a history is uploaded, a new chat is created to hold it. That is, you don't lose your current chat in the Chat tab.

Upload character

YAML or JSON

Allows you to upload characters in the YAML format used by the web UI, including optionally a profile picture.

TavernAI PNG

Allows you to upload a TavernAI character card. It will be converted to the internal YAML format of the web UI after upload.