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Move documentation from UI to docs/

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oobabooga 2023-09-25 12:28:28 -07:00
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docs/Generation-Parameters.md Normal file
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For a technical description of the parameters, the [transformers documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig) is a good reference.
The best presets, according to the [Preset Arena](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md) experiment, are:
* Instruction following:
1) Divine Intellect
2) Big O
3) simple-1
4) Space Alien
5) StarChat
6) Titanic
7) tfs-with-top-a
8) Asterism
9) Contrastive Search
* Chat:
1) Midnight Enigma
2) Yara
3) Shortwave
### Temperature
Primary factor to control 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.
### top_k
Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.
### 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.
### 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. Should be used with top_p, top_k, and eta_cutoff set to 0.
### eta_cutoff
In units of 1e-4; a reasonable value is 3. Should be used with top_p, top_k, and epsilon_cutoff set to 0.
### repetition_penalty
Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.
### repetition_penalty_range
The number of most recent tokens to consider for repetition penalty. 0 makes all tokens be used.
### 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.
### min_length
Minimum generation length in tokens.
### 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.

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modules/ui_parameters.py
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@ -54,55 +54,7 @@ def create_ui(default_preset):
shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
with gr.Accordion("Learn more", open=False):
gr.Markdown("""
For a technical description of the parameters, the [transformers documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig) is a good reference.
The best presets, according to the [Preset Arena](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md) experiment, are:
* Instruction following:
1) Divine Intellect
2) Big O
3) simple-1
4) Space Alien
5) StarChat
6) Titanic
7) tfs-with-top-a
8) Asterism
9) Contrastive Search
* Chat:
1) Midnight Enigma
2) Yara
3) Shortwave
### Temperature
Primary factor to control 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.
### top_k
Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.
### 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.
### 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. Should be used with top_p, top_k, and eta_cutoff set to 0.
### eta_cutoff
In units of 1e-4; a reasonable value is 3. Should be used with top_p, top_k, and epsilon_cutoff set to 0.
### repetition_penalty
Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.
### repetition_penalty_range
The number of most recent tokens to consider for repetition penalty. 0 makes all tokens be used.
### 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.
### min_length
Minimum generation length in tokens.
### 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.
""", elem_classes="markdown")
gr.Markdown("[Learn more](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Generation-Parameters.md)")
with gr.Column():
with gr.Row():