Introduction
This module provides prolog predicates to call the GPT API.
Large Language Models (LLM) like GPT essentially predict what text comes next, based on
learning the (latent) probabilistic relationships between text tokens. By training the
model on massive samples of text, the natural language capabilities have improved
dramatically in recent years.
However, such language models can benefit from interaction with other types of modules,
such as logic engines. To aid in developing such interactions, this library aims
to make it easy to interact with GPT directly from Prolog, using predicates that call
the GPT API.
The Prolog predicates are based on the OpenAI API Reference: https://platform.openai.com/docs/api-reference
Usage
- Create your GPT account at https://platform.openai.com
- Create an API key at https://platform.openai.com/account/api-keys and, as instructed, save that key
somewhere (e.g. in a text file in a secure folder).
- Set an environment variable called GPTKEY to the key value (don't forget in Linux that if you added the environment variable to your bash startup script, e.g. `~/.bashrc`, then you need to
source your `~/.bashrc` script to activate the new environment variable).
- Use the
prolog2gpt.pro Prolog module as usual
@author Richard de Rozario
@license MIT
- init_gptkey is semidet
- Get the GPT API Key from the environment variable named GPTKEY and create
a prolog flag (
gptkey) with the key value. Note: execute this predicate
before using any of the others, because the gpt key is needed for authorization
with each gpt api call.
Example use:
:- init_gptkey, current_prolog_flag(gptkey,Key), writeln(Key).
Key = sk-manycharactersoftheacturalkeyvalue
- gpt_models(-Models:list) is semidet
- gpt_models(-Models:json, +Raw:boolean) is semidet
- Get a list of the available GPT models
Example use:
:- gpt_models(Models).
Models = [babbage,davinci,...]
- Arguments:
-
| Models | - The list of model names, or JSON term with model details |
| Raw | - If true then Models is the raw json result, else Models is a list of model names
|
- gpt_models_detail(+Model:atom, -ModelDetails:json) is semidet
- Get the details of a particular model
Example use:
:- gpt_models('text-davinci-003',Details).
Details = ... % the JSON term
- Arguments:
-
| Model | - The model name, Note: put names that end with numeric suffixes in
single quotes, to avoid the numeric being treated as a number.
For example, use 'text-davinci-003' |
| Details | - The details of the model as a JSON term
|
- gpt_extract_data(+Group:atom, +Fielname:atom, +Data:json, -Result:list) is semidet
- Extract a list of field data from a gpt json structure. Note: this predicate
makes some simple assumptions about how GPT API result data is structured.
Example use:
:- gpt_models(Ms,true), gpt_extract_data(data,id,Ms,Models).
Models = ['babbage','text-davinci-001',...]
- Arguments:
-
| Group | - The GPT data group name. e.g. data, choices,... |
| Fieldname | - The name of the field whose data we want |
| Data | - The json data list from the GPT API, that contains one or more field values |
| Result | - The resulting list of data values |
- gpt_extract_fields(+Fieldname:atom, +Data:json, -Result:list) is semidet
- Extract a list of field data from a gpt json structure. Note: this predicate
makes some simple assumptions about how GPT API result data is structured.
Example use:
:- Data=[json([id='babbage',object='model']),json([id='text-davinci-001',object='model'])], gpt_extract_data(data,id,Data,Models).
Models = ['babbage','text-davinci-001']
- Arguments:
-
| Fieldname | - The name of the field whose data we want |
| Data | - The list with json data from the GPT API, that contains one or more field values |
| Result | - The resulting list of data values |
- gpt_extract_field_pairs(+Field1:atom, +Field2:atom, +Data:json, -Result:list) is semidet
- Extract a list of field pairs from a gpt json structure. Note: this predicate
makes some simple assumptions about how GPT API result data is structured.
Example use:
:- Data=[json([id='123',filename=file1]),json([id='345',filename=file2])], gpt_extract_field_pairs(filename,id,Data,FieldPairs).
FieldPairs = [file1-'123',file2-'345']
- Arguments:
-
| Fieldname | - The name of the field whose data we want |
| Data | - The list with json data from the GPT API, that contains one or more field values |
| Result | - The resulting list of data values |
- gpt_completions(+Model:atom, +Prompt:atom, -Result:text, +Options:list) is semidet
- gpt_completions(+Model:atom, +Prompt:atom, -Result:term, ?Raw:boolean, +Options:list) is semidet
- Get a prompted text completion from a GPT model.
Example use:
:- gpt_completions('text-davinci-003','My favourite animal is ',Result,_,[]),
Result = ['a dog']
- Arguments:
-
| Model | - The GPT model name, Note: put names that end with numeric suffixes in
single quotes, to avoid the numeric being treated as a number.
For example, use 'text-davinci-003' |
| Prompt | - The prompt that GPT will complete |
| Result | - The text result, or json term with the result from GPT |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be the text completion result |
| Options | - The model completion options as list of json pair values (see below)
Options (Note option descriptions are mostly from the GPT API reference -- see the https://platform.openai.com/docs/api-reference for up-to-date and further details):
- suffix=S
- A string (S) that is inserted after the completion
- max_tokens=M
- The size of output, where M is a natural number (incl. 0).
GPT-3 can theoretically return up to 4096 tokens, but in practice less than half that.
One token is about 4 characters or 0.75 average word length. Defaults to 16.
- temperature=N
- Controls "randomness" of output, with `0<=N<=2`. Defaults to 1.
Higher temperature means text will be more diverse, but
also risks more grammar mistakes and nonsense. Recommended to
change either this or
top_p, but not both.
- top_p
- An alternative to sampling with
temperature, called
nucleus sampling, where the model considers the results
of the tokens with top_p probability mass. So 0.1 means
only the tokens comprising the top 10% probability mass are
considered. Use this, or temperature, but not both.
Defaults to 1.
- n=N
- The number of completions (e.g. Results) to generate for each
prompt. Defaults to 1.
- stream=TF
- If
true then tokens will be sent as data-only
`server-sent events` as they become available, with the
stream terminated by a data: [DONE] message.
Defaults to false
- logprobs=N
- Include the log probabilities on most likely tokens. For
example, if
logprobs=5, the API will return a list of
the 5 most likely tokens. The API will always return the
log probability of the sampled token, so there may be up
to logprobs+1 elements in the response. Defaults to 0.
- echo=TF
- If
true, echo back the prompt in addition to
the completion. Defaults to false
- stop=S
- (string or list of strings). Up to 4 strings ("sequences")
where the API will stop generating further tokens. The
returned text will not contain the stop sequences. Defaults
to
null
- presence_penalty=N
- Number between -2.0 and 2.0. Positive values penalize new
tokens based on whether they appear in the text so far,
increase the model's likelihood to talk about new topics.
Defaults to 0.
- frequency_penalty=N
- Number between -2.0 and 2.0. Positive values penalize new
tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line
verbatim. Defaults to 0.
- best_of=N
- Generates best_of completions server-side and returns the "best"
(the one with the highest log probability per token). Results cannot be streamed.
When used with n, best_of controls the number of candidate completions
and n specifies how many to return â best_of must be greater than n.
Note: Because this parameter generates many completions, it can quickly consume
your token quota. Use carefully and ensure that you have reasonable settings for
max_tokens and stop.
- logit_bias=JSON_TERM
- Modify the likelihood of specified tokens appearing in the completion.
Accepts a json object that maps tokens (specified by their token ID in the
GPT tokenizer) to an associated bias value from -100 to 100. You can use this
tokenizer tool (which works for both GPT-2 and GPT-3) to convert text to token IDs.
Mathematically, the bias is added to the logits generated by the model prior to sampling.
The exact effect will vary per model, but values between -1 and 1 should decrease or
increase likelihood of selection; values like -100 or 100 should result in a ban or
exclusive selection of the relevant token.
As an example, you can pass json('50256': -100) to prevent the <|endoftext|> token
from being generated.
- user=S
- A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
|
- gpt_edits(+Model:atom, +Instruction:atom, -Result:text, +Options:list) is semidet
- gpt_edits(+Model:atom, +Instruction:atom, -Result:term, ?Raw:boolean, +Options:list) is semidet
- Get a new edit for a given model, input and instruction.
Note: Only for the 'text-davinci-edit-001' or 'code-davinci-edit-001' models.
Example use:
:- gpt_edit('text-davinci-001','Fix the spelling mistakes',Result,_,
[ input='What day of the wek is it?'
]),
Result = 'What day of the week is it?'
- Arguments:
-
| Model | - The GPT model name, Note: put names that end with numeric suffixes in
single quotes, to avoid the numeric being treated as a number.
For example, use 'text-davinci-003' |
| Instruction | - The natural language editing instruction. |
| Result | - The text result, or json term with the result from GPT |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be the (first) text result |
| Options | - The edit options as list of json pair values (see below)
Options (Note option descriptions are mostly from the GPT API reference -- see the https://platform.openai.com/docs/api-reference for up-to-date and further details):
- input=S
- An atom of text (S) that the model needs to edit. Default=''
- max_tokens=M
- The size of output, where M is a natural number (incl. 0).
GPT-3 can theoretically return up to 4096 tokens, but in practice less than half that.
One token is about 4 characters or 0.75 average word length. Defaults to 16.
- n=N
- The number of completions (e.g. Results) to generate for each
prompt. Defaults to 1.
- temperature=N
- Controls "randomness" of output, with `0<=N<=2`. Defaults to 1.
Higher temperature means text will be more diverse, but
also risks more grammar mistakes and nonsense. Recommended to
change either this or
top_p, but not both.
- top_p
- An alternative to sampling with
temperature, called
nucleus sampling, where the model considers the results
of the tokens with top_p probability mass. So 0.1 means
only the tokens comprising the top 10% probability mass are
considered. Use this, or temperature, but not both.
Defaults to 1.
|
- gpt_images_create(+Prompt:atom, -Result:term, +Options:list) is semidet
- gpt_images_create(+Prompt:atom, -Result:term, ?Raw:boolean, +Options:list) is semidet
- Create an image from a text prompt.
Example use:
:- gpt_images_create('A cute baby sea otter',Result,_,[]),
Result = ['https://...'] % url of the resulting image
- Arguments:
-
| Prompt | - The prompt that GPT will complete |
| Result | - The text result, or json term with the result from GPT |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be the (first) url or b64 result |
| Options | - The edit options as list of json pair values (see below)
Options (Note option descriptions are mostly from the GPT API reference -- see the https://platform.openai.com/docs/api-reference for up-to-date and further details):
- n=N
- The number of images to generate. Defaults to 1.
- size=Z
- The size of the image. Must be one of
'256x256', '512x512', or '1024x1024'.
Default is '1024x1024'
- response_format=S
- The format of the generated images. Must be one of
url or b64_json. Default is url
- user=S
- A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
|
- gpt_images_variations(+File:atom, -Result:term, +Options:list) is semidet
- gpt_images_variations(+File:atom, -Result:term, ?Raw:boolean, +Options:list) is semidet
- Produce
variation(s) of an image.
Example use:
:- gpt_images_variations('./test/otter.png',Result,_,[]),
Result = ['https://...'] % url of the resulting image
- Arguments:
-
| Image | - The path/filename of image to vary |
| Result | - The text result, or json term with the result from GPT |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be the (first) url or b64 result |
| Options | - The edit options as list of pair values (see below)
Options (Note option descriptions are mostly from the GPT API reference -- see the https://platform.openai.com/docs/api-reference for up-to-date and further details):
- n=N
- The number of images to generate. Defaults to 1.
- size=Z
- The size of the image. Must be one of
'256x256', '512x512', or '1024x1024'.
Default is '1024x1024'
- response_format=S
- The format of the generated images. Must be one of
url or b64_json. Default is url
- user=S
- A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
|
- gpt_files(-Result:list) is semidet
- gpt_files(-Result:list, +Raw:boolean) is semidet
- List all files that belong to the user's organization.
Example use:
:- gpt_files(Result),
Result = ['file1.jsonl'-'file-12345','file2.jsonl'-'file-56789']
- Arguments:
-
| Result | - List of Filename-ID pairs, or json term (depending on Raw) |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be a simple list of file names |
- gpt_files_delete(+FileID:atom, +Purpose:text, -Result:list) is semidet
- gpt_files_delete(+FileID:atom, +Purpose:text, -Result:list, +Raw:boolean) is semidet
- Delete a (user) file from GPT storage
Example use:
:- gpt_files_delete('file-XjGxS3KTG0uNmNOK362iJua3',Result),
Result = ['file-XjGxS3KTG0uNmNOK362iJua3']
- Arguments:
-
| FileID | - File ID of file in GPT storage to delete |
| Purpose | - Purpose of the file. Currently only 'fine-tune' |
| Result | - List of file names, or json term (depending on Raw) |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be a simple list of file names |
- gpt_files_retrieve(+FileID:atom, -Result:list) is semidet
- gpt_files_retrieve(+FileID:atom, -Result:list, +Raw:boolean) is semidet
- Retrieve a (user) file details
Example use:
:- gpt_files_retrieve('file-XjGxS3KTG0uNmNOK362iJua3',Result),
Result = ['myfile.jsonl']
- Arguments:
-
| FileID | - File ID of file in GPT storage to retrieve |
| Result | - List with file name, or json term (depending on Raw) |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be a simple list of file names |
- gpt_files_retrieve_content(+FileID:atom, +Purpose:text, -Result:list) is semidet
- gpt_files_retrieve(+FileID:atom, +Purpose:text, -Result:list, +Raw:boolean) is semidet
- Retrieve a (user) file details
Example use:
:- gpt_files_retrieve('file-XjGxS3KTG0uNmNOK362iJua3',Result),
Result = ['myfile.jsonl']
- Arguments:
-
| FileID | - File ID of file in GPT storage to retrieve |
| Result | - List with file name, or json term (depending on Raw) |
|
Raw If `true` the Result will be the json term, if `false` (default)
the Result will be a simple list of file names
TODO: ***** this API doesn't work for some reason *****
| - |
- gpt_fine_tunes(+TrainingFile:text, -Result:list) is semidet
- gpt_fine_tunes(+TrainingFile:text, -Result:list, +Raw:boolean) is semidet
- Get a vector representation of a given TrainingFile that can be easily consumed by machine learning models and algorithms.
Example use:
:- gpt_fine_tunes('file-XGinujblHPwGLSztz8cPS8XY',Result),
Result = ['ft-AF1WoRqd3aJAHsqc9NY7iL8F']
- Arguments:
-
| TrainingFile | - Atom with the GPT file ID of an uploaded file |
| Result | - Fine-tuned request event in list, or json term of details (depending on Raw) |
|
Raw If `true` the Result will be the json term, if `false` (default)
the Result will be a simple list of file names
Options (Note option descriptions are mostly from the GPT API reference -- see the https://platform.openai.com/docs/api-reference for up-to-date and further details):
| -
- validation_file=F
- The ID of an uploaded file that contains validation data.
If you provide this file, the data is used to generate validation
metrics periodically during fine-tuning. These metrics can be viewed
in the fine-tuning results file. Your train and validation data should
be mutually exclusive.
Your dataset must be formatted as a JSONL file, where each validation
example is a JSON object with the keys "prompt" and "completion".
Additionally, you must upload your file with the purpose fine-tune.
- model=M
- The name of the base model to fine-tune. You can select one of 'ada',
'babbage', 'curie', 'davinci', or a fine-tuned model created after
2022-04-21. To learn more about these models, see the Models documentation.
Defaults to 'curie'.
- n_epochs=N
- The number of epochs to train the model for. An epoch refers to one full
cycle through the training dataset. Defaults to 4.
- batch_size=N
- The batch size to use for training. The batch size is the number of
training examples used to train a single forward and backward pass.
By default, the batch size will be dynamically configured to be ~0.2%
of the number of examples in the training set, capped at 256 - in
general, we've found that larger batch sizes tend to work better for
larger datasets. Defaults to null.
- learning_rate_multiplier=N
- The learning rate multiplier to use for training. The fine-tuning
learning rate is the original learning rate used for pretraining
multiplied by this value.
By default, the learning rate multiplier is the 0.05, 0.1, or 0.2
depending on final batch_size (larger learning rates tend to perform
better with larger batch sizes). We recommend experimenting with
values in the range 0.02 to 0.2 to see what produces the best results.
Defaults to null.
- prompt_loss_weight=N
- The weight to use for loss on the prompt tokens. This controls how
much the model tries to learn to generate the prompt (as compared to
the completion which always has a weight of 1.0), and can add a
stabilizing effect to training when completions are short.
If prompts are extremely long (relative to completions), it may make
sense to reduce this weight so as to avoid over-prioritizing learning
the prompt. Defaults to 0.01
- compute_classification_metrics=B
- If set, we calculate classification-specific metrics such as accuracy
and F-1 score using the validation set at the end of every epoch.
These metrics can be viewed in the results file.
In order to compute classification metrics, you must provide a
validation_file. Additionally, you must specify classification_n_classes
for multiclass classification or classification_positive_class for
binary classification. Defaults to false
- classification_n_classes=N
- The number of classes in a classification task. This parameter is
required for multiclass classification. Defaults to
null.
- classification_positive_class=S
- The positive class in binary classification. This parameter is needed
to generate precision, recall, and F1 metrics when doing binary
classification. Defaults to
null.
- classification_betas=List
- If this is provided, we calculate F-beta scores at the specified beta
values. The F-beta score is a generalization of F-1 score. This is only
used for binary classification.
With a beta of 1 (i.e. the F-1 score), precision and recall are given
the same weight. A larger beta score puts more weight on recall and
less on precision. A smaller beta score puts more weight on precision
and less on recall. Defaults to null.
- suffix=S
- A string of up to 40 characters that will be added to your fine-tuned
model name. For example, a suffix of "custom-model-name" would produce
a model name like
ada:ft-your-org:custom-model-name-2022-02-15-04-21-04.
|
- gpt_fine_tunes(-Result:list) is semidet
- gpt_fine_tunes(-Result:list, +Raw:boolean) is semidet
- Gets a list of fine-tunes jobs.
Example use:
:- gpt_fine-tunes(Result),
Result = ['curie:ft-personal-2022-02-15-04-21-04'-'ft-090asf0asf0',...]
- Arguments:
-
| Result | - List with file name, or json term (depending on Raw) |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be a simple list of file names |
- gpt_fine_tunes_detail(+ID:atom, -Result:list) is semidet
- gpt_fine_tunes_detail(+ID:atom, -Result:list, +Raw:boolean) is semidet
- Gets details of a fine-tunes job.
Example use:
:- gpt_fine_tunes_detail('ft-090asf0asf0',Result),
Result = ['curie:ft-personal-2022-02-15-04-21-04']
- Arguments:
-
| Result | - List with file name, or json term (depending on Raw) |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be a simple list of file names |
- gpt_fine_tunes_cancel(+ID:atom, -Result:list) is semidet
- gpt_fine_tunes_cancel(+ID:atom, -Result:list, +Raw:boolean) is semidet
- Cancel a fine-tunes job.
Example use:
:- gpt_fine_tunes_cancel([_-ID]),(ID,Result),
Result = ['curie:ft-personal-2022-02-15-04-21-04']
- Arguments:
-
| ID | - ID of the fine-tunes job |
| Result | - List with file name, or json term (depending on Raw) |
|
Raw If `true` the Result will be the json term, if `false` (default)
the Result will be a simple list of file names
TODO: ***** DOES NOT WORK **** something to do with post without data?
| - |
- gpt_fine_tunes_events(+ID:atom, -Result:list) is semidet
- gpt_fine_tunes_events(+ID:atom, -Result:list, +Raw:boolean) is semidet
- List events of a fine-tunes job.
Example use:
:- gpt_fine_tunes_events([_-ID]),(ID,Result),
Result = ['curie:ft-personal-2022-02-15-04-21-04']
- Arguments:
-
| ID | - ID of the fine-tunes job |
| Result | - List with file name, or json term (depending on Raw) |
|
Raw If `true` the Result will be the json term, if `false` (default)
the Result will be a simple list of file names
TODO: ***** DOES NOT WORK **** something to do with post without data?
| - |
- gpt_fine_tunes_delete(+ID:atom, -Result:list) is semidet
- gpt_fine_tunes_delete(+ID:atom, -Result:list, +Raw:boolean) is semidet
- Delete a fine-tunes job from GPT storage
Example use:
:- gpt_fine_tunes([_-ID]),gpt_fine_tunes_delete(ID,Result),
Result = ['ft-XjGxS3KTG0uNmNOK362iJua3']
- Arguments:
-
| ID | - File ID of file in GPT storage to delete |
| Purpose | - Purpose of the file. Currently only 'fine-tune' |
| Result | - List of file names, or json term (depending on Raw) |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be a simple list of file names |
- gpt_moderations(+Model:atom, +Input:text, -Result:list, +Options:list) is semidet
- Given a input text, outputs if the model classifies it as violating OpenAI's content policy.
Example use:
:- gpt_moderations('I want to kill them',Result),
Result = [sexual=false, hate=false, violence=true, 'self-harm'=false,
'sexual/minors'=false, 'hate/threatening'=false, 'violence/graphic'=false].
- Arguments:
-
| Input | - Text to test for content policy violation |
| Result | - JSON structure with policy scores |
- gpt_images_edits(+Prompt:atom, -Result:term, +Options:list) is semidet
- gpt_images_edits(+Prompt:atom, -Result:term, ?Raw:boolean, +Options:list) is semidet
- Modify an image from a text prompt.
Example use:
:- gpt_images_edits('A cute baby sea otter with a hat','./test/otter.png',Result,_,[]),
Result = ['https://...'] % url of the resulting image
- Arguments:
-
| Prompt | - The prompt that GPT will complete |
| File | - The path/filename of the image to edit |
| Result | - The text result, or json term with the result from GPT |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be the (first) url or b64 result |
| Options | - The edit options as list of pair values (see below)
Options (Note option descriptions are mostly from the GPT API reference -- see the https://platform.openai.com/docs/api-reference for up-to-date and further details):
- n=N
- The number of images to generate. Defaults to 1.
- size=Z
- The size of the image. Must be one of
'256x256', '512x512', or '1024x1024'.
Default is '1024x1024'
- mask
- An additional image whose fully transparent areas (e.g. where alpha is zero)
indicate where image should be edited. Must be a valid
PNG file, less than 4MB, and have the same dimensions as image.
- response_format=S
- The format of the generated images. Must be one of
url or b64_json. Default is url
- user=S
- A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
|
- gpt_files_upload(+File:atom, +Purpose:text, -Result:list) is semidet
- gpt_files_upload(+File:atom, +Purpose:text, -Result:list, +Raw:boolean) is semidet
- Upload a JSON Lines file (typically for fine-tuning)
Example use:
:- gpt_files_upload('./test/tune_answer.jsonl','fine-tune',Result),
Result = ['file-XjGxS3KTG0uNmNOK362iJua3']
- Arguments:
-
| File | - Filename to upload |
| Purpose | - Purpose of the file. Currently only 'fine-tune' |
| Result | - List of file names, or json term (depending on Raw) |
| Raw | - If true the Result will be the json term, if false (default)
the Result will be a simple list of file names |
Undocumented predicates
The following predicates are exported, but not or incorrectly documented.
- gpt_models(Arg1, Arg2)
- gpt_completions(Arg1, Arg2, Arg3, Arg4, Arg5)
- gpt_edits(Arg1, Arg2, Arg3, Arg4, Arg5)
- gpt_images_create(Arg1, Arg2, Arg3, Arg4)
- gpt_images_edits(Arg1, Arg2, Arg3, Arg4, Arg5)
- gpt_images_variations(Arg1, Arg2, Arg3, Arg4)
- gpt_embeddings(Arg1, Arg2, Arg3, Arg4)
- gpt_embeddings(Arg1, Arg2, Arg3, Arg4, Arg5)
- gpt_files(Arg1, Arg2)
- gpt_files_upload(Arg1, Arg2, Arg3, Arg4, Arg5)
- gpt_files_delete(Arg1, Arg2)
- gpt_files_retrieve(Arg1, Arg2, Arg3)
- gpt_files_retrieve_content(Arg1, Arg2)
- gpt_fine_tunes(Arg1, Arg2, Arg3)
- gpt_fine_tunes(Arg1, Arg2, Arg3, Arg4)
- gpt_fine_tunes_detail(Arg1, Arg2, Arg3)
- gpt_fine_tunes_cancel(Arg1, Arg2, Arg3)
- gpt_fine_tunes_events(Arg1, Arg2, Arg3)
- gpt_fine_tunes_delete(Arg1, Arg2, Arg3)
- gpt_moderations(Arg1, Arg2, Arg3)
