diff --git a/docs/guides/integrations/other/openai-api.md b/docs/guides/integrations/other/openai-api.md
new file mode 100644
index 0000000000..5b099dbfc2
--- /dev/null
+++ b/docs/guides/integrations/other/openai-api.md
@@ -0,0 +1,68 @@
+---
+slug: /guides/integrations/openai-api
+description: How to use W&B with the OpenAI API.
+displayed_sidebar: default
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# OpenAI API
+
+Use the Weights & Biases OpenAI API integration to log requests, responses, token counts and model metadata with 1 line of code for all OpenAI models, including fine-tuned models.
+
+**[Try in a Colab Notebook here →](https://github.com/wandb/examples/blob/master/colabs/openai/OpenAI_API_Autologger_Quickstart.ipynb)**
+
+With just 1 line of code you can now automatically log inputs and outputs from the OpenAI Python SDK to Weights & Biases!
+
+
+
+Once you start logging your API inputs and outputs you can quickly evaluate the performance of difference prompts, compare different model settings (such as temperature), and track other usage metrics such as token usage.
+
+To get started, pip install the `wandb` library, then follow the steps below:
+
+### 1. Import autolog and initialise it
+First, import `autolog` from `wandb.integration.openai` and initialise it.
+
+```python
+import os
+import openai
+from wandb.integration.openai import autolog
+
+autolog({"project":"gpt5"})
+```
+
+You can optionally pass a dictionary with argument that `wandb.init()` accepts to `autolog`. This includes a project name, team name, entity, and more. For more information about [`wandb.init`](../../../ref/python/init.md), see the API Reference Guide.
+
+### 2. Call the OpenAI API
+Each call you make to the OpenAI API will now be logged to Weights & Biases automatically.
+
+```python
+os.environ["OPENAI_API_KEY"] = "XXX"
+
+chat_request_kwargs = dict(
+ model="gpt-3.5-turbo",
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Who won the world series in 2020?"},
+ {"role": "assistant", "content": "The Los Angeles Dodgers"},
+ {"role": "user", "content": "Where was it played?"},
+ ],
+)
+response = openai.ChatCompletion.create(**chat_request_kwargs)
+```
+
+### 3. View your OpenAI API inputs and responses
+
+Click on the Weights & Biases [run](../../runs/intro.md) link generated by `autolog` in **step 1**. This will redirect you to your project workspace in the W&B App.
+
+Select a run you created to view the trace table, trace timeline and the model architecture of the OpenAI LLM used.
+
+### 4. Disable autolog
+We recommend that you call `disable()` to close all W&B processes when you are finished using the OpenAI API.
+
+```python
+autolog.disable()
+```
+
+Now your inputs and completions will be logged to Weights & Biases, ready for analysis or to be shared with colleagues.
\ No newline at end of file
diff --git a/docs/guides/integrations/other/openai-fine-tuning.md b/docs/guides/integrations/other/openai-fine-tuning.md
new file mode 100644
index 0000000000..e994795a11
--- /dev/null
+++ b/docs/guides/integrations/other/openai-fine-tuning.md
@@ -0,0 +1,212 @@
+---
+slug: /guides/integrations/openai
+description: How to Fine-Tune OpenAI models using W&B.
+displayed_sidebar: default
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# OpenAI Fine-Tuning
+
+With Weights & Biases you can log your OpenAI ChatGPT-3.5 or GPT-4 model's fine-tuning metrics and configuration to Weights & Biases to analyse and understand the performance of your newly fine-tuned models and share the results with your colleagues.
+
+## Sync your OpenAI Fine-Tuning Results in 1 Line
+
+If you use OpenAI's API to [fine-tune OpenAI models](https://platform.openai.com/docs/guides/fine-tuning/), you can now use the W&B integration to track experiments, models, and datasets in your central dashboard.
+
+
+
+
+```shell-session
+openai wandb sync
+```
+
+
+
+
+```python
+from openai.wandb_logger import WandbLogger
+
+WandbLogger.sync(project="OpenAI-Fine-Tune")
+```
+
+
+
+
+
+
+### Check out interactive examples
+
+* [Demo Colab](http://wandb.me/openai-colab)
+* [Report - OpenAI Fine-Tuning Exploration and Tips](http://wandb.me/openai-report)
+
+### Sync your fine-tunes with one line
+
+Make sure you are using latest version of openai and wandb.
+
+```shell-session
+pip install --upgrade openai wandb
+```
+
+Then sync your results from the command line or from your script.
+
+
+
+
+```shell-session
+# one line command
+openai wandb sync
+
+# passing optional parameters
+openai wandb sync --help
+```
+
+
+
+```python
+from openai.wandb_logger import WandbLogger
+
+# one line command
+WandbLogger.sync()
+
+# passing optional parameters
+WandbLogger.sync(
+ id=None,
+ n_fine_tunes=None,
+ project="OpenAI-Fine-Tune",
+ entity=None,
+ force=False,
+ **kwargs_wandb_init
+)
+```
+
+
+
+When you sync your results, wandb checks OpenAI for newly completed fine-tunes and automatically adds them to your dashboard.
+
+### Optional arguments
+
+| Argument | Description |
+| ------------------------ | ------------------------------------------------------------------------------------------------------------------------- |
+| -i ID, --id ID | The id of the fine-tune (optional) |
+| -n N, --n\_fine\_tunes N | Number of most recent fine-tunes to log when an id is not provided. By default, every fine-tune is synced. |
+| --project PROJECT | Name of the Weights & Biases project where you're sending runs. By default, it is "OpenAI-Fine-Tune". |
+| --entity ENTITY | Weights & Biases Username or team name where you're sending runs. By default, your default entity is used, which is usually your username. |
+| --force | Forces logging and overwrite existing wandb run of the same fine-tune. |
+| --legacy | Log results from the legacy OpenAI GPT-3 fine-tune api. |
+| \*\*kwargs\_wandb\_init | In python, any additional argument is directly passed to [`wandb.init()`](../../../ref/python/init.md) |
+
+### Inspect sample predictions
+
+Use [Tables](../../tables/intro.md) to better visualize sample predictions and compare models.
+
+
+
+Create a new run:
+
+```python
+run = wandb.init(project="OpenAI-Fine-Tune", job_type="eval")
+```
+
+Retrieve a model id for inference.
+
+You can use automatically logged artifacts to retrieve your latest model:
+
+```python
+ft_artifact = run.use_artifact("ENTITY/PROJECT/fine_tune_details:latest")
+fine_tuned_model = ft_artifact.metadata["fine_tuned_model"]
+```
+
+You can also retrieve your validation file:
+
+```python
+artifact_valid = run.use_artifact("ENTITY/PROJECT/FILENAME:latest")
+valid_file = artifact_valid.get_path("FILENAME").download()
+```
+
+Perform some inferences using OpenAI API:
+
+```python
+# perform inference and record results
+my_prompts = ["PROMPT_1", "PROMPT_2"]
+results = []
+for prompt in my_prompts:
+ res = openai.ChatCompletion.create(model=fine_tuned_model,
+ prompt=prompt,
+ ...)
+ results.append(res["choices"][0]["text"])
+```
+
+Log your results with a Table:
+
+```python
+table = wandb.Table(columns=['prompt', 'completion'],
+ data=list(zip(my_prompts, results)))
+```
+
+## Frequently Asked Questions
+
+### How do I share my fine-tune resutls with my team in W&B?
+
+Sync all your runs to your team account with:
+
+```shell-session
+openai wandb sync --entity MY_TEAM_ACCOUNT
+```
+
+### How can I organize my runs?
+
+Your W&B runs are automatically organized and can be filtered/sorted based on any configuration parameter such as job type, base model, learning rate, training filename and any other hyper-parameter.
+
+In addition, you can rename your runs, add notes or create tags to group them.
+
+Once you’re satisfied, you can save your workspace and use it to create report, importing data from your runs and saved artifacts (training/validation files).
+
+### How can I access my fine-tune details?
+
+Fine-tune details are logged to W&B as artifacts and can be accessed with:
+
+```python
+import wandb
+
+ft_artifact = wandb.run.use_artifact('USERNAME/PROJECT/job_details:VERSION')
+```
+
+where `VERSION` is either:
+
+* a version number such as `v2`
+* the fine-tune id such as `ft-xxxxxxxxx`
+* an alias added automatically such as `latest` or manually
+
+You can then access fine-tune details through `artifact_job.metadata`. For example, the fine-tuned model can be retrieved with `artifact_job.metadata[`"`fine_tuned_model"]`.
+
+### What if a fine-tune was not synced successfully?
+
+You can always call again `openai wandb sync` and we will re-sync any run that was not synced successfully.
+
+If needed, you can call `openai wandb sync --id fine_tune_id --force` to force re-syncing a specific fine-tune.
+
+### Can I track my datasets with W&B?
+
+Yes, you can integrate your entire pipeline to W&B through Artifacts, including creating your dataset, splitting it, training your models and evaluating them!
+
+This will allow complete traceability of your models.
+
+
+
+## Resources
+
+* [OpenAI Fine-tuning Documentation](https://platform.openai.com/docs/guides/fine-tuning/) is very thorough and contains many useful tips
+* [Demo Colab](http://wandb.me/openai-colab)
+* [Report - OpenAI Fine-Tuning Exploration & Tips](http://wandb.me/openai-report)
diff --git a/docs/guides/integrations/other/openai.md b/docs/guides/integrations/other/openai.md
deleted file mode 100644
index 20fc03a20d..0000000000
--- a/docs/guides/integrations/other/openai.md
+++ /dev/null
@@ -1,260 +0,0 @@
----
-slug: /guides/integrations/openai
-description: How to integrate W&B with OpenAI.
-displayed_sidebar: default
----
-
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-# OpenAI API
-
-Weights & Biases has 2 OpenAI integrations
-
-1. **[OpenAI Python SDK API](#log-openai-api-calls-in-1-line-of-code):** Log requests, responses, token counts and model metadata with 1 line of code for all OpenAI models
-
-2. **[OpenAI GPT-3 Fine-tuning](#log-openai-fine-tunes-to-wb):** Log your GPT-3 fine-tuning metrics and configuration to Weights & Biases to analyse and understand the performance of your newly fine-tuned models.
-
-
-## Log OpenAI API calls in 1 line of code
-
-**[Try in a Colab Notebook here →](https://github.com/wandb/examples/blob/master/colabs/openai/OpenAI_API_Autologger_Quickstart.ipynb)**
-
-With just 1 line of code you can now automatically log inputs and outputs from the OpenAI Python SDK to Weights & Biases!
-
-
-
-Once you start logging your API inputs and outputs you can quickly evaluate the performance of difference prompts, compare different model settings (such as temperature), and track other usage metrics such as token usage.
-
-To get started, pip install the `wandb` library, then follow the steps below:
-
-### 1. Import autolog and initialise it
-First, import `autolog` from `wandb.integration.openai` and initialise it.
-
-```python
-import os
-import openai
-from wandb.integration.openai import autolog
-
-autolog({"project": "gpt5"})
-```
-
-You can optionally pass a dictionary with argument that `wandb.init()` accepts to `autolog`. This includes a project name, team name, entity, and more. For more information about [`wandb.init`](../../../ref/python/init.md), see the API Reference Guide.
-
-### 2. Call the OpenAI API
-Each call you make to the OpenAI API will now be logged to Weights & Biases automatically.
-
-```python
-os.environ["OPENAI_API_KEY"] = "XXX"
-
-chat_request_kwargs = dict(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": "You are a helpful assistant."},
- {"role": "user", "content": "Who won the world series in 2020?"},
- {"role": "assistant", "content": "The Los Angeles Dodgers"},
- {"role": "user", "content": "Where was it played?"},
- ],
-)
-response = openai.ChatCompletion.create(**chat_request_kwargs)
-```
-
-### 3. View your OpenAI API inputs and responses
-
-Click on the Weights & Biases [run](../../runs/intro.md) link generated by `autolog` in **step 1**. This will redirect you to your project workspace in the W&B App.
-
-Select a run you created to view the trace table, trace timeline and the model architecture of the OpenAI LLM used.
-
-### 4. Disable autolog
-We recommend that you call `disable()` to close all W&B processes when you are finished using the OpenAI API.
-
-```python
-autolog.disable()
-```
-
-Now your inputs and completions will be logged to Weights & Biases, ready for analysis or to be shared with colleagues.
-
-
-
-
-## Log OpenAI fine-tunes to W&B
-
-If you use OpenAI's API to [fine-tune GPT-3](https://beta.openai.com/docs/guides/fine-tuning), you can now use the W&B integration to track experiments, models, and datasets in your central dashboard.
-
-
-
-All it takes is one line: `openai wandb sync`
-
-### :sparkles: Check out interactive examples
-
-* [Demo Colab](http://wandb.me/openai-colab)
-* [Report - GPT-3 Exploration and Fine-Tuning Tips](http://wandb.me/openai-report)
-
-### :tada: Sync your fine-tunes with one line!
-
-Make sure you are using latest version of openai and wandb.
-
-```shell-session
-$ pip install --upgrade openai wandb
-```
-
-Then sync your results from the command line or from your script.
-
-
-
-
-```shell-session
-$ # one line command
-$ openai wandb sync
-
-$ # passing optional parameters
-$ openai wandb sync --help
-```
-
-
-
-```python
-from openai.wandb_logger import WandbLogger
-
-# one line command
-WandbLogger.sync()
-
-# passing optional parameters
-WandbLogger.sync(
- id=None,
- n_fine_tunes=None,
- project="GPT-3",
- entity=None,
- force=False,
- **kwargs_wandb_init
-)
-```
-
-
-
-We scan for new completed fine-tunes and automatically add them to your dashboard.
-
-
-
-In addition your training and validation files are logged and versioned, as well as details of your fine-tune results. This let you interactively explore your training and validation data.
-
-
-
-### :gear: Optional arguments
-
-| Argument | Description |
-| ------------------------ | ------------------------------------------------------------------------------------------------------------------------- |
-| -i ID, --id ID | The id of the fine-tune (optional) |
-| -n N, --n\_fine\_tunes N | Number of most recent fine-tunes to log when an id is not provided. By default, every fine-tune is synced. |
-| --project PROJECT | Name of the project where you're sending runs. By default, it is "GPT-3". |
-| --entity ENTITY | Username or team name where you're sending runs. By default, your default entity is used, which is usually your username. |
-| --force | Forces logging and overwrite existing wandb run of the same fine-tune. |
-| \*\*kwargs\_wandb\_init | In python, any additional argument is directly passed to [`wandb.init()`](../../../ref/python/init.md) |
-
-### 🔍 Inspect sample predictions
-
-Use [Tables](../../tables/intro.md) to better visualize sample predictions and compare models.
-
-
-
-Create a new run:
-
-```python
-run = wandb.init(project="GPT-3", job_type="eval")
-```
-
-Retrieve a model id for inference.
-
-You can use automatically logged artifacts to retrieve your latest model:
-
-```python
-artifact_job = run.use_artifact("ENTITY/PROJECT/fine_tune_details:latest")
-fine_tuned_model = artifact_job.metadata["fine_tuned_model"]
-```
-
-You can also retrieve your validation file:
-
-```python
-artifact_valid = run.use_artifact("ENTITY/PROJECT/FILENAME:latest")
-valid_file = artifact_valid.get_path("FILENAME").download()
-```
-
-Perform some inferences using OpenAI API:
-
-```python
-# perform inference and record results
-my_prompts = ["PROMPT_1", "PROMPT_2"]
-results = []
-for prompt in my_prompts:
- res = openai.Completion.create(model=fine_tuned_model, prompt=prompt, ...)
- results.append(res["choices"][0]["text"])
-```
-
-Log your results with a Table:
-
-```python
-table = wandb.Table(
- columns=["prompt", "completion"], data=list(zip(my_prompts, results))
-)
-```
-
-## :question:Frequently Asked Questions
-
-### How do I share runs with my team?
-
-Sync all your runs to your team account with:
-
-```shell-session
-$ openai wandb sync --entity MY_TEAM_ACCOUNT
-```
-
-### How can I organize my runs?
-
-Your runs are automatically organized and can be filtered/sorted based on any configuration parameter such as job type, base model, learning rate, training filename and any other hyper-parameter.
-
-In addition, you can rename your runs, add notes or create tags to group them.
-
-Once you’re satisfied, you can save your workspace and use it to create report, importing data from your runs and saved artifacts (training/validation files).
-
-### How can I access my fine-tune details?
-
-Fine-tune details are logged to W&B as artifacts and can be accessed with:
-
-```python
-import wandb
-
-artifact_job = wandb.run.use_artifact("USERNAME/PROJECT/job_details:VERSION")
-```
-
-where `VERSION` is either:
-
-* a version number such as `v2`
-* the fine-tune id such as `ft-xxxxxxxxx`
-* an alias added automatically such as `latest` or manually
-
-You can then access fine-tune details through `artifact_job.metadata`. For example, the fine-tuned model can be retrieved with `artifact_job.metadata[`"`fine_tuned_model"]`.
-
-### What if a fine-tune was not synced successfully?
-
-You can always call again `openai wandb sync` and we will re-sync any run that was not synced successfully.
-
-If needed, you can call `openai wandb sync --id fine_tune_id --force` to force re-syncing a specific fine-tune.
-
-### Can I track my datasets with W&B?
-
-Yes, you can integrate your entire pipeline to W&B through Artifacts, including creating your dataset, splitting it, training your models and evaluating them!
-
-This will allow complete traceability of your models.
-
-
-
-## :books: Resources
-
-* [OpenAI Fine-tuning Documentation](https://beta.openai.com/docs/guides/fine-tuning) is very thorough and contains many useful tips
-* [Demo Colab](http://wandb.me/openai-colab)
-* [Report - GPT-3 Exploration & Fine-tuning Tips](http://wandb.me/openai-report)
diff --git a/docs/guides/prompts/intro.md b/docs/guides/prompts/intro.md
index b05b33fc7b..c62d942cf0 100644
--- a/docs/guides/prompts/intro.md
+++ b/docs/guides/prompts/intro.md
@@ -60,7 +60,8 @@ The Model Architecture view provides details about the structure of the chain an
Weights and Biases also has lightweight integrations for:
* [LangChain](../integrations/langchain.md)
-* [OpenAI API](../integrations/other/openai.md)
+* [OpenAI API](../guides/integrations/openai-api)
+* [OpenAI GPT-3.5 Fine-Tuning](../guides/integrations/openai)
* [Hugging Face Transformers](../integrations/huggingface.md)
diff --git a/docs/guides/prompts/openai.md b/docs/guides/prompts/openai.md
new file mode 100644
index 0000000000..6433fe0469
--- /dev/null
+++ b/docs/guides/prompts/openai.md
@@ -0,0 +1,18 @@
+---
+slug: /guides/prompts/openai
+description: OpenAI integrations for Weights & Biases
+displayed_sidebar: default
+---
+
+# OpenAI and Weights & Biases
+Weights & Biases has 2 OpenAI integrations to help you train better models faster and keep track of your prompts and generations:
+
+## OpenAI ChatGPT-3.5 and GPT-4 fine-tuning
+With Weights & Biases you can log your OpenAI model's fine-tuning metrics and configuration to Weights & Biases to analyse and understand the performance of your newly fine-tuned models and share the results with your colleagues.
+
+See the **[W&B and OpenAI Fine-Tuning docs here](../integrations/openai)** to learn more
+
+## OpenAI Python SDK API
+Log requests, responses, token counts and model metadata with 1 line of code for all OpenAI models
+
+See the **[W&B and OpenAI API section docs](../integrations/openai-api)** to learn more
diff --git a/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-api.md b/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-api.md
new file mode 100644
index 0000000000..5b099dbfc2
--- /dev/null
+++ b/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-api.md
@@ -0,0 +1,68 @@
+---
+slug: /guides/integrations/openai-api
+description: How to use W&B with the OpenAI API.
+displayed_sidebar: default
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# OpenAI API
+
+Use the Weights & Biases OpenAI API integration to log requests, responses, token counts and model metadata with 1 line of code for all OpenAI models, including fine-tuned models.
+
+**[Try in a Colab Notebook here →](https://github.com/wandb/examples/blob/master/colabs/openai/OpenAI_API_Autologger_Quickstart.ipynb)**
+
+With just 1 line of code you can now automatically log inputs and outputs from the OpenAI Python SDK to Weights & Biases!
+
+
+
+Once you start logging your API inputs and outputs you can quickly evaluate the performance of difference prompts, compare different model settings (such as temperature), and track other usage metrics such as token usage.
+
+To get started, pip install the `wandb` library, then follow the steps below:
+
+### 1. Import autolog and initialise it
+First, import `autolog` from `wandb.integration.openai` and initialise it.
+
+```python
+import os
+import openai
+from wandb.integration.openai import autolog
+
+autolog({"project":"gpt5"})
+```
+
+You can optionally pass a dictionary with argument that `wandb.init()` accepts to `autolog`. This includes a project name, team name, entity, and more. For more information about [`wandb.init`](../../../ref/python/init.md), see the API Reference Guide.
+
+### 2. Call the OpenAI API
+Each call you make to the OpenAI API will now be logged to Weights & Biases automatically.
+
+```python
+os.environ["OPENAI_API_KEY"] = "XXX"
+
+chat_request_kwargs = dict(
+ model="gpt-3.5-turbo",
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Who won the world series in 2020?"},
+ {"role": "assistant", "content": "The Los Angeles Dodgers"},
+ {"role": "user", "content": "Where was it played?"},
+ ],
+)
+response = openai.ChatCompletion.create(**chat_request_kwargs)
+```
+
+### 3. View your OpenAI API inputs and responses
+
+Click on the Weights & Biases [run](../../runs/intro.md) link generated by `autolog` in **step 1**. This will redirect you to your project workspace in the W&B App.
+
+Select a run you created to view the trace table, trace timeline and the model architecture of the OpenAI LLM used.
+
+### 4. Disable autolog
+We recommend that you call `disable()` to close all W&B processes when you are finished using the OpenAI API.
+
+```python
+autolog.disable()
+```
+
+Now your inputs and completions will be logged to Weights & Biases, ready for analysis or to be shared with colleagues.
\ No newline at end of file
diff --git a/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-fine-tuning.md b/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-fine-tuning.md
new file mode 100644
index 0000000000..e994795a11
--- /dev/null
+++ b/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-fine-tuning.md
@@ -0,0 +1,212 @@
+---
+slug: /guides/integrations/openai
+description: How to Fine-Tune OpenAI models using W&B.
+displayed_sidebar: default
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# OpenAI Fine-Tuning
+
+With Weights & Biases you can log your OpenAI ChatGPT-3.5 or GPT-4 model's fine-tuning metrics and configuration to Weights & Biases to analyse and understand the performance of your newly fine-tuned models and share the results with your colleagues.
+
+## Sync your OpenAI Fine-Tuning Results in 1 Line
+
+If you use OpenAI's API to [fine-tune OpenAI models](https://platform.openai.com/docs/guides/fine-tuning/), you can now use the W&B integration to track experiments, models, and datasets in your central dashboard.
+
+
+
+
+```shell-session
+openai wandb sync
+```
+
+
+
+
+```python
+from openai.wandb_logger import WandbLogger
+
+WandbLogger.sync(project="OpenAI-Fine-Tune")
+```
+
+
+
+
+
+
+### Check out interactive examples
+
+* [Demo Colab](http://wandb.me/openai-colab)
+* [Report - OpenAI Fine-Tuning Exploration and Tips](http://wandb.me/openai-report)
+
+### Sync your fine-tunes with one line
+
+Make sure you are using latest version of openai and wandb.
+
+```shell-session
+pip install --upgrade openai wandb
+```
+
+Then sync your results from the command line or from your script.
+
+
+
+
+```shell-session
+# one line command
+openai wandb sync
+
+# passing optional parameters
+openai wandb sync --help
+```
+
+
+
+```python
+from openai.wandb_logger import WandbLogger
+
+# one line command
+WandbLogger.sync()
+
+# passing optional parameters
+WandbLogger.sync(
+ id=None,
+ n_fine_tunes=None,
+ project="OpenAI-Fine-Tune",
+ entity=None,
+ force=False,
+ **kwargs_wandb_init
+)
+```
+
+
+
+When you sync your results, wandb checks OpenAI for newly completed fine-tunes and automatically adds them to your dashboard.
+
+### Optional arguments
+
+| Argument | Description |
+| ------------------------ | ------------------------------------------------------------------------------------------------------------------------- |
+| -i ID, --id ID | The id of the fine-tune (optional) |
+| -n N, --n\_fine\_tunes N | Number of most recent fine-tunes to log when an id is not provided. By default, every fine-tune is synced. |
+| --project PROJECT | Name of the Weights & Biases project where you're sending runs. By default, it is "OpenAI-Fine-Tune". |
+| --entity ENTITY | Weights & Biases Username or team name where you're sending runs. By default, your default entity is used, which is usually your username. |
+| --force | Forces logging and overwrite existing wandb run of the same fine-tune. |
+| --legacy | Log results from the legacy OpenAI GPT-3 fine-tune api. |
+| \*\*kwargs\_wandb\_init | In python, any additional argument is directly passed to [`wandb.init()`](../../../ref/python/init.md) |
+
+### Inspect sample predictions
+
+Use [Tables](../../tables/intro.md) to better visualize sample predictions and compare models.
+
+
+
+Create a new run:
+
+```python
+run = wandb.init(project="OpenAI-Fine-Tune", job_type="eval")
+```
+
+Retrieve a model id for inference.
+
+You can use automatically logged artifacts to retrieve your latest model:
+
+```python
+ft_artifact = run.use_artifact("ENTITY/PROJECT/fine_tune_details:latest")
+fine_tuned_model = ft_artifact.metadata["fine_tuned_model"]
+```
+
+You can also retrieve your validation file:
+
+```python
+artifact_valid = run.use_artifact("ENTITY/PROJECT/FILENAME:latest")
+valid_file = artifact_valid.get_path("FILENAME").download()
+```
+
+Perform some inferences using OpenAI API:
+
+```python
+# perform inference and record results
+my_prompts = ["PROMPT_1", "PROMPT_2"]
+results = []
+for prompt in my_prompts:
+ res = openai.ChatCompletion.create(model=fine_tuned_model,
+ prompt=prompt,
+ ...)
+ results.append(res["choices"][0]["text"])
+```
+
+Log your results with a Table:
+
+```python
+table = wandb.Table(columns=['prompt', 'completion'],
+ data=list(zip(my_prompts, results)))
+```
+
+## Frequently Asked Questions
+
+### How do I share my fine-tune resutls with my team in W&B?
+
+Sync all your runs to your team account with:
+
+```shell-session
+openai wandb sync --entity MY_TEAM_ACCOUNT
+```
+
+### How can I organize my runs?
+
+Your W&B runs are automatically organized and can be filtered/sorted based on any configuration parameter such as job type, base model, learning rate, training filename and any other hyper-parameter.
+
+In addition, you can rename your runs, add notes or create tags to group them.
+
+Once you’re satisfied, you can save your workspace and use it to create report, importing data from your runs and saved artifacts (training/validation files).
+
+### How can I access my fine-tune details?
+
+Fine-tune details are logged to W&B as artifacts and can be accessed with:
+
+```python
+import wandb
+
+ft_artifact = wandb.run.use_artifact('USERNAME/PROJECT/job_details:VERSION')
+```
+
+where `VERSION` is either:
+
+* a version number such as `v2`
+* the fine-tune id such as `ft-xxxxxxxxx`
+* an alias added automatically such as `latest` or manually
+
+You can then access fine-tune details through `artifact_job.metadata`. For example, the fine-tuned model can be retrieved with `artifact_job.metadata[`"`fine_tuned_model"]`.
+
+### What if a fine-tune was not synced successfully?
+
+You can always call again `openai wandb sync` and we will re-sync any run that was not synced successfully.
+
+If needed, you can call `openai wandb sync --id fine_tune_id --force` to force re-syncing a specific fine-tune.
+
+### Can I track my datasets with W&B?
+
+Yes, you can integrate your entire pipeline to W&B through Artifacts, including creating your dataset, splitting it, training your models and evaluating them!
+
+This will allow complete traceability of your models.
+
+
+
+## Resources
+
+* [OpenAI Fine-tuning Documentation](https://platform.openai.com/docs/guides/fine-tuning/) is very thorough and contains many useful tips
+* [Demo Colab](http://wandb.me/openai-colab)
+* [Report - OpenAI Fine-Tuning Exploration & Tips](http://wandb.me/openai-report)
diff --git a/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-gym.md b/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-gym.md
index 4afb9d6843..b4c34f87a2 100644
--- a/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-gym.md
+++ b/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai-gym.md
@@ -1,15 +1,21 @@
---
slug: /guides/integrations/openai-gym
description: How to integrate W&B with OpenAI Gym.
-displayed_sidebar: ja
+displayed_sidebar: default
---
# OpenAI Gym
-[OpenAI Gym](https://gym.openai.com/)をお使いの場合、`gym.wrappers.Monitor`で生成された環境のビデオを自動的に記録します。[`wandb.init`](../../../ref/python/init.md)の`monitor_gym`キーワード引数を`True`に設定するか、`wandb.gym.monitor()`を呼び出してください。
+:::info
+"The team that has been maintaining Gym since 2021 has moved all future development to [Gymnasium](https://github.com/Farama-Foundation/Gymnasium), a drop in replacement for Gym (import gymnasium as gym), and Gym will not be receiving any future updates." ([Source](https://github.com/openai/gym#the-team-that-has-been-maintaining-gym-since-2021-has-moved-all-future-development-to-gymnasium-a-drop-in-replacement-for-gym-import-gymnasium-as-gym-and-gym-will-not-be-receiving-any-future-updates-please-switch-over-to-gymnasium-as-soon-as-youre-able-to-do-so-if-youd-like-to-read-more-about-the-story-behind-this-switch-please-check-out-this-blog-post))
-Gymとの統合は非常に軽量です。`gym`から記録されているビデオファイルの名前を[調べ](https://github.com/wandb/wandb/blob/master/wandb/integration/gym/__init__.py#L15)、それに基づいて名前を付けたり、マッチしない場合は`"videos"`にフォールバックします。さらにコントロールが必要な場合は、手動で[ビデオを記録](../../track/log/media.md)することもできます。
+Since Gym is no longer an actively maintained project, try out our integration with Gymnasium. Learn more about it here # TODO add link.
+:::
-[CleanRL](https://github.com/vwxyzjn/cleanrl)の[OpenRL Benchmark](http://wandb.me/openrl-benchmark-report)は、OpenAI Gymの例でこの統合を使用しています。Gymを使って実行する方法を示すソースコード（[特定のランで使用された特定のコード](https://wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/code?workspace=user-costa-huang)を含む）があります。
+If you're using [OpenAI Gym](https://gym.openai.com/) we will automatically log videos of your environment generated by `gym.wrappers.Monitor`. Just set the `monitor_gym` keyword argument to [`wandb.init`](../../../ref/python/init.md) to `True` or call `wandb.gym.monitor()`.
-
\ No newline at end of file
+Our gym integration is very light. We simply [look at the name of the video file](https://github.com/wandb/wandb/blob/master/wandb/integration/gym/\_\_init\_\_.py#L15) being logged from `gym` and name it after that or fall back to `"videos"` if we don't find a match. If you want more control, you can always just manually [log a video](../../track/log/media.md).
+
+The [OpenRL Benchmark](http://wandb.me/openrl-benchmark-report) by[ CleanRL](https://github.com/vwxyzjn/cleanrl) uses this integration for its OpenAI Gym examples. You can find source code (including [the specific code used for specific runs](https://wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/code?workspace=user-costa-huang)) that demonstrates how to use gym with
+
+
diff --git a/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai.md b/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai.md
deleted file mode 100644
index 422bb2981d..0000000000
--- a/i18n/ja/docusaurus-plugin-content-docs/current/guides/integrations/other/openai.md
+++ /dev/null
@@ -1,252 +0,0 @@
----
-slug: /guides/integrations/openai
-description: How to integrate W&B with OpenAI.
-displayed_sidebar: ja
----
-
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-# OpenAI API
-
-:::info
-**Beta Integration**: これは新しい機能であり、この機能を改善するために積極的に取り組んでいます。何かフィードバックがあれば、お気軽にお問い合わせください — contact@wandb.com
-:::
-
-OpenAIのAPIは、機械学習開発者がGPT-4にアクセスできるようにします。これは非常にパワフルな自然言語モデルであり、自然言語を理解したり生成したりするタスクにほぼ適用できます。
-
-## OpenAI APIコールを1行のコードでログに記録する
-たった1行のコードで、OpenAI Python SDKからWeights & Biasesに入力と出力を自動的にログに記録できるようになりました！
-
-
-
-始めるには、`wandb`ライブラリをpipでインストールし、以下の手順に従ってください。
-
-### 1. autologをインポートして初期化する
-まず、`wandb.integration.openai`から`autolog`をインポートし、初期化します。
-
-```python
-import os
-import openai
-from wandb.integration.openai import autolog
-
-autolog({"project": "gpt5"})
-```
-
-必要に応じて、`autolog`に`wandb.init()`が受け付ける引数を含むディクショナリを渡すことができます。これには、プロジェクト名、チーム名、エンティティなどが含まれます。 [`wandb.init`](../../../ref/python/init.md)についての詳細は、APIリファレンスガイドを参照してください。
-
-### 2. OpenAI APIを呼び出す
-これで、OpenAI APIへの各呼び出しがWeights＆Biasesに自動的に記録されます。
-
-```python
-os.environ["OPENAI_API_KEY"] = "XXX"
-
-chat_request_kwargs = dict(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": "あなたは親切なアシスタントです。"},
- {"role": "user", "content": "2020年のワールドシリーズで誰が勝ちましたか？"},
- {"role": "assistant", "content": "ロサンゼルス・ドジャースです"},
- {"role": "user", "content": "それはどこで行われましたか？"},
- ],
-)
-response = openai.ChatCompletion.create(**chat_request_kwargs)
-```
-
-### 3. OpenAI APIの入力とレスポンスを表示する
-
-**手順1**で`autolog`によって生成されたWeights＆Biases [run](../../runs/intro.md)リンクをクリックします。これにより、W&Bアプリのプロジェクトワークスペースにリダイレクトされます。
-
-作成したrunを選択して、トレーステーブル、トレースタイムライン、および使用されたOpenAI LLMのアーキテクチャーを表示します。
-### 4. オートログを無効にする
-OpenAI APIを使用し終わったら、`disable()`を呼び出して、W&Bのすべてのプロセスを閉じることをお勧めします。
-
-```python
-autolog.disable()
-```
-
-これで、入力と完成がWeights & Biasesにログされ、分析や同僚との共有の準備が整います。
-
-## W&BにOpenAIの微調整をログする
-
-OpenAIのAPIを使って[GPT-3を微調整する](https://beta.openai.com/docs/guides/fine-tuning)場合、W&Bの統合を利用して、実験、モデル、データセットを一元管理できるダッシュボードでトラッキングできるようになります。
-
-
-
-必要なのは、`openai wandb sync`という一行だけです。
-
-## :sparkles: インタラクティブな例をチェックしよう
-
-* [デモColab](http://wandb.me/openai-colab)
-* [レポート - GPT-3の探索と微調整のヒント](http://wandb.me/openai-report)
-
-## :tada: 1行で微調整を同期しよう！
-
-openaiとwandbの最新バージョンを使用していることを確認してください。
-
-```shell-session
-$ pip install --upgrade openai wandb
-```
-
-次に、コマンドラインまたはスクリプトから結果を同期します。
-
-
-
-
-```shell-session
-$ # 1行のコマンド
-$ openai wandb sync
-
-$ # オプションパラメータを渡す
-$ openai wandb sync --help
-```
-
-
-
-```python
-from openai.wandb_logger import WandbLogger
-
-# 1行のコマンド
-WandbLogger.sync()
-
-# オプションパラメータを渡す
-WandbLogger.sync(
- id=None,
- n_fine_tunes=None,
- project="GPT-3",
- entity=None,
- force=False,
- **kwargs_wandb_init
-)
-```
-
-
-新しい完了した微調整をスキャンし、自動的にダッシュボードに追加します。
-
-
-
-さらに、トレーニングと検証のファイルがログ化されバージョン管理され、微調整の結果の詳細も記録されます。これにより、トレーニングデータと検証データを対話式に調べることができます。
-
-
-
-## :gear: 任意の引数
-
-| 引数 | 説明 |
-| ------------------------ | ------------------------------------------------------------------------------------------------------------------------ |
-| -i ID, --id ID | 微調整のID（任意） |
-| -n N, --n\_fine\_tunes N | IDが提供されていない場合にログする最も新しい微調整の数。デフォルトでは、すべての微調整が同期されます。 |
-| --project PROJECT | プロジェクトの名前。デフォルトでは、「GPT-3」です。 |
-| --entity ENTITY | runsを送信するユーザー名またはチーム名。デフォルトでは、デフォルトのエンティティ（通常はユーザー名）が使用されます。 |
-| --force | ログ記録を強制し、同じ微調整の既存のwandb runを上書きします。 |
-| \*\*kwargs\_wandb\_init | Pythonでは、追加の引数は[`wandb.init()`](../../../ref/python/init.md)に直接渡されます。 |
-
-## 🔍 サンプル予測の検証
-
-[Tables](../../tables/intro.md)を使用して、サンプル予測をより良く可視化し、モデルを比較します。
-
-
-
-新しいrunを作成します：
-
-```python
-run = wandb.init(project="GPT-3", job_type="eval")
-```
-推論用のモデルIDを取得します。
-
-自動的にログされたアーティファクトを使用して、最新のモデルを取得できます。
-
-```python
-artifact_job = run.use_artifact("ENTITY/PROJECT/fine_tune_details:latest")
-fine_tuned_model = artifact_job.metadata["fine_tuned_model"]
-```
-
-検証ファイルも取得できます。
-
-```python
-artifact_valid = run.use_artifact("ENTITY/PROJECT/FILENAME:latest")
-valid_file = artifact_valid.get_path("FILENAME").download()
-```
-
-OpenAI APIを使っていくつかの推論を行います。
-
-```python
-# 推論を行い結果を記録する
-my_prompts = ["PROMPT_1", "PROMPT_2"]
-results = []
-for prompt in my_prompts:
- res = openai.Completion.create(model=fine_tuned_model, prompt=prompt, ...)
- results.append(res["choices"][0]["text"])
-```
-
-結果をテーブルでログします。
-```python
-table = wandb.Table(
- columns=["prompt", "completion"], data=list(zip(my_prompts, results))
-)
-```
-
-## :question:よくある質問
-
-### どのようにしてチームとrunを共有できますか？
-
-以下のようにして、すべてのrunをチームアカウントと同期させます。
-
-```shell-session
-$ openai wandb sync --entity MY_TEAM_ACCOUNT
-```
-
-### runをどのように整理できますか？
-
-runは自動的に整理され、ジョブタイプ、ベースモデル、学習率、トレーニングファイル名、その他のハイパーパラメータなどの設定パラメータに基づいてフィルター/並び替えができます。
-
-また、runの名前を変更したり、ノートを追加したり、タグを作成してグループ化することができます。
-
-満足したら、ワークスペースを保存し、レポートを作成するために、runからデータや保存されたアーティファクト（トレーニング/検証ファイル）をインポートできます。
-
-### 微調整の詳細にどのようにアクセスできますか？
-
-微調整の詳細はW&Bにアーティファクトとしてログされており、以下のようにアクセスできます。
-
-```python
-import wandb
-```
-artifact_job = wandb.run.use_artifact('USERNAME/PROJECT/job_details:VERSION')
-
-```
-
-ここで`VERSION`は以下のいずれかです。
-
-* バージョン番号（例：`v2`）
-
-* 微調整ID（例：`ft-xxxxxxxxx`）
-
-* 自動的に追加されたエイリアス（例：`latest`）または手動で追加されたエイリアス
-
-その後、`artifact_job.metadata` を通して微調整の詳細にアクセスできます。例えば、微調整されたモデルは `artifact_job.metadata["fine_tuned_model"]`で取得できます。
-
-### ファインチューンが正常に同期されなかった場合は？
-
-いつでも `openai wandb sync` を再度呼び出すことで、正常に同期されなかったランを再同期できます。
-
-必要に応じて、`openai wandb sync --id fine_tune_id --force` を呼び出して、特定のファインチューンを強制的に再同期できます。
-
-### W&Bでデータセットをトラッキングできますか？
-
-はい、Artifactsを通じて、データセットの作成、分割、モデルのトレーニングおよび評価を含む、W&Bの完全な開発フローを統合できます！
-
-これにより、モデルの完全なトレーサビリティが実現されます。
-
-
-
-## :books: リソース
-
-* [OpenAI Fine-tuning Documentation](https://beta.openai.com/docs/guides/fine-tuning) は非常に詳細で、多くの有益なヒントが含まれています。
-
-* [デモColab](http://wandb.me/openai-colab)
-
-* [レポート - GPT-3 Exploration & Fine-tuning Tips](http://wandb.me/openai-report)
\ No newline at end of file
diff --git a/sidebars.js b/sidebars.js
index 54c8b77ad0..bdf8fda7c9 100644
--- a/sidebars.js
+++ b/sidebars.js
@@ -185,6 +185,7 @@ const sidebars = {
},
items: [
'guides/prompts/quickstart',
+ 'guides/prompts/openai',
],
},
{
@@ -371,7 +372,8 @@ const sidebars = {
'guides/integrations/mmdetection',
'guides/integrations/other/mmf',
'guides/integrations/other/composer',
- 'guides/integrations/other/openai',
+ 'guides/integrations/other/openai-api',
+ 'guides/integrations/other/openai-fine-tuning',
'guides/integrations/other/openai-gym',
'guides/integrations/other/paddledetection',
'guides/integrations/other/paddleocr',
@@ -566,6 +568,7 @@ const sidebars = {
},
items: [
'guides/prompts/quickstart',
+ 'guides/prompts/openai',
],
},
{
@@ -724,7 +727,8 @@ const sidebars = {
'guides/integrations/mmdetection',
'guides/integrations/other/mmf',
'guides/integrations/other/composer',
- 'guides/integrations/other/openai',
+ 'guides/integrations/other/openai-api',
+ 'guides/integrations/other/openai-fine-tuning',
'guides/integrations/other/openai-gym',
'guides/integrations/other/paddledetection',
'guides/integrations/other/paddleocr',