Quantstruct: update changelog and fine-tuning guide for OpenAPI spec …

changelog.md

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+# Updates to Admin APIs, Real-time Sessions, and Fine-tuning Request Payloads
+
+1. **New admin API key endpoints**: You can now manage admin-level keys with “GET,” “POST,” “GET by ID,” and “DELETE” calls at `/organization/admin_api_keys`. This makes it easier to create, review, and delete high-permission keys without mixing them up with normal API keys.
+2. **New realtime sessions endpoint**: There is now a “POST /realtime/sessions” method for ephemeral token creation. This lets you quickly generate short-lived client tokens to power more dynamic, real-time applications.
+Example Payload:
+```json
+{
+    "model": "gpt-4o-realtime-preview-2024-12-17",
+    "modalities": ["audio", "text"],
+    "instructions": "You are a helpful assistant.",
+    "voice": "alloy",
+    "input_audio_format": "pcm16",
+    "output_audio_format": "pcm16",
+    "input_audio_transcription": {
+        "model": "whisper-1"
+    },
+    "turn_detection": null,
+    "tools": [],
+    "tool_choice": "auto",
+    "temperature": 0.8,
+    "max_response_output_tokens": "inf"
+}
+```
+
+3. **Fine-tuning job creation changed**: the request now requires you to nest fields under “method” with “type” and “hyperparameters” inside. The [finetuning UI](https://platform.openai.com/finetune) remains unchanged
+```json
+{
+    "training_file": "file-abc123",
+    "model": "gpt-4o-mini",
+    "method": {
+        "type": "supervised",
+        "supervised": {
+            "hyperparameters": {
+                "n_epochs": 2
+            }
+        }
+    }
+}
+```

finetuning-guide.md

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+# OpenAI Fine-tuning Guide
+
+## Overview
+Fine-tuning lets you customize OpenAI models by training them on your data to improve style, reliability, and task-specific performance.
+
+- Setting specific style, tone, or format
+- Improving reliability for desired outputs
+- Handling complex prompts and edge cases
+- Teaching new skills or tasks
+
+## Currently Available Models
+
+- `gpt-4o-mini` (recommended for most users)
+- `gpt-3.5-turbo`
+- `babbage-002`
+- `davinci-002`
+
+Note: GPT-4 fine-tuning is in experimental access.
+
+## Step-by-Step Guide
+
+### 1. Prepare Dataset
+Create a JSONL file with conversation examples:
+
+### 2. Create Fine-tuning Job
+Use the OpenAI API to create a fine-tuning job:
+
+### 3. Monitor Job
+Use the dashboard to track job status and progress.
+
+### 4. Evaluate Model
+
+### 5. Deploy Model