Create lmstudio.md

docs/reference/serve/lmstudio.md

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+# Serve with LM Studio
+
+!!! tip "Would rather not self-host?"
+
+    If you want to get started quickly with JSON-structured generation you can call instead [.json](https://h1xbpbfsf0w.typeform.com/to/ZgBCvJHF), a [.txt](http://dottxt.co) API that guarantees valid JSON.
+
+[LM Studio](https://lmstudio.ai/) is an application that runs local LLMs. It flexibly mixes GPU and CPU compute in hardware-constrained environments.
+
+As of [LM Studio 0.3.4](https://lmstudio.ai/blog/lmstudio-v0.3.4), it natively supports Outlines for structured text generation, using an OpenAI-compatible endpoint.
+
+## Setup
+
+1. Install LM Studio by visiting their [downloads page](https://lmstudio.ai/download).
+2. Enable the LM Studio [server functionality](https://lmstudio.ai/docs/basics/server).
+3. Download [a model](https://lmstudio.ai/docs/basics#1-download-an-llm-to-your-computer).
+4. Install Python dependencies.
+```bash
+pip install pydantic openai
+```
+
+## Calling the server
+
+By default, LM Studio will serve from `http://localhost:1234`. If you are serving on a different port or host, make sure to change the `base_url` argument in `OpenAI` to the relevant location.
+
+```python
+class Testing(BaseModel):
+    """
+    A class representing a testing schema.
+    """
+    name: str
+    age: int
+
+openai_client = openai.OpenAI(
+    base_url="http://0.0.0.0:1234/v1",
+    api_key="dopeness"
+)
+
+# Make a request to the local LM Studio server
+response = openai_client.beta.chat.completions.parse(
+    model="hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF",
+    messages=[
+        {"role": "system", "content": "You are like so good at whatever you do."},
+        {"role": "user", "content": "My name is Cameron and I am 28 years old. What's my name and age?"}
+    ],
+    response_format=Testing
+)
+```
+
+You should receive a `ParsedChatCompletion[Testing]` object back:
+
+```python
+ParsedChatCompletion[Testing](
+    id='chatcmpl-3hykyf0fxus7jc90k6gwlw',
+    choices=[
+        ParsedChoice[Testing](
+            finish_reason='stop',
+            index=0,
+            logprobs=None,
+            message=ParsedChatCompletionMessage[Testing](
+                content='{ "age": 28, "name": "Cameron" }',
+                refusal=None,
+                role='assistant',
+                function_call=None,
+                tool_calls=[],
+                parsed=Testing(name='Cameron', age=28)
+            )
+        )
+    ],
+    created=1728595622,
+    model='lmstudio-community/Phi-3.1-mini-128k-instruct-GGUF/Phi-3.1-mini-128k-instruct-Q4_K_M.gguf',
+    object='chat.completion',
+    service_tier=None,
+    system_fingerprint='lmstudio-community/Phi-3.1-mini-128k-instruct-GGUF/Phi-3.1-mini-128k-instruct-
+Q4_K_M.gguf',
+    usage=CompletionUsage(
+        completion_tokens=17,
+        prompt_tokens=47,
+        total_tokens=64,
+        completion_tokens_details=None,
+        prompt_tokens_details=None
+    )
+)
+```
+
+You can retrieve your `Testing` object with 
+
+```python
+response.choices[0].message.parsed
+```