Steps:
1. Visit lmstudio.ai to download the installation package
   - Windows: .exe installation package
   - macOS: .dmg installation package
   - Linux: .AppImage

2. After the installation is complete, open LM Studio

3. Click the "Search" icon (magnifying glass) on the left

4. Search models, recommended for novices:
   - "Qwen2.5-7B-Instruct-GGUF" (Excellent Chinese)
   - "Llama-3.2-3B-Instruct-GGUF" (lightweight)

5. Select the quantized version (Q4_K_M is the balance between quality and size)

6. Click Download and wait for completion

Hardware configuration and model selection guide:




8GB memory (without discrete graphics):


- Select 3B-4B parametric model


- Quantization: Q4_K_M or Q5_K_M


- Recommended: Llama-3.2-3B, Phi-3.5-mini




16GB memory (without discrete graphics):


- Can run 7B parameter model


- Quantization: Q4_K_M


- Recommended: Qwen2.5-7B, Llama-3.1-8B




With discrete graphics card (8GB video memory):


- Models can be loaded to the GPU, greatly improving speed


- Fully load the 7B model to the GPU


- Turn on GPU acceleration in LM Studio settings




32GB RAM or 24GB VRAM:


- Can run 13B-14B parametric models


- Recommended: Qwen2.5-14B, Llama-3.1-14B

Steps:


1. Click the "Conversation" icon (bubble) on the left


2. Click the model selection drop-down menu at the top


3. Select the downloaded model


4. Wait for the model to load (about 10-30 seconds for the first time)


5. Enter the question in the bottom input box




Example conversation:


"Hello, please introduce yourself in Chinese,


And what can you do to help me? "

Steps:
1. Click the "Developer" icon on the left in LM Studio
2. Select the model to use
3. Click "Start Server"
4. Default port: http://localhost:1234

Python call example:
```python
from openai import OpenAI

# Point to local LM Studio server
client = OpenAI(
    base_url="http://localhost:1234/v1",
    api_key="lm-studio" # Any string
)

response = client.chat.completions.create(
    model="qwen2.5-7b-instruct", # Use the loaded model name
    messages=[
        {"role": "user", "content": "Explain what a vector database is"}
    ]
)
print(response.choices[0].message.content)
```