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Use LM Studio for local inference with a graphical interface, easy model discovery and download, and quick testing without command-line setup.

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Installation

Download and install LM Studio directly from lmstudio.ai.

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Downloading Models

  1. Open LM Studio and click the Search tab (🔍)
  2. Search for “LiquidAI” or “LFM2”
  3. Select a model and quantization level (Q4_K_M recommended)
  4. Click Download
See the Models page for all available GGUF models.

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Using the Chat Interface

  1. Go to the Chat tab (💬)
  2. Select your model from the dropdown
  3. Adjust parameters (temperature, max_tokens, top_p) in the sidebar
  4. Start chatting

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Generation Parameters

Control text generation behavior using the GUI sidebar or API parameters. Key parameters:
  • temperature (float, default 1.0): Controls randomness (0.0 = deterministic, higher = more random). Typical range: 0.1-2.0
  • top_p (float, default 1.0): Nucleus sampling - limits to tokens with cumulative probability ≤ top_p. Typical range: 0.1-1.0
  • top_k (int, default 40): Limits to top-k most probable tokens. Typical range: 1-100
  • max_tokens (int): Maximum number of tokens to generate
  • repetition_penalty (float, default 1.0): Penalty for repeating tokens (>1.0 = discourage repetition). Typical range: 1.0-1.5
  • stop (str or list[str]): Strings that terminate generation when encountered
Via the OpenAI-compatible API: 
response = client.chat.completions.create(
    model="local-model",
    messages=[{"role": "user", "content": "What is machine learning?"}],
    temperature=0.7,
    top_p=0.9,
    top_k=40,
    max_tokens=512,
    repetition_penalty=1.1,
)

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Running the Server

Start an OpenAI-compatible server for programmatic access:
  1. Go to the Developer tab (⚙️)
  2. Select your model
  3. Click Start Server (runs at http://localhost:1234)
Use the OpenAI Python client: 
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:1234/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="local-model",  # Any string works
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ],
    temperature=0.7,
    max_tokens=512
)
print(response.choices[0].message.content)

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Streaming Responses

stream = client.chat.completions.create(
    model="local-model",
    messages=[
        {"role": "user", "content": "Tell me a story."}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "local-model",
    "messages": [{"role": "user", "content": "Hello!"}],
    "temperature": 0.7
  }'

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Vision Models

Search for “LiquidAI LFM2-VL” to download vision models. In the Chat tab:
  • Drag and drop images into the chat
  • Click the image icon to upload
  • Provide image URLs
from openai import OpenAI
import base64

client = OpenAI(
    base_url="http://localhost:1234/v1",
    api_key="not-needed"
)

# Encode image to base64
with open("image.jpg", "rb") as image_file:
    image_data = base64.b64encode(image_file.read()).decode("utf-8")

response = client.chat.completions.create(
    model="local-model",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}},
                {"type": "text", "text": "What's in this image?"}
            ]
        }
    ]
)
print(response.choices[0].message.content)

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Tips

  • GPU Acceleration: Automatically detects and uses available GPUs
  • Model Management: Delete models from the My Models section
  • Performance: Adjust GPU layers in server settings for speed/memory balance
  • Quantization: Q4 is faster, Q6/Q8 have better quality