EXAONE 4.5

EXAONE 4.5#

EXAONE 4.5 is LG AI Research’s open-weight vision-language model. It integrates a dedicated vision encoder with the dense EXAONE 4 language-model architecture to handle images and text, and supports hybrid reasoning and tool calling.

Furiosa-LLM runs EXAONE 4.5 using the upstream FP8 release. Selected linear weights use static FP8 quantization with dynamic per-token FP8 activation quantization, while the vision encoder and attention projections stay in higher precision. FuriosaAI publishes a pre-compiled build under the furiosa-ai organization on the Hugging Face Hub, shipping a Furiosa Executable Bundle (FXB) for running the model on FuriosaAI RNGD with Furiosa-LLM. The same upstream weights also run on other frameworks (such as vLLM, SGLang, and Transformers); for usage with those, see the upstream model card linked below.

For the text-only predecessor, see EXAONE 4.0.

Available Models#

Model

Quantization

RNGD cards

Notes

furiosa-ai/EXAONE-4.5-33B-FP8

FP8

4

33B dense vision-language model; thinking by default

  • Architecture: EXAONE 4.5 (dense vision-language), Exaone4_5_ForConditionalGeneration

  • Input / Output: Image + Text / Text

  • Quantization: Selected linear weights are quantized to FP8 (static, per-channel), following the upstream FP8 release, and their activations use dynamic per-token FP8 quantization at runtime. The upstream configuration excludes the vision encoder, attention projections, LM head, and MTP attention projections from FP8 quantization; the KV cache stays in 16-bit precision.

Usage#

To run this model with Furiosa-LLM, follow the example commands below after installing Furiosa-LLM and its prerequisites.

Launch the server#

EXAONE 4.5 reasons by default and can switch thinking on and off (see Advanced Usage). Launch the server with the qwen3 reasoning parser so its chain of thought is separated from the final answer:

# Launch the server, listening on port 8000 by default
furiosa-llm serve furiosa-ai/EXAONE-4.5-33B-FP8 \
  --reasoning-parser qwen3

To also enable tool (function) calling, add the hermes tool-call parser; keep --reasoning-parser qwen3 so thinking is still parsed into its own field:

furiosa-llm serve furiosa-ai/EXAONE-4.5-33B-FP8 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

When the server is ready, you will see:

INFO:     Started server process [27507]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Basic Usage#

The server exposes an OpenAI-compatible API. You can send a text-only request with curl:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "furiosa-ai/EXAONE-4.5-33B-FP8",
    "messages": [{"role": "user", "content": "What is the capital of France?"}]
    }' \
    | python -m json.tool

To ask about an image, pass an image_url content part in the message:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "furiosa-ai/EXAONE-4.5-33B-FP8",
    "messages": [{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "https://github.com/LG-AI-EXAONE/EXAONE-4.5/blob/main/assets/exaone45_input1.jpg?raw=true"}},
            {"type": "text", "text": "What dish is the person preparing?"}
        ]
    }]
    }' \
    | python -m json.tool

The image_url.url field accepts a remote http:///https:// URL, an inline base64 data: URL, or a local file:// path (the last requires the --allowed-local-media-path flag described under Advanced Usage).

With --reasoning-parser qwen3, EXAONE 4.5 returns its reasoning separately from the final answer:

  • response.choices[].message.reasoning (non-streaming)

  • response.choices[].delta.reasoning (streaming)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="furiosa-ai/EXAONE-4.5-33B-FP8",
    messages=[{"role": "user", "content": "How many r's are in 'strawberry'?"}],
)

print("Reasoning:", response.choices[0].message.reasoning)
print("Answer:", response.choices[0].message.content)

Note: The reasoning field is not part of the OpenAI API specification but is a widely followed convention (the OpenAI Agents SDK, vLLM, and others). It appears only in responses that contain reasoning content; accessing it otherwise raises an AttributeError.

Advanced Usage#

Multimodal serving options. furiosa-llm serve provides flags to control multimodal behavior; requests that violate them are rejected with HTTP 400:

  • --image-limit-per-prompt N — maximum number of images allowed per request (default: unlimited).

  • --allowed-local-media-path PATH — allow file:// URLs whose resolved path is under PATH. Local file access is disabled unless this is set.

  • --allowed-media-domains D [D ...] — whitelist remote domains for SSRF protection. When set, only images from the listed domains are fetched.

  • --interleave-mm-strings — keep image placeholders at their original positions when the model uses a string-format chat template.

For example, to serve local images under /srv/media and restrict remote fetches to a single domain:

furiosa-llm serve furiosa-ai/EXAONE-4.5-33B-FP8 \
  --reasoning-parser qwen3 \
  --allowed-local-media-path /srv/media \
  --allowed-media-domains cdn.example.com \
  --image-limit-per-prompt 4

See the Vision-Language Models guide for image input formats and Python client examples.

Turning thinking off. EXAONE 4.5 reasons by default. To turn thinking off for a single request, pass enable_thinking through chat_template_kwargs; the response then carries no reasoning content, so read only message.content:

# Disable thinking for a single request
response = client.chat.completions.create(
    model="furiosa-ai/EXAONE-4.5-33B-FP8",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(response.choices[0].message.content)

To default every request to non-thinking, launch the server with --default-chat-template-kwargs (a request can still re-enable thinking with its own chat_template_kwargs):

furiosa-llm serve furiosa-ai/EXAONE-4.5-33B-FP8 \
  --reasoning-parser qwen3 \
  --default-chat-template-kwargs '{"enable_thinking": false}'

Tool calling. With the server launched using --enable-auto-tool-choice --tool-call-parser hermes (see Launch the server), pass tools in the request and let the model decide when to call them. See the Tool Calling guide for a complete client example and details on tool-choice options.

Learn more#