Instructions to use prithivMLmods/microsoft-AesCode-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/microsoft-AesCode-8B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/microsoft-AesCode-8B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/microsoft-AesCode-8B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/microsoft-AesCode-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/microsoft-AesCode-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/microsoft-AesCode-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/microsoft-AesCode-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/microsoft-AesCode-8B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/microsoft-AesCode-8B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/microsoft-AesCode-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/microsoft-AesCode-8B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/microsoft-AesCode-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/microsoft-AesCode-8B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/microsoft-AesCode-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/microsoft-AesCode-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/microsoft-AesCode-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.microsoft-AesCode-8B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/microsoft-AesCode-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/microsoft-AesCode-8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prithivMLmods/microsoft-AesCode-8B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

microsoft-AesCode-8B-GGUF

AesCode-8B is Microsoft's 8B vision-language model, built on Qwen3-VL-8B-Instruct, for generating information-rich visual artifacts such as slides, posters, dashboards and reports as structured, editable HTML/CSS. It addresses a gap between two kinds of generator: code models can't see how layout, hierarchy and color come together on the canvas, while image generators compose attractive pages but misrender text, numbers and logical relationships. AesCode therefore takes an image generated from the same prompt as an aesthetic reference and follows the prompt for the required content. Tables come out as HTML table structures and charts as ECharts specifications, so both stay inspectable and verifiable. Training was cold-start SFT on 3,000 demonstrations, followed by GDPO reinforcement learning for 400 steps over 7,408 prompts, using verl and no separately trained reward model. Each target is represented as a design graph, and the reward is split into seven decoupled channels: execution, text, boundary, tablechart, layout, whitespace and design. Deterministic verifiers score what can be parsed from the code and its rendering, and a VLM judge scores the rest against a sample-specific Visual Graph Rubric. On the card's 300 infographic samples, AesCode-8B scores 82.94 Overall (Rule 90.07, Visual 75.80). That is above the reference-conditioned GPT-5.5 (81.28) and Claude Opus 4.8 (80.39), and 31.0 points above its reference-conditioned Qwen3-VL-8B backbone. The 32B companion model leads at 85.89. Style is a ceiling for every system, with no model above 60. The reference image is optional, since withholding it costs AesCode-8B only 1.00 Visual point, versus 19.55 for the base model, though quality is highest with it. The model loads through standard Transformers or vLLM, with the training code, verifier stack and rubric builder released on GitHub. The paper is still listed as under review. It is released under Apache 2.0.

Model Files

File Name	Quant Type	File Size	File Link	Description
AesCode-8B.BF16.gguf	BF16	16.4 GB	Link	Full BF16 weights. Highest quality, largest file size.
AesCode-8B.Q3_K_L.gguf	Q3_K_L	4.43 GB	Link	Lower quality but usable, good for low RAM availability.
AesCode-8B.Q3_K_M.gguf	Q3_K_M	4.12 GB	Link	Low quality.
AesCode-8B.Q4_K_M.gguf	Q4_K_M	5.03 GB	Link	Good quality, default size for most use cases, recommended.
AesCode-8B.Q4_K_S.gguf	Q4_K_S	4.8 GB	Link	Slightly lower quality with more space savings, recommended.
AesCode-8B.Q5_K_M.gguf	Q5_K_M	5.85 GB	Link	High quality, recommended.
AesCode-8B.Q5_K_S.gguf	Q5_K_S	5.72 GB	Link	High quality, recommended.
AesCode-8B.Q6_K.gguf	Q6_K	6.73 GB	Link	Very high quality, near perfect, recommended.
AesCode-8B.Q8_0.gguf	Q8_0	8.71 GB	Link	Extremely high quality, generally unneeded but max available quant.
AesCode-8B.mmproj-bf16.gguf	mmproj-bf16	1.16 GB	Link	Multimodal projection file in BF16 format. Used for vision/language models.