Instructions to use bartowski/AesCode-32B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bartowski/AesCode-32B-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 bartowski/AesCode-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/AesCode-32B-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 bartowski/AesCode-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/AesCode-32B-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 bartowski/AesCode-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/AesCode-32B-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 bartowski/AesCode-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/AesCode-32B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/AesCode-32B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/AesCode-32B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/AesCode-32B-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": "bartowski/AesCode-32B-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/bartowski/AesCode-32B-GGUF:Q4_K_M

Ollama
How to use bartowski/AesCode-32B-GGUF with Ollama:
```
ollama run hf.co/bartowski/AesCode-32B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use bartowski/AesCode-32B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/AesCode-32B-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": "bartowski/AesCode-32B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bartowski/AesCode-32B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/AesCode-32B-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/AesCode-32B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/AesCode-32B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AesCode-32B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bartowski/AesCode-32B-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 bartowski/AesCode-32B-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 bartowski/AesCode-32B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bartowski/AesCode-32B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/AesCode-32B-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 "bartowski/AesCode-32B-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"

Llamacpp imatrix Quantizations of AesCode-32B by microsoft

Using llama.cpp release b11490 for quantization.

Original model: https://huggingface.co/microsoft/AesCode-32B

Model details:

Parameter count: 33B (source checkpoint)
Input support: text, image (with mmproj file) - details
Speculative decoding: no
imatrix: yes - details

How to run

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Prompt format with tool definitions

<|im_start|>system
{system_prompt}

# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{"type": "function", "function": {"name": "get_stock_price", "description": "Get the current stock price", "parameters": {"type": "object", "properties": {"symbol": {"type": "string", "description": "The stock symbol, e.g. AAPL, GOOG"}}, "required": ["symbol"]}}}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call><|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Don't know which to choose? Grab Q4_K_M (20.49GB) - usually a good mix of size and performance. Download instructions available here

Available files:

Filename	Quant type	File Size	Split	Description
AesCode-32B-bf16.gguf	bf16	65.53GB	true	Full BF16 weights.
AesCode-32B-Q8_0.gguf	Q8_0	34.82GB	false	Extremely high quality, generally unneeded but max available quant.
AesCode-32B-Q6_K_L.gguf	Q6_K_L	28.36GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
AesCode-32B-Q6_K.gguf	Q6_K	27.98GB	false	Very high quality, near perfect, recommended.
AesCode-32B-Q5_K_M.gguf	Q5_K_M	23.79GB	false	High quality, recommended.
AesCode-32B-Q5_K_S.gguf	Q5_K_S	22.81GB	false	High quality, recommended.
AesCode-32B-Q4_K_L.gguf	Q4_K_L	21.06GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
AesCode-32B-Q4_1.gguf	Q4_1	20.90GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
AesCode-32B-Q4_K_M.gguf	Q4_K_M	20.49GB	false	Good quality, default size for most use cases, recommended.
AesCode-32B-Q4_K_S.gguf	Q4_K_S	19.14GB	false	Slightly lower quality with more space savings, recommended.
AesCode-32B-IQ4_NL.gguf	IQ4_NL	19.07GB	false	Similar to IQ4_XS, but slightly larger.
AesCode-32B-Q4_0.gguf	Q4_0	19.05GB	false	Legacy format, kept for compatibility with older tools.
AesCode-32B-IQ4_XS.gguf	IQ4_XS	18.12GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
AesCode-32B-Q3_K_L.gguf	Q3_K_L	17.52GB	false	Lower quality but usable, good for low RAM availability.
AesCode-32B-Q3_K_M.gguf	Q3_K_M	16.52GB	false	Low quality.
AesCode-32B-IQ3_M.gguf	IQ3_M	15.50GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
AesCode-32B-Q3_K_S.gguf	Q3_K_S	14.91GB	false	Low quality, not recommended.
AesCode-32B-IQ3_XS.gguf	IQ3_XS	14.37GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
AesCode-32B-IQ3_XXS.gguf	IQ3_XXS	13.60GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
AesCode-32B-Q2_K.gguf	Q2_K	13.01GB	false	Very low quality but surprisingly usable.
AesCode-32B-IQ2_M.gguf	IQ2_M	12.71GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.
AesCode-32B-IQ2_S.gguf	IQ2_S	12.02GB	false	Low quality, uses SOTA techniques to be usable.
AesCode-32B-IQ2_XS.gguf	IQ2_XS	11.41GB	false	Low quality, uses SOTA techniques to be usable.
AesCode-32B-IQ2_XXS.gguf	IQ2_XXS	10.63GB	false	Very low quality, uses SOTA techniques to be usable.

Download a specific file:

hf download bartowski/AesCode-32B-GGUF --include "AesCode-32B-Q4_K_M.gguf" --local-dir ./

Downloading using the Hugging Face CLI

Click to view download instructions

First, make sure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

Download a specific file:

hf download bartowski/AesCode-32B-GGUF --include "AesCode-32B-Q4_K_M.gguf" --local-dir ./

The files marked true in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

hf download bartowski/AesCode-32B-GGUF --include "AesCode-32B-bf16/*" --local-dir ./

You can either specify a new local-dir (AesCode-32B-bf16) or download them all in place (./)

How to run

These quants run with llama.cpp - installable in one line via llama.app:

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/AesCode-32B-GGUF:Q4_K_M

llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.

These quants were made with llama.cpp release b11490 - if this model's architecture is newly supported, you'll need that release or newer to run them.

They also work in: LM Studio · koboldcpp · ramalama · Jan AI · Text Generation Web UI · LoLLMs · Atomic Chat

Multimodal

This model supports image input. Alongside the quants, this repo includes the multimodal projector files mmproj-AesCode-32B-bf16.gguf and mmproj-AesCode-32B-f16.gguf, which pair with any quant above.

llama.cpp downloads the mmproj automatically when using -hf as shown above; if you're loading files manually, pass it with --mmproj.

imatrix

All quants made using imatrix option, with a calibration corpus rendered through this model's own chat template. The corpus pairs plain prose with tool-calling and reasoning conversations (corpus source data), encoded exactly as this model sees them at inference and processed with --parse-special, so chat-format special tokens contribute to the importance matrix. The corpus rendered for this model is included in this repo: AesCode-32B-calibration-v6.txt. The imatrix is available here: AesCode-32B-imatrix.gguf.

Calibration render details

{
  "recipe": "calibration-v6",
  "encoder": "chat_template",
  "library_versions": {
    "transformers": "5.9.0",
    "tokenizers": "0.22.2",
    "tiktoken": "0.14.0",
    "blobfile": "3.3.0",
    "huggingface_hub": "1.31.0"
  },
  "chunk_size": 512,
  "prose_chunks": 218,
  "tool_chunks": 268,
  "total_chunks": 486,
  "n_conversations": 164,
  "conversation_token_lengths": [
    319,
    847,
    573,
    1180,
    806,
    635,
    719,
    2084,
    1087,
    410,
    612,
    967,
    810,
    1200,
    481,
    669,
    2076,
    771,
    696,
    633,
    695,
    367,
    316,
    637,
    667,
    615,
    1009,
    580,
    547,
    785,
    1530,
    807,
    637,
    477,
    622,
    1229,
    999,
    455,
    509,
    718,
    425,
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    244,
    778,
    645,
    646,
    984,
    1545,
    879,
    1713,
    680,
    944,
    536,
    2326,
    2079,
    450,
    2395,
    489,
    666,
    456,
    332,
    377,
    1978,
    518,
    607,
    661,
    636,
    729,
    579,
    716,
    651,
    559,
    706,
    689,
    595,
    1115,
    401,
    786,
    1583,
    816,
    265,
    130,
    345,
    731,
    2760,
    1947,
    357,
    1277,
    472,
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    500,
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    368,
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    1158,
    2144,
    2082,
    328,
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    2071,
    1357,
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    1115,
    1091,
    1406,
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    522,
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    1977,
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    650,
    1695,
    1386,
    2020,
    2057,
    467,
    636,
    497,
    528,
    620,
    563,
    358,
    786,
    548,
    404,
    1095,
    617,
    516,
    739,
    1011
  ]
}

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information

llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in this PR. This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.

Which file should I choose?

Click here for details

An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Hugging Face can also do this math for you: add your hardware in your Local Apps settings and the model page will show which files fit.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.