Instructions to use bartowski/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/FrontisAI_Frontis-MA1-35B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M

Ollama
How to use bartowski/FrontisAI_Frontis-MA1-35B-GGUF with Ollama:
```
ollama run hf.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use bartowski/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bartowski/FrontisAI_Frontis-MA1-35B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/FrontisAI_Frontis-MA1-35B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.FrontisAI_Frontis-MA1-35B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bartowski/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bartowski/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-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"

FrontisAI_Frontis-MA1-35B-GGUF / README.md

bartowski

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	---
	quantized_by: bartowski
	pipeline_tag: image-text-to-text
	base_model_relation: quantized
	base_model: FrontisAI/Frontis-MA1-35B
	tags:
	- openmle
	- frontis-ma1
	- machine-learning-engineering
	- autoresearch
	- agent
	- coding
	- qwen3
	- qwen3.6
	- moe
	- multimodal
	- post-training
	license: cc-by-nc-4.0
	---

	## Llamacpp imatrix Quantizations of Frontis-MA1-35B by FrontisAI

	Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b10262">b10262</a> for quantization.

	Original model: https://huggingface.co/FrontisAI/Frontis-MA1-35B

	Model details:
	- Parameter count: 36B
	- Input support: text, image (with mmproj file) - [details](#multimodal)
	- MTP: yes - [details](#mtp)
	- imatrix: yes - [details](#imatrix)

	[How to run](#how-to-run)

	## Prompt format

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

	Don't know which to choose? Grab [Q4_K_M](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q4_K_M.gguf) (21.86GB) - usually a good mix of size and performance. Download instructions available [here](#downloading-using-the-hugging-face-cli)

	## Available files:

	\| Filename \| Quant type \| File Size \| Split \| Description \|
	\| -------- \| ---------- \| --------- \| ----- \| ----------- \|
	\| [FrontisAI_Frontis-MA1-35B-bf16.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/tree/main/FrontisAI_Frontis-MA1-35B-bf16) \| bf16 \| 71.07GB \| true \| Full BF16 weights. \|
	\| [FrontisAI_Frontis-MA1-35B-Q8_0.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q8_0.gguf) \| Q8_0 \| 37.81GB \| false \| Extremely high quality, generally unneeded but max available quant. \|
	\| [FrontisAI_Frontis-MA1-35B-Q6_K_L.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q6_K_L.gguf) \| Q6_K_L \| 30.77GB \| false \| Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q6_K.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q6_K.gguf) \| Q6_K \| 30.53GB \| false \| Very high quality, near perfect, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q5_K_L.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q5_K_L.gguf) \| Q5_K_L \| 25.81GB \| false \| Uses Q8_0 for embed and output weights. High quality, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q5_K_M.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q5_K_M.gguf) \| Q5_K_M \| 25.49GB \| false \| High quality, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q5_K_S.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q5_K_S.gguf) \| Q5_K_S \| 24.63GB \| false \| High quality, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q4_1.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q4_1.gguf) \| Q4_1 \| 22.45GB \| false \| Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. \|
	\| [FrontisAI_Frontis-MA1-35B-Q4_K_L.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q4_K_L.gguf) \| Q4_K_L \| 22.24GB \| false \| Uses Q8_0 for embed and output weights. Good quality, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q4_K_M.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q4_K_M.gguf) \| Q4_K_M \| 21.86GB \| false \| Good quality, default size for most use cases, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q4_K_S.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q4_K_S.gguf) \| Q4_K_S \| 21.07GB \| false \| Slightly lower quality with more space savings, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q4_0.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q4_0.gguf) \| Q4_0 \| 20.42GB \| false \| Legacy format, kept for compatibility with older tools. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ4_NL.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ4_NL.gguf) \| IQ4_NL \| 20.33GB \| false \| Similar to IQ4_XS, but slightly larger. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ4_XS.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ4_XS.gguf) \| IQ4_XS \| 19.28GB \| false \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-Q3_K_XL.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q3_K_XL.gguf) \| Q3_K_XL \| 17.80GB \| false \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ3_M.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ3_M.gguf) \| IQ3_M \| 17.37GB \| false \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \|
	\| [FrontisAI_Frontis-MA1-35B-Q3_K_L.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q3_K_L.gguf) \| Q3_K_L \| 17.36GB \| false \| Lower quality but usable, good for low RAM availability. \|
	\| [FrontisAI_Frontis-MA1-35B-Q3_K_M.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q3_K_M.gguf) \| Q3_K_M \| 16.70GB \| false \| Low quality. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ3_XS.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ3_XS.gguf) \| IQ3_XS \| 16.69GB \| false \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \|
	\| [FrontisAI_Frontis-MA1-35B-Q3_K_S.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q3_K_S.gguf) \| Q3_K_S \| 15.98GB \| false \| Low quality, not recommended. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ3_XXS.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ3_XXS.gguf) \| IQ3_XXS \| 15.34GB \| false \| Lower quality, new method with decent performance, comparable to Q3 quants. \|
	\| [FrontisAI_Frontis-MA1-35B-Q2_K_L.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q2_K_L.gguf) \| Q2_K_L \| 13.58GB \| false \| Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. \|
	\| [FrontisAI_Frontis-MA1-35B-Q2_K.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-Q2_K.gguf) \| Q2_K \| 13.09GB \| false \| Very low quality but surprisingly usable. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ2_M.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ2_M.gguf) \| IQ2_M \| 12.54GB \| false \| Relatively low quality, uses SOTA techniques to be surprisingly usable. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ2_S.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ2_S.gguf) \| IQ2_S \| 11.49GB \| false \| Low quality, uses SOTA techniques to be usable. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ2_XS.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ2_XS.gguf) \| IQ2_XS \| 11.27GB \| false \| Low quality, uses SOTA techniques to be usable. \|
	\| [FrontisAI_Frontis-MA1-35B-IQ2_XXS.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-IQ2_XXS.gguf) \| IQ2_XXS \| 10.26GB \| false \| Very low quality, uses SOTA techniques to be usable. \|

	Download a specific file:

	```
	hf download bartowski/FrontisAI_Frontis-MA1-35B-GGUF --include "FrontisAI_Frontis-MA1-35B-Q4_K_M.gguf" --local-dir ./
	```

	## Downloading using the Hugging Face CLI

	<details>
	<summary>Click to view download instructions</summary>

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

	```
	pip install -U "huggingface_hub[cli]"
	```

	Download a specific file:

	```
	hf download bartowski/FrontisAI_Frontis-MA1-35B-GGUF --include "FrontisAI_Frontis-MA1-35B-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/FrontisAI_Frontis-MA1-35B-GGUF --include "FrontisAI_Frontis-MA1-35B-bf16/*" --local-dir ./
	```

	You can either specify a new local-dir (FrontisAI_Frontis-MA1-35B-bf16) or download them all in place (./)

	</details>

	## How to run

	These quants run with [llama.cpp](https://github.com/ggml-org/llama.cpp) - installable in one line via [llama.app](https://llama.app/):

	```
	curl -LsSf https://llama.app/install.sh \| sh
	llama-server -hf bartowski/FrontisAI_Frontis-MA1-35B-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 b10262 - if this model's architecture is newly supported, you'll need that release or newer to run them.

	They also work in: [LM Studio](https://lmstudio.ai/) · [koboldcpp](https://github.com/LostRuins/koboldcpp) · [ramalama](https://github.com/containers/ramalama) · [Jan AI](https://www.jan.ai/) · [Text Generation Web UI](https://github.com/oobabooga/text-generation-webui) · [LoLLMs](https://github.com/ParisNeo/lollms) · [Atomic Chat](https://atomic.chat/)

	## Multimodal

	This model supports image input. Alongside the quants, this repo includes the multimodal projector files [mmproj-FrontisAI_Frontis-MA1-35B-f16.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/mmproj-FrontisAI_Frontis-MA1-35B-f16.gguf) and [mmproj-FrontisAI_Frontis-MA1-35B-bf16.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/mmproj-FrontisAI_Frontis-MA1-35B-bf16.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`.

	## MTP

	This model has MTP (Multi-Token Prediction) layers, and they are included in these quants

	MTP layers act as a built-in draft model, letting llama.cpp run speculative decoding for faster generation. To use them, add the following flag to your llama.cpp command:

	```
	--spec-type draft-mtp
	```

	Note: the MTP layers are stored at Q4_0 in the imatrix quants (except for the Q8_0 quant), since imatrix calibration does not exercise them. Q4_0 is chosen for its speed which massively benefits MTP performance.

	## imatrix

	All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/82ae9b520227f57d79ba04add13d0d0d). The imatrix is available here: [FrontisAI_Frontis-MA1-35B-imatrix.gguf](https://huggingface.co/bartowski/FrontisAI_Frontis-MA1-35B-GGUF/blob/main/FrontisAI_Frontis-MA1-35B-imatrix.gguf).

	## 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](https://github.com/ggml-org/llama.cpp/pull/9921). 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?

	<details>
	<summary>Click here for details</summary>

	An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)

	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](https://huggingface.co/settings/local-apps) 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](https://github.com/ggml-org/llama.cpp/wiki/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.

	</details>

	## Credits

	Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

	Thank you ZeroWw for the inspiration to experiment with embed/output.

	Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski