Instructions to use pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-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 pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-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 pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama cli -hf pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

Use Docker

docker model run hf.co/pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

Llama new
LM Studio
Jan

vLLM

How to use pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-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": "pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-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/pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

Ollama
How to use pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF with Ollama:
```
ollama run hf.co/pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S
```
Unsloth Desktop

How to use pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

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": "pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S
```

Lemonade

How to use pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

Run and chat with the model

lemonade run user.MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF-Q3_K_S

List all available models

lemonade list

Hermes Agent

How to use pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-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 pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

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 pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S

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 "pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF:Q3_K_S" \
  --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"

🆕 Newer, better-quality build: pottokao/MiniMax-H3-TextEncoder-16GB-GGUF — official encoder weights, quantized per tensor by how much each one affects the H3 DiT, K-quants only. At the same 12.65 GB _vis size its conditioning error is about half of this Q3_K_S (7.9% vs 14.4%), with 13.7 / 14.8 GB options at 6.4% / 4.9%. ⚠️ The _vis file here stores the visual tower's 5-D patch_embed weight as-is; recent gguf versions refuse tensors with more than 4 dimensions, so it may fail to load on an up-to-date ComfyUI-GGUF. The new repo stores it the ComfyUI-GGUF way. 🆕 有新版：pottokao/MiniMax-H3-TextEncoder-16GB-GGUF（官方權重、依 H3 DiT 敏感度逐張量量化、只用 K 系列）。同樣 12.65 GB 誤差約為這顆的一半。這裡的 _vis 檔在新版 gguf 套件上可能載不進去。

📌 Heads-up: I've been focused on still-image work lately, so I don't have the bandwidth to help with video setups like MiniMax-H3 for now. The files stay up as they are — thanks for understanding! 最近重心放在平面攝影，影片類（像 MiniMax-H3）暫時比較沒辦法協助排錯。檔案會照舊留著，謝謝體諒！

MiniMax-H3 Text Encoder — Qwen3-VL-32B (abliterated) · GGUF

GGUF (llama.cpp) builds of the MiniMax-H3 text encoder — abliterated Qwen3-VL-32B, 50 layers, Q3_K_S (≈3.53 BPW).

Files

File	Size	Visual tower	Use for
`MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-Q3_K_S.gguf`	~11.5 GB	✗ (text only)	plain llama.cpp text-encoder use
`MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-Q3_K_S_vis.gguf`	~12.6 GB	✓ merged in	ComfyUI (MiniMax-H3 pipeline)

For ComfyUI use the _vis file. ComfyUI detects the H3 text encoder via the visual tower tensors (visual.deepstack_merger_list.* + model.layers.49.*); the plain file lacks them and won't be recognized. The _vis build has the BF16 visual tower merged back in.

Layers: 50 (H3 consumes the hidden state after layer 50) · Arch: qwen3vl · Base: abliterated Qwen3-VL-32B-Instruct.

For the vLLM-Omni-ready NVFP4-AWQ build, see pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-NVFP4-AWQ.

Abliterated / uncensored derivative, released as a component for the MiniMax-H3 text-to-video / image-to-video pipeline.

Downloads last month: 36,050

GGUF

Model size

27B params

Architecture

qwen3vl

Hardware compatibility

3-bit

Model tree for pottokao/MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF

Base model

Qwen/Qwen3-VL-32B-Instruct

Quantized

(51)

this model