Instructions to use backpack-run/GLM-5.3-Flash-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 backpack-run/GLM-5.3-Flash-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 backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/GLM-5.3-Flash-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 backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/GLM-5.3-Flash-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 backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf backpack-run/GLM-5.3-Flash-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 backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use backpack-run/GLM-5.3-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "backpack-run/GLM-5.3-Flash-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": "backpack-run/GLM-5.3-Flash-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/backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M

Ollama
How to use backpack-run/GLM-5.3-Flash-GGUF with Ollama:
```
ollama run hf.co/backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M
```
Unsloth Desktop

How to use backpack-run/GLM-5.3-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf backpack-run/GLM-5.3-Flash-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": "backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use backpack-run/GLM-5.3-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M
```

Lemonade

How to use backpack-run/GLM-5.3-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-5.3-Flash-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use backpack-run/GLM-5.3-Flash-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 backpack-run/GLM-5.3-Flash-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 backpack-run/GLM-5.3-Flash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use backpack-run/GLM-5.3-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf backpack-run/GLM-5.3-Flash-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 "backpack-run/GLM-5.3-Flash-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"

GLM-5.3-Flash — Backpack GGUF

GGUF quantizations of zai-org/GLM-5.3-Flash, packaged for llama.cpp-compatible image-and-text inference and Backpack.

Model

Property	Value
Original model	`zai-org/GLM-5.3-Flash`
Original publisher	zai-org
Upstream revision	`690b705278a3a58e538fcb37c2ca8b5f9511213c`
Architecture	Glm5NextForConditionalGeneration
Parameters	321,323,031,390
Context length	Not declared
Input modalities	text, image
Output modalities	text
License	mit

Available packages

Quantization	Size	Approx. RAM	Recommended for
Q4_K_M	180.5 GiB	263.78 GB	Most users

Memory values are estimates, not guarantees. Runtime configuration and context length change actual use.

Multimodal projector

File	Precision	Size
`GLM-5.3-Flash-mmproj-F16.gguf`	F16	1.1 GiB

The projector is required for image input and must be used with one of the language-model GGUF files above.

Backpack recommendation

Recommended: Q4_K_M. It usually offers a practical quality, size, and speed balance for local inference.

Run with llama.cpp

Using the llama.cpp revision recorded below:

llama-mtmd-cli --model GLM-5.3-Flash-Q4_K_M.gguf --mmproj GLM-5.3-Flash-mmproj-F16.gguf --image image.jpg --prompt "Describe this image."

Run with Backpack

These artifacts and backpack-model.yaml are prepared for the Backpack AI workspace.

Validation

Artifact integrity and GGUF metadata validation are the publication requirements. Runtime load, inference, and tokenizer results are reported independently and do not imply a certification or endorsement.

Package	Integrity	Load	Inference	Tokenizer
Q4_K_M	passed	failed	skipped	skipped

Runtime execution validation has not completed successfully for every artifact. Treat the affected package as experimental with the pinned toolchain until downstream runtime testing is complete.

Packaged: 2026-09-06T07:07:13.528564+00:00
llama.cpp toolchain revision: 8134115f88ed8018474e7db69afcfe97fb097fc4
SHA-256 checksums: see checksums.sha256
GLM-5.3-Flash-Q4_K_M.gguf: 3e1f1720e869d98acd55a8f94b5efd78814a6ba0a2c2e4e609d637e9cca60406
GLM-5.3-Flash-mmproj-F16.gguf: f64a2e935c899224054258d2372d9ad4c19b760141044292fa6ee0fb5ff36624

Provenance

The source model was resolved to immutable revision 690b705278a3a58e538fcb37c2ca8b5f9511213c. It was converted with llama.cpp's convert_hf_to_gguf.py, including its multimodal projector, and quantized with llama-quantize; the exact toolchain revision is recorded above and in backpack-model.yaml.

License and attribution

Upstream declares mit. Review the upstream model card and comply with all applicable terms.

Backpack does not claim ownership of the original model. These artifacts are packaged and quantized distributions of the upstream model.

Disclaimer

Quantization can alter output quality. Memory estimates vary with runtime configuration, context length, and hardware.

Downloads last month: 96

GGUF

Model size

321B params

Architecture

glm5-next

Hardware compatibility

4-bit

Model tree for backpack-run/GLM-5.3-Flash-GGUF

Base model

zai-org/GLM-5.3-Flash

Quantized

(97)

this model