Instructions to use avar6/GLM-5.3-Flash-BF16-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 avar6/GLM-5.3-Flash-BF16-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 avar6/GLM-5.3-Flash-BF16-gguf:BF16
# Run inference directly in the terminal:
llama cli -hf avar6/GLM-5.3-Flash-BF16-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf avar6/GLM-5.3-Flash-BF16-gguf:BF16
# Run inference directly in the terminal:
llama cli -hf avar6/GLM-5.3-Flash-BF16-gguf:BF16

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 avar6/GLM-5.3-Flash-BF16-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf avar6/GLM-5.3-Flash-BF16-gguf:BF16

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 avar6/GLM-5.3-Flash-BF16-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf avar6/GLM-5.3-Flash-BF16-gguf:BF16

Use Docker

docker model run hf.co/avar6/GLM-5.3-Flash-BF16-gguf:BF16

LM Studio
Jan
Ollama
How to use avar6/GLM-5.3-Flash-BF16-gguf with Ollama:
```
ollama run hf.co/avar6/GLM-5.3-Flash-BF16-gguf:BF16
```
Unsloth Desktop

How to use avar6/GLM-5.3-Flash-BF16-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf avar6/GLM-5.3-Flash-BF16-gguf:BF16

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": "avar6/GLM-5.3-Flash-BF16-gguf:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use avar6/GLM-5.3-Flash-BF16-gguf with Docker Model Runner:
```
docker model run hf.co/avar6/GLM-5.3-Flash-BF16-gguf:BF16
```

Lemonade

How to use avar6/GLM-5.3-Flash-BF16-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull avar6/GLM-5.3-Flash-BF16-gguf:BF16

Run and chat with the model

lemonade run user.GLM-5.3-Flash-BF16-gguf-BF16

List all available models

lemonade list

Hermes Agent

How to use avar6/GLM-5.3-Flash-BF16-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 avar6/GLM-5.3-Flash-BF16-gguf:BF16

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 avar6/GLM-5.3-Flash-BF16-gguf:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use avar6/GLM-5.3-Flash-BF16-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf avar6/GLM-5.3-Flash-BF16-gguf:BF16

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 "avar6/GLM-5.3-Flash-BF16-gguf:BF16" \
  --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"

experimental ggufs with vision support by timkronos. Requires this llamacpp fork https://github.com/timkhronos/llama.cpp/tree/GLM5.3-Flash. Link to the current pr for it https://github.com/ggml-org/llama.cpp/pull/27773

also for clarity, these are quantitized from the bf16 safetensors as it says in the title. But the default repo is fp8. I've linked the default repo as the base since its where people looking for llamacpp compatible ggufs for glm 5.3 flash will look

Current Quant Recipes:

IQ2_XXS-2.32bpw ~ 92gb
IQ2_S-2.83bpw ~ 114gb
Q2_K-Q3_K-3.20bpw ~ 128gb
Q3_S-3.60bpw ~ 144gb
Q3_XL-3.86bpw ~ 155gb

blk.45→Q8_0, ssm→F32/Q8_0, indexer→F32 on all quants

Updates:

08/27/2026 2pm: the mmproj has been updated, should see improvements in vision
08/28/2026 8am: uploaded all new quants. New set of mixed precision i and k quants using Aes Sedai's gguf recipe. The mtp tensor is kept at Q8 for performance. MTP currently working in timkronos's mtp fork
08/28/2026 6pm: oops all new quants needed
08/29/2026 3pm: ok fast moving few days. Quants going back up today. PR looks close to merging. I will keep my quants up until Aes Sedai makes his which may only be a few days. I will keep any odd quants he does not make
08/30/2026: Aes Sedai has uploaded his glm flash quants so I recommend checking out his repo: https://huggingface.co/AesSedai/GLM-5.3-Flash-GGUF
I may upload more small sized mixed quants but at this point I need to save space on my hf account and will delete the q5 (same as Aes's anyway)

Downloads last month: 7,557

GGUF

Model size

321B params

Architecture

glm5-next

Hardware compatibility

2-bit

View +3 variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for avar6/GLM-5.3-Flash-BF16-gguf

Base model

zai-org/GLM-5.3-Flash

Quantized

(83)

this model