Instructions to use 6block/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 6block/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 6block/GLM-5.3-Flash-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf 6block/GLM-5.3-Flash-GGUF:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 6block/GLM-5.3-Flash-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf 6block/GLM-5.3-Flash-GGUF:IQ1_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 6block/GLM-5.3-Flash-GGUF:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf 6block/GLM-5.3-Flash-GGUF:IQ1_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 6block/GLM-5.3-Flash-GGUF:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 6block/GLM-5.3-Flash-GGUF:IQ1_M

Use Docker

docker model run hf.co/6block/GLM-5.3-Flash-GGUF:IQ1_M

LM Studio
Jan

vLLM

How to use 6block/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 "6block/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": "6block/GLM-5.3-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/6block/GLM-5.3-Flash-GGUF:IQ1_M

Ollama
How to use 6block/GLM-5.3-Flash-GGUF with Ollama:
```
ollama run hf.co/6block/GLM-5.3-Flash-GGUF:IQ1_M
```
Unsloth Desktop

How to use 6block/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 6block/GLM-5.3-Flash-GGUF:IQ1_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": "6block/GLM-5.3-Flash-GGUF:IQ1_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 6block/GLM-5.3-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/6block/GLM-5.3-Flash-GGUF:IQ1_M
```

Lemonade

How to use 6block/GLM-5.3-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 6block/GLM-5.3-Flash-GGUF:IQ1_M

Run and chat with the model

lemonade run user.GLM-5.3-Flash-GGUF-IQ1_M

List all available models

lemonade list

Hermes Agent

How to use 6block/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 6block/GLM-5.3-Flash-GGUF:IQ1_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 6block/GLM-5.3-Flash-GGUF:IQ1_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 6block/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 6block/GLM-5.3-Flash-GGUF:IQ1_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 "6block/GLM-5.3-Flash-GGUF:IQ1_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 GGUF

GGUF quantizations of zai-org/GLM-5.3-Flash, made with llama.cpp.

Chinese version: README_zh.md

320B total parameters, 18B active. 45 layers with hybrid attention: 34 KDA linear-attention layers interleaved with 11 DSA sparse-attention layers, built on MLA, wrapped in Manifold-Constrained Hyper-Connections (mHC). 288 MoE experts with top-8 routing plus a shared expert. Context length up to 1M.

Quantized from the official BF16 weights. Every tier is imatrix-calibrated and ships as 15 shards.

Quantizations

Tier	Size	Shards	BPW	PPL
master (BF16, not in this repo)	583 GiB	15	16.00	6.6974 ± 0.34871
`IQ4_XS`	155 GiB	15	4.27	7.1537 ± 0.37538
`IQ3_XXS`	112 GiB	15	3.09	8.2485 ± 0.41877
`IQ2_XS`	85 GiB	15	2.35	20.2651 ± 1.10727
`IQ1_M`	65 GiB	15	1.80	73.9234 ± 4.73259

The master row is not a file in this repo. It is listed so the numbers above have a reference point — and this time the BF16 master did fit on the machine used for the PPL measurements, so the tiers are compared against a real baseline.

The layers that would hurt most under low-bit compression are protected (measured: on IQ4_XS this costs a few GiB over the bare tier):

Tensors	Type	Reason
`mlp.gate.weight`	F32	MoE router; compressing it routes to the wrong experts
`mlp.shared_experts.*`	Q8_0	the shared expert runs on every token
`hc_attn_` / `hc_ffn_`	F32	hyper-connection streams, every layer
`self_attn.A_log` / `k_conv1d` / `dt_bias`	F32	KDA linear-attention state; low bit-width destroys long-range recall
`mlp.gate_proj` / `up_proj` / `down_proj`	Q8_0	the 3 dense MLP layers before the MoE block
`token_embd` / `output`	Q6_K	a global type would otherwise squeeze these hard

With 288 experts the expert layers dominate the file, so protecting everything else is cheap.

One NextN (MTP) layer exists in the checkpoint but is excluded at conversion time via --no-mtp; these files carry the main model's tensors only.

Usage

# Point at the first shard; llama.cpp finds the rest on its own.
# Leave -c off as well on the first run — the same fitting that picks the layer
# split will also reduce the context size if that is what it takes to fit.
llama-cli -m GLM-5.3-Flash-IQ4_XS-00001-of-00015.gguf

Do not pass -ngl manually. llama.cpp fits layers to free VRAM by itself, and any explicit -ngl value — including 0 — aborts that fitting:

common_fit_params: failed to fit params to free device memory:
                   n_gpu_layers already set by user to 99, abort
llama_model_load: error loading model: unable to allocate CUDA0 buffer

Leave -ngl off and let the fitting run.

Every tier ships as 15 shards (the master is 15 shards of ~39 GiB each for the first 14 and ~9 GiB for the last). Download all 15 into one directory — you only ever name -00001-of-00015 on the command line.

About the PPL numbers

wikitext-2 test, n_ctx=512, 12 chunks, every tier through the exact same command, measured on 7× H100 80GB. These numbers are only comparable within this table. Do not compare them against PPL figures published by other repos — different corpora and chunk counts make absolute values meaningless across setups.

The BF16 master row is the measured baseline (it fits in RAM on this box, unlike the 4.5 TB monsters). Note IQ2_XS and IQ1_M degrade sharply — they exist for when you must fit in a small footprint; prefer IQ4_XS or IQ3_XXS.

License

MIT, inherited from zai-org/GLM-5.3-Flash — see LICENSE for terms. Quantized by 6block.

Downloads last month: -

GGUF

Model size

313B params

Architecture

glm5next

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

Model tree for 6block/GLM-5.3-Flash-GGUF

Base model

zai-org/GLM-5.3-Flash

Quantized

(39)

this model