Instructions to use vcruz305/GLM-5.3-Flash-DFlash2-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 vcruz305/GLM-5.3-Flash-DFlash2-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 vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf vcruz305/GLM-5.3-Flash-DFlash2-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 vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf vcruz305/GLM-5.3-Flash-DFlash2-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 vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16

Use Docker

docker model run hf.co/vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16

LM Studio
Jan

vLLM

How to use vcruz305/GLM-5.3-Flash-DFlash2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vcruz305/GLM-5.3-Flash-DFlash2-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": "vcruz305/GLM-5.3-Flash-DFlash2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16

Ollama
How to use vcruz305/GLM-5.3-Flash-DFlash2-GGUF with Ollama:
```
ollama run hf.co/vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16
```
Unsloth Desktop

How to use vcruz305/GLM-5.3-Flash-DFlash2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/GLM-5.3-Flash-DFlash2-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": "vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vcruz305/GLM-5.3-Flash-DFlash2-GGUF with Docker Model Runner:
```
docker model run hf.co/vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16
```

Lemonade

How to use vcruz305/GLM-5.3-Flash-DFlash2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16

Run and chat with the model

lemonade run user.GLM-5.3-Flash-DFlash2-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use vcruz305/GLM-5.3-Flash-DFlash2-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 vcruz305/GLM-5.3-Flash-DFlash2-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 vcruz305/GLM-5.3-Flash-DFlash2-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vcruz305/GLM-5.3-Flash-DFlash2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/GLM-5.3-Flash-DFlash2-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 "vcruz305/GLM-5.3-Flash-DFlash2-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"

GLM-5.3-Flash-DFlash2-GGUF

Community GGUF of Inco AI's DFlash 2 draft model for GLM-5.3-Flash.

This is not a standalone language model. It only drafts tokens for a GLM-5.3-Flash target under speculative decoding.

Spark serve recipe: vcruz305/GLM-5.3-Flash-DFlash2-DGX-Spark-recipe

Source and attribution

Original weights: incoai/GLM-5.3-Flash-DFlash2
Target model: zai-org/GLM-5.3-Flash
Method: DFlash 2: Keep Drafting Parallel
Code: z-lab/dflash

License follows the original: CC BY-NC-ND 4.0. For commercial use, contact contact@inco.ai.

If you use this GGUF, please cite Inco AI's DFlash 2 writeup and the DFlash paper:

@misc{inco2026dflash2,
  title  = {{DFlash 2: Keep Drafting Parallel}},
  author = {{Inco AI}},
  year   = {2026},
  month  = {August},
  url    = {https://inco.ai/blog/dflash2/}
}

@inproceedings{chen2026dflash,
  title     = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
  author    = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026}
}

Files

File	Type	Size	Notes
`GLM-5.3-Flash-DFlash2-BF16.gguf`	BF16 GGUF	2.191 GiB (2,352,022,432 B)	81 tensors, arch `dflash`

GGUF metadata from this convert: dflash.block_size=8, conv_kernel_size=2, conv_group_size=16, selector_rank=256, selector_top_k=16, target_layers=[6,15,25,34,43]. Vocab is the GLM-5.3 tokenizer (154880).

Q8_0 / Q4_K_M drafts were measured on a Spark and are not in this repo.

Run

Needs llama.cpp with DFlash 2 (grouped dynamic conv + candidate selector). That is ggml-org/llama.cpp#27342, also on vcruz305/llama.cpp main / glm5next-mtp at 6f5ac9a (+ aarch64 cmath 4a06ec6). Pair with a GLM-5.3-Flash target GGUF such as vcruz305/GLM-5.3-Flash-GGUF.

--spec-draft-n-max clamps to 7 (block_size - 1). Do not combine with --spec-type draft-mtp on the same server.

hf download vcruz305/GLM-5.3-Flash-DFlash2-GGUF GLM-5.3-Flash-DFlash2-BF16.gguf --local-dir GLM-5.3-Flash-DFlash2-GGUF

llama-server \
  -m GLM-5.3-Flash-Q2_K.gguf \
  -md GLM-5.3-Flash-DFlash2-GGUF/GLM-5.3-Flash-DFlash2-BF16.gguf \
  --spec-type draft-dflash --spec-draft-n-max 7 --spec-draft-p-min 0.30 \
  -fa on -ctk q8_0 -ctv q8_0 --jinja \
  -c 98304 -np 1 --no-kv-unified --fit off

Measured (one DGX Spark GB10, 2026-08-27)

Target: GLM-5.3-Flash-Q2_K.gguf. Tool: llama-speculative-simple, greedy, seed 42.

Unique prompt "The capital of France is" (n=64, FA on, no q8 KV): 17.58 t/s, accept 31.41%.

Repetitive bench file, FA + q8 KV, -c 2048, n=128:

draft	n_max	t/s	accept
MTP-3 control	3	28.23	73.2%
DFlash2 BF16	7	41.40	94.4%
DFlash2 Q4_K_M	7	43.43	94.4%

Do not quote 94% as a model score — that file is a repeated sentence.

Ctx ladder (Q4_K_M, n_max=7, p_min=0.30, FA+q8, n=64):

ctx	t/s	accept
8,192	39.58	89.9%
32,768	39.53	89.9%
65,536	39.71	89.9%
98,304	38.86	89.9%

96k is the last measured OK (114,820 MiB of 124,610). 114k/128k not re-run for DFlash2.

Convert

python convert_hf_to_gguf.py incoai/GLM-5.3-Flash-DFlash2 \
  --target-model-dir zai-org/GLM-5.3-Flash-BF16 \
  --outtype bf16 \
  --outfile GLM-5.3-Flash-DFlash2-BF16.gguf

--target-model-dir is tokenizer + config.json only. Converted with vcruz305/llama.cpp 6f5ac9a.

Downloads last month: 1,204

GGUF

Model size

1B params

Architecture

dflash

Hardware compatibility

16-bit

Model tree for vcruz305/GLM-5.3-Flash-DFlash2-GGUF

Base model

zai-org/GLM-5.3-Flash

Finetuned

incoai/GLM-5.3-Flash-DFlash2

Quantized

(3)

this model