Instructions to use unsloth/GLM-4.7-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/GLM-4.7-Flash-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="unsloth/GLM-4.7-Flash-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("unsloth/GLM-4.7-Flash-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unsloth/GLM-4.7-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/GLM-4.7-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/GLM-4.7-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": "unsloth/GLM-4.7-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

SGLang

How to use unsloth/GLM-4.7-Flash-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "unsloth/GLM-4.7-Flash-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/GLM-4.7-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "unsloth/GLM-4.7-Flash-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/GLM-4.7-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use unsloth/GLM-4.7-Flash-GGUF with Ollama:
```
ollama run hf.co/unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/GLM-4.7-Flash-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/GLM-4.7-Flash-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/GLM-4.7-Flash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/GLM-4.7-Flash-GGUF to start chatting

How to use unsloth/GLM-4.7-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/GLM-4.7-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use unsloth/GLM-4.7-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 unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

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 "unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL" \
  --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"

Docker Model Runner
How to use unsloth/GLM-4.7-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/GLM-4.7-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/GLM-4.7-Flash-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.GLM-4.7-Flash-GGUF-UD-Q4_K_XL

List all available models

lemonade list

GGUF model with architecture deepseek2 is not supported yet.

#11

by T1-Faker1 - opened Jan 22

Discussion

T1-Faker1

Jan 22

(vadmin) vadmin@vadmin:~$ vllm serve ./GLM-4.7-Flash-Q4_K_M.gguf --tokenizer glm4_tokenizer/ZhipuAI/glm-4-9b-chat
(APIServer pid=12531) INFO 01-22 01:25:04 [api_server.py:872] vLLM API server version 0.14.0rc2.dev199+gc80f92c14
(APIServer pid=12531) INFO 01-22 01:25:04 [utils.py:267] non-default args: {'model_tag': './GLM-4.7-Flash-Q4_K_M.gguf', 'model': './GLM-4.7-Flash-Q4_K_M.gguf', 'tokenizer': 'glm4_tokenizer/ZhipuAI/glm-4-9b-chat'}
(APIServer pid=12531) Traceback (most recent call last):
(APIServer pid=12531) File "/home/vadmin/.venv/bin/vllm", line 10, in
(APIServer pid=12531) sys.exit(main())
(APIServer pid=12531) ^^^^^^
(APIServer pid=12531) File "/home/vadmin/vllm/vllm/entrypoints/cli/main.py", line 73, in main
(APIServer pid=12531) args.dispatch_function(args)
(APIServer pid=12531) File "/home/vadmin/vllm/vllm/entrypoints/cli/serve.py", line 60, in cmd
(APIServer pid=12531) uvloop.run(run_server(args))
(APIServer pid=12531) File "/home/vadmin/.venv/lib/python3.12/site-packages/uvloop/init.py", line 96, in run
(APIServer pid=12531) return __asyncio.run(
(APIServer pid=12531) ^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu/lib/python3.12/asyncio/runners.py", line 195, in run
(APIServer pid=12531) return runner.run(main)
(APIServer pid=12531) ^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu/lib/python3.12/asyncio/runners.py", line 118, in run
(APIServer pid=12531) return self._loop.run_until_complete(task)
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
(APIServer pid=12531) File "/home/vadmin/.venv/lib/python3.12/site-packages/uvloop/init.py", line 48, in wrapper
(APIServer pid=12531) return await main
(APIServer pid=12531) ^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/vllm/vllm/entrypoints/openai/api_server.py", line 919, in run_server
(APIServer pid=12531) await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)
(APIServer pid=12531) File "/home/vadmin/vllm/vllm/entrypoints/openai/api_server.py", line 938, in run_server_worker
(APIServer pid=12531) async with build_async_engine_client(
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu/lib/python3.12/contextlib.py", line 210, in aenter
(APIServer pid=12531) return await anext(self.gen)
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/vllm/vllm/entrypoints/openai/api_server.py", line 146, in build_async_engine_client
(APIServer pid=12531) async with build_async_engine_client_from_engine_args(
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu/lib/python3.12/contextlib.py", line 210, in aenter
(APIServer pid=12531) return await anext(self.gen)
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/vllm/vllm/entrypoints/openai/api_server.py", line 172, in build_async_engine_client_from_engine_args
(APIServer pid=12531) vllm_config = engine_args.create_engine_config(usage_context=usage_context)
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/vllm/vllm/engine/arg_utils.py", line 1360, in create_engine_config
(APIServer pid=12531) maybe_override_with_speculators(
(APIServer pid=12531) File "/home/vadmin/vllm/vllm/transformers_utils/config.py", line 528, in maybe_override_with_speculators
(APIServer pid=12531) config_dict, _ = PretrainedConfig.get_config_dict(
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/.venv/lib/python3.12/site-packages/transformers/configuration_utils.py", line 662, in get_config_dict
(APIServer pid=12531) config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/.venv/lib/python3.12/site-packages/transformers/configuration_utils.py", line 753, in _get_config_dict
(APIServer pid=12531) config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)["config"]
(APIServer pid=12531) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=12531) File "/home/vadmin/.venv/lib/python3.12/site-packages/transformers/modeling_gguf_pytorch_utils.py", line 431, in load_gguf_checkpoint
(APIServer pid=12531) raise ValueError(f"GGUF model with architecture {architecture} is not supported yet.")
(APIServer pid=12531) ValueError: GGUF model with architecture deepseek2 is not supported yet.

T1-Faker1 changed discussion title from vllm 好像跑起来有点问题 to GGUF model with architecture deepseek2 is not supported yet. Jan 22

danielhanchen

Unsloth AI org Jan 22

I'm not sure if the GGUF works in vLLM as of yet. Only llama.cpp supported backends

martinmarko

Jan 23

I'm running into this issue when trying to use it with transformers directly. Is there a workaround until the library gets support for it?

MODEL_PATH = "unsloth/GLM-4.7-Flash-GGUF"
GGUF_FILE = "GLM-4.7-Flash-UD-Q2_K_XL.gguf"

tokenizer = AutoTokenizer.from_pretrained(
    MODEL_PATH,
    gguf_file=GGUF_FILE,
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    gguf_file=GGUF_FILE,
    device_map="auto",
)

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