Instructions to use axteam/GLM-4.7-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 axteam/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 axteam/GLM-4.7-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf axteam/GLM-4.7-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 axteam/GLM-4.7-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf axteam/GLM-4.7-Flash-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf axteam/GLM-4.7-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf axteam/GLM-4.7-Flash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/axteam/GLM-4.7-Flash-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use axteam/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 "axteam/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": "axteam/GLM-4.7-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/axteam/GLM-4.7-Flash-GGUF:Q4_K_M

Ollama
How to use axteam/GLM-4.7-Flash-GGUF with Ollama:
```
ollama run hf.co/axteam/GLM-4.7-Flash-GGUF:Q4_K_M
```
Unsloth Desktop

How to use axteam/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 axteam/GLM-4.7-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": "axteam/GLM-4.7-Flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use axteam/GLM-4.7-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/axteam/GLM-4.7-Flash-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull axteam/GLM-4.7-Flash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-4.7-Flash-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use axteam/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 axteam/GLM-4.7-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 axteam/GLM-4.7-Flash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use axteam/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 axteam/GLM-4.7-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 "axteam/GLM-4.7-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-4.7-Flash · GGUF (llama.cpp)

GGUF quantizations of zai-org/GLM-4.7-Flash for llama.cpp / Ollama / LM Studio. Produced by axteam, converted with the official llama.cpp convert_hf_to_gguf.py (the original chat_template is embedded).

Files

File	Size	Notes
`GLM-4.7-Flash-Q2_K.gguf`	11.5 GB	2-bit k-quant — smallest, noticeable quality loss (for tight memory).
`GLM-4.7-Flash-Q3_K_M.gguf`	15.0 GB	3-bit k-quant (medium) — small, some quality loss.
`GLM-4.7-Flash-Q4_K_M.gguf`	18.9 GB	4-bit k-quant (medium) — recommended size/quality balance.
`GLM-4.7-Flash-Q5_K_M.gguf`	22.2 GB	5-bit k-quant (medium) — high quality.
`GLM-4.7-Flash-Q6_K.gguf`	25.7 GB	6-bit k-quant — near-lossless, larger.
`GLM-4.7-Flash-Q8_0.gguf`	33.2 GB	8-bit — closest to the original.

Quantization method

These GGUF files are post-training quantization (PTQ) made with llama.cpp (convert_hf_to_gguf.py → llama-quantize): the BF16 weights are quantized block-wise (k-quants: per-block scales with mixed bit allocation), with no retraining. The original chat_template is embedded in each file.

Run with llama.cpp

# pull one quant straight from the Hub and serve an OpenAI-compatible API on :8080
llama-server -hf axteam/GLM-4.7-Flash-GGUF:Q4_K_M -c 8192 --jinja

# or download a single file and chat in the terminal
hf download axteam/GLM-4.7-Flash-GGUF GLM-4.7-Flash-Q4_K_M.gguf --local-dir .
llama-cli -m GLM-4.7-Flash-Q4_K_M.gguf -c 8192 --jinja -cnv

Query the server:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
  "messages": [{"role": "user", "content": "대한민국의 수도는 어디인가요?"}],
  "temperature": 1.0, "max_tokens": 512
}'

Reduce KV-cache memory (optional, run-time)

KV-cache type is chosen at run time in llama.cpp (it is not baked into the file):

llama-server -hf axteam/GLM-4.7-Flash-GGUF:Q4_K_M -c 8192 --jinja --cache-type-k q8_0 --cache-type-v q8_0 -fa on

Ollama

ollama run hf.co/axteam/GLM-4.7-Flash-GGUF:Q4_K_M

LM Studio

Search for axteam/GLM-4.7-Flash-GGUF in the model browser and pick a quant (e.g. Q4_K_M).

Recommended sampling (from the original generation_config.json): temperature=1.0.

License

Quantized derivative of zai-org/GLM-4.7-Flash (MIT). Original weights & tokenizer belong to Z.ai / zai-org.

한국어 요약

zai-org/GLM-4.7-Flash 를 llama.cpp 용 GGUF 여러 퀀트로 변환(axteam 제작, 공식 convert_hf_to_gguf.py). llama.cpp/Ollama/LM Studio 에서 사용, 원본 chat_template 내장. VRAM 초과분은 llama.cpp 가 CPU RAM 으로 자동 오프로드(-ngl). 라이선스 MIT(원본 Z.ai).

파일 — Q2_K(2비트·최소 용량), Q3_K_M(3비트), Q4_K_M(4비트·권장), Q5_K_M(5비트), Q6_K(6비트·거의 무손실), Q8_0(8비트·원본에 가장 가까움).
실행 — llama-server -hf axteam/GLM-4.7-Flash-GGUF:Q4_K_M -c 8192 --jinja (OpenAI 호환 서버 :8080) 또는 llama-cli -m GLM-4.7-Flash-Q4_K_M.gguf -cnv --jinja.
Ollama — ollama run hf.co/axteam/GLM-4.7-Flash-GGUF:Q4_K_M. LM Studio 에서는 axteam/GLM-4.7-Flash-GGUF 검색.
KV 캐시 절감(런타임) — --cache-type-k q8_0 --cache-type-v q8_0 -fa on.
권장 샘플링 temperature=1.0.

Downloads last month: -

GGUF

Model size

31B params

Architecture

deepseek2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for axteam/GLM-4.7-Flash-GGUF

Base model

zai-org/GLM-4.7-Flash

Quantized

(111)

this model