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 — high quality, 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.

Requirements


llama.cpp	build ≥ b10251 — architecture `deepseek2 + MTP layer` needs ≥ b10251, and the commands on this card use `-fa on` (≥ b6325). Load-checked here with b12005 (commit e0f142d) and b10724 on CPU — the loader accepts the model, tokenizer and the `-fa on` / q8_0 KV-cache flags; no generation was run in that check.
Memory	the chosen GGUF file (sizes in the table) plus KV cache for the context (`-c`); `--cache-type-k/v q8_0 -fa on` halves the KV cache.
Input	text.
Ollama / LM Studio	not tested here.

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

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-Flash의 GGUF 양자화 빌드 (llama.cpp·Ollama·LM Studio용) · 제작 axteam

개요

항목	내용
원본 모델	`zai-org/GLM-4.7-Flash`
형식	GGUF (llama.cpp)
변환 도구	llama.cpp `convert_hf_to_gguf.py` → `llama-quantize`
파일 수	6개

양자화 상세

양자화 절차: PTQ(학습 후 양자화, 재학습 없음), llama.cpp 블록 단위 양자화(k-quant Q*_K, Q8_0)
chat template: GGUF 파일에 내장
품질: 미측정 — 아래 파일별 설명은 llama.cpp 양자화 단계의 일반적 경향

제공 파일

파일	크기	설명
`GLM-4.7-Flash-Q2_K.gguf`	11.5 GB	2비트 k-quant — 가장 작음, 품질 저하 큼 (메모리가 부족할 때)
`GLM-4.7-Flash-Q3_K_M.gguf`	15.0 GB	3비트 k-quant — 작음, 품질 다소 저하
`GLM-4.7-Flash-Q4_K_M.gguf`	18.9 GB	4비트 k-quant — 크기·품질 균형 (권장)
`GLM-4.7-Flash-Q5_K_M.gguf`	22.2 GB	5비트 k-quant — 고품질
`GLM-4.7-Flash-Q6_K.gguf`	25.7 GB	6비트 k-quant — 고품질, 용량 큼
`GLM-4.7-Flash-Q8_0.gguf`	33.2 GB	8비트 — 원본에 가장 가까움

요구사항

llama.cpp: b10251 이상 (deepseek2 구조 + MTP 층 지원 b10251부터, 예시 명령의 -fa on 옵션 b6325부터)
로딩 확인: b12005(커밋 e0f142d)와 b10724에서 CPU로 모델·토크나이저 로딩과 -fa on·q8_0 KV 캐시 옵션 인식만 확인 (생성은 실행 미확인)
메모리: 선택한 GGUF 파일 크기 + 컨텍스트(-c)만큼의 KV 캐시 (KV 캐시 줄이는 방법은 아래 사용법 참고)
입력: 텍스트
Ollama·LM Studio: 제작 측에서 실행 미확인

사용법

파일 받기: hf download axteam/GLM-4.7-Flash-GGUF GLM-4.7-Flash-Q4_K_M.gguf --local-dir . (아래 -m 명령에 필요, 다른 양자화는 파일명의 Q4_K_M만 변경)
서버 실행: llama-server -hf axteam/GLM-4.7-Flash-GGUF:Q4_K_M -c 8192 --jinja — OpenAI 호환 API
터미널 대화: llama-cli -m GLM-4.7-Flash-Q4_K_M.gguf -c 8192 --jinja
Ollama: ollama run hf.co/axteam/GLM-4.7-Flash-GGUF:Q4_K_M
LM Studio: 모델 검색에서 axteam/GLM-4.7-Flash-GGUF 입력 후 양자화 단계 선택
KV 캐시 절감(선택): --cache-type-k q8_0 --cache-type-v q8_0 -fa on — KV 캐시 약 절반, GGUF 파일과 상관없이 실행 시 지정
권장 샘플링: temperature=1.0 (원본 generation_config.json 기준)

라이선스

라이선스: MIT — 원본 모델의 라이선스를 그대로 따름
권리: 원본 가중치·토크나이저는 Z.ai 소유, 이 리포는 양자화 파생물

Downloads last month: 273

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