Instructions to use rafw007/glm-4.7-flash-opencode-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rafw007/glm-4.7-flash-opencode-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rafw007/glm-4.7-flash-opencode-GGUF",
	filename="glm-4.7-flash-opencode-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rafw007/glm-4.7-flash-opencode-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_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 rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_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 rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M

Use Docker

docker model run hf.co/rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use rafw007/glm-4.7-flash-opencode-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rafw007/glm-4.7-flash-opencode-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rafw007/glm-4.7-flash-opencode-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M

Ollama
How to use rafw007/glm-4.7-flash-opencode-GGUF with Ollama:
```
ollama run hf.co/rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M
```

Unsloth Studio

How to use rafw007/glm-4.7-flash-opencode-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 rafw007/glm-4.7-flash-opencode-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 rafw007/glm-4.7-flash-opencode-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rafw007/glm-4.7-flash-opencode-GGUF to start chatting

Docker Model Runner
How to use rafw007/glm-4.7-flash-opencode-GGUF with Docker Model Runner:
```
docker model run hf.co/rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M
```

Lemonade

How to use rafw007/glm-4.7-flash-opencode-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rafw007/glm-4.7-flash-opencode-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.glm-4.7-flash-opencode-GGUF-Q4_K_M

List all available models

lemonade list

GLM-4.7-Flash Coder — local family for agentic harnesses

A family of custom models built on GLM-4.7-Flash (MoE, 30B total / 3B active), tuned to act as autonomous coding agents — each variant targeting a specific harness (opencode, codex, and Claude Code soon). The models speak native tool-calling, so they fully run agentic coding tools locally — your code never leaves your machine, and your cloud token cost drops to zero.

In thinking mode the base GLM-4.7-Flash is very slow on Apple Silicon (several minutes for a simple reply). This family separates reasoning from output: thinking goes into a dedicated thinking field, while content carries only the clean answer and tool calls. The result: the model responds immediately instead of monologuing, and the output is clean for the harness parser.

Models in the family

Model	Base	Context	Purpose
`glm-4.7-flash-opencode`	GLM-4.7-Flash (MoE 30B / 3B active)	64K	Published. Coding agent tuned for opencode (also works in codex). Clean content, no hallucinations, real tool-calling.
`glm-4.7-flash-claude-code`	GLM-4.7-Flash	64K	In progress. Variant for Claude Code (CC overrides thinking control — needs a dedicated renderer/template).

What it's for

Agentic work in opencode / codex with native tool-calling.
Writing and editing agent code — files, edits, full agent loops.
Sysadmin/DevOps tasks in the terminal (disk, network, scripts).
Full privacy and offline operation — no code is sent to the cloud.

Quick start

ollama run rafw007/glm-4.7-flash-opencode

In opencode:

ollama launch opencode rafw007/glm-4.7-flash-opencode

Behavior tuning

Thinking out of the content. /nothink together with our SYSTEM prompt moves reasoning into a separate field — no monologue leaks into content.
No hallucinations. The model reports only values actually present in the tool output — no invented hosts, hardware, or numbers.
Acts, doesn't ask. Inspect / scan / check / measure → it runs the command; its output is the answer.
macOS-aware. Uses df -h, du, nmap, system_profiler instead of Linux-only commands.

Sampling / context

temperature 1.0, num_ctx 65536 (64K).
GLM-4.7-Flash natively carries 128K context — you can raise it further on stronger hardware.

Test hardware

Mac Studio M2 (Apple Silicon), 32 GB class, macOS — performance reference.
Mac Mini M4, 32 GB RAM — works, though slower.
Ollama 0.24, GPU inference (Metal).

Measured behavior

Task	Verdict
`du` / `df`	Read the real disk output, no fabrication.
`nmap`	Handled permission limits and returned 22 real hosts.
Tetris	Full, working implementation — 396 lines (score, levels, next-piece preview, controls, game-over screen).

Performance (measured, same model, 64K context, 100% GPU, ~25 GB in memory):

Hardware	Generation	Prompt eval
Mac Studio M2 (32 GB)	~46 tok/s	~494 tok/s
Mac Mini M4 (32 GB)	~25 tok/s	~250 tok/s

The Studio is nearly 2× faster at identical quality — the difference comes from memory bandwidth, not the model.

Limitations

Claude Code incompatible — CC overrides thinking control and hangs the base build. A dedicated -claude-code variant is in the works.
In opencode the </tool_call> tag is sometimes printed as text (parser mismatch on the harness side).

How they were made

Designed, built, and tested with Claude Opus — the idea: the world's best coding model builds smaller models in its own image that take over the work right on your desk. The system prompts, parameter choices, and context configuration come directly from that work.

License

MIT (inherited from the base GLM-4.7-Flash).

Downloads last month: 165

GGUF

Model size

30B params

Architecture

glm4moelite

Hardware compatibility

4-bit