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

Libraries

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

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

pipe = pipeline("text-generation", model="unsloth/GLM-4.7-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-GGUF", dtype="auto")

llama-cpp-python

How to use unsloth/GLM-4.7-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/GLM-4.7-GGUF",
	filename="BF16/GLM-4.7-BF16-00001-of-00015.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use unsloth/GLM-4.7-GGUF with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

SGLang

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

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

Unsloth Studio new

How to use unsloth/GLM-4.7-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-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-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-GGUF to start chatting

Pi new

How to use unsloth/GLM-4.7-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf unsloth/GLM-4.7-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-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/GLM-4.7-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf unsloth/GLM-4.7-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-GGUF:UD-Q4_K_XL

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

UD-Q5_K_XL seemingly broken

by Nimbz - opened Dec 23, 2025

Discussion

Nimbz

Dec 23, 2025

Hello unsloth team,

just to inform you, I just downloaded the UD-Q5_K_XL quant and sadly it seems to be broken.

maybe you can look into it?

best regards,
Nimbz

aaron-newsome

Dec 23, 2025

my first download of UD-Q5_K_XL was also broken. it ran and I was able to generate a one-shot html app, but in the agent (opencode), it would crash the server. i re-downloaded and the Q5 has been cooking for an hour straight. already knocked out a few issues in my application issues list. i've been running minimax m2 since it dropped, giving GLM 4.7 a spin now (4.6 was a dud for me). So far, I'm liking the concise and correct code 4.7 has spun out. Takes more than an hour and a couple issues to form an opinion, but so far I'm liking it.

aaron-newsome

Dec 24, 2025

spoke too soon. cooking on some tasks and then crash. i'll try stripping down some of the command line arguments, if it crashes again, then back to minimax-m2 i guess. it's been VERY stable.

slot update_slots: id  2 | task 23541 | prompt done, n_tokens = 46812, batch.n_tokens = 65
slot print_timing: id  2 | task 23541 | 
prompt eval time =    1323.43 ms /    65 tokens (   20.36 ms per token,    49.11 tokens per second)
       eval time =   31156.17 ms /   383 tokens (   81.35 ms per token,    12.29 tokens per second)
      total time =   32479.60 ms /   448 tokens
slot      release: id  2 | task 23541 | stop processing: n_tokens = 47194, truncated = 0
srv  update_slots: all slots are idle
srv  log_server_r: request: POST /v1/chat/completions 172.20.0.107 200
srv  params_from_: Chat format: GLM 4.5
slot get_availabl: id  2 | task -1 | selected slot by LCP similarity, sim_best = 0.995 (> 0.100 thold), f_keep = 0.992
slot launch_slot_: id  2 | task -1 | sampler chain: logits -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist 
slot launch_slot_: id  2 | task 23925 | processing task
slot update_slots: id  2 | task 23925 | new prompt, n_ctx_slot = 131072, n_keep = 0, task.n_tokens = 47051
slot update_slots: id  2 | task 23925 | n_tokens = 46811, memory_seq_rm [46811, end)
slot update_slots: id  2 | task 23925 | prompt processing progress, n_tokens = 47051, batch.n_tokens = 240, progress = 1.000000
slot update_slots: id  2 | task 23925 | prompt done, n_tokens = 47051, batch.n_tokens = 240
/llama.cpp/ggml/src/ggml-cuda/ggml-cuda.cu:94: CUDA error
CUDA error: unspecified launch failure
  current device: 2, in function ggml_cuda_mul_mat_id at /llama.cpp/ggml/src/ggml-cuda/ggml-cuda.cu:2327
  cudaMemcpyAsync(ids_host.data(), ids->data, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)
llama-server(+0x557ccf)[0x5878fd36accf]
llama-server(+0x6481d7)[0x5878fd45b1d7]
llama-server(+0x65c7fb)[0x5878fd46f7fb]
llama-server(+0x65d230)[0x5878fd470230]
llama-server(+0x661543)[0x5878fd474543]
llama-server(+0x663f6a)[0x5878fd476f6a]
llama-server(+0x5649b7)[0x5878fd3779b7]
llama-server(+0x3aeed3)[0x5878fd1c1ed3]
llama-server(+0x3a2d07)[0x5878fd1b5d07]
llama-server(+0x3b14e2)[0x5878fd1c44e2]
llama-server(+0x1c377f)[0x5878fcfd677f]
llama-server(+0x19a632)[0x5878fcfad632]
llama-server(+0xe07f0)[0x5878fcef37f0]
/usr/lib/x86_64-linux-gnu/libc.so.6(+0x2a1ca)[0x7d031f79a1ca]
/usr/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0x8b)[0x7d031f79a28b]
llama-server(+0x138585)[0x5878fcf4b585]
/mnt/data/models/GLM-4.7-UD-Q5_K_XL/start-llama: line 21:   139 Aborted                 (core dumped) llama-server --model /mnt/data/models/GLM-4.7-UD-Q5_K_XL/GLM-4.7-UD-Q5_K_XL-00001-of-00006.gguf --host 0.0.0.0 --alias glm-4.7 --n-gpu-layers -1 --ctx-size 131072 --cache-ram 4096 --threads 8 --tensor-split 32,34,34 --temp 1.0 --top-p 0.95 --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 --batch-size 4096 --ubatch-size 2048 --cont-batching --prio 3 --jinja

shimmyshimmer

Unsloth AI org Dec 24, 2025

Hello unsloth team,

just to inform you, I just downloaded the UD-Q5_K_XL quant and sadly it seems to be broken.

maybe you can look into it?

best regards,
Nimbz

Can you redownload? We overrode the old ones which may have explained the glitch

danielhanchen

Unsloth AI org Dec 24, 2025

@Nimbz Sorry definitely please re-download the model! For eg use:

# !pip install huggingface_hub hf_transfer
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0" # Can sometimes rate limit, so set to 0 to disable
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id = "unsloth/GLM-4.7-GGUF",
    local_dir = "unsloth/GLM-4.7-GGUF",
    allow_patterns = ["*UD-Q2_K_XL*"], # Dynamic 2bit Use "*UD-TQ1_0*" for Dynamic 1bit
)

For eg see:

@aaron-newsome If you have a stripped reproducible example, I can then forward this to the llama cpp team - apologies on the issue!

aaron-newsome

Dec 24, 2025

i've rebuilt the b7522, now running b7524. Cooking again so we'll see if it holds up. These are the same issues I saw with GLM-4.5-Air when flash attention on. With air, I was able to run with flash attention off because it fit with full context. I'm using flash attention with this Q5 because it won't fit without it. If it crashes the server again (a real pain because I have to reboot the entire system), then I'll move down to a smaller quant that can fit with fa off or just go back to MiniMax-M2, which has been rock solid.

jettoblack

Dec 24, 2025

The cuda_mul_mat crashes may be related to: https://github.com/ggml-org/llama.cpp/issues/18331

Try adding --defrag-thold 0 and see if that helps, but note its still under investigation.

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