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-Q4_K_XL Error when UD-Q3_K_XL Works.

by mtcl - opened Dec 23, 2025

Discussion

mtcl

Dec 23, 2025

(base) mukul@jarvis:~/dev-ai/llama.cpp$ CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES="0,1" ./build/bin/llama-server \
        --model /media/mukul/data/models/unsloth/GLM-4.7-GGUF/UD-Q4_K_XL/GLM-4.7-UD-Q4_K_XL-00001-of-00005.gguf \
        --alias unsloth/GLM-4.7 \
        --ctx-size 131072 \
        -fa on \
        -np 1 -kvu \
        --temp 0.6 \
        --top-p 0.95 \
        --top-k 40 \
        -b 4096 -ub 4096 \
        -ngl 99 \
        -ot ".ffn_(up)_exps.=CPU" \
        --threads 56 \
        --jinja \
        --host 0.0.0.0 \
        --port 10002
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 2 CUDA devices:
  Device 0: NVIDIA RTX PRO 6000 Blackwell Workstation Edition, compute capability 12.0, VMM: yes
  Device 1: NVIDIA RTX PRO 6000 Blackwell Workstation Edition, compute capability 12.0, VMM: yes
build: 7517 (a6a552e4e) with GNU 13.3.0 for Linux x86_64
system info: n_threads = 56, n_threads_batch = 56, total_threads = 112

system_info: n_threads = 56 (n_threads_batch = 56) / 112 | CUDA : ARCHS = 1200 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | AMX_INT8 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | 

init: using 111 threads for HTTP server
start: binding port with default address family
main: loading model
srv    load_model: loading model '/media/mukul/data/models/unsloth/GLM-4.7-GGUF/UD-Q4_K_XL/GLM-4.7-UD-Q4_K_XL-00001-of-00005.gguf'
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_model_load: error loading model: invalid model: tensor 'blk.90.ffn_up_exps.weight' is duplicated
llama_model_load_from_file_impl: failed to load model
llama_params_fit: failed to fit params to free device memory: failed to load model
llama_params_fit: fitting params to free memory took 0.24 seconds
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA RTX PRO 6000 Blackwell Workstation Edition) (0000:16:00.0) - 95580 MiB free
llama_model_load_from_file_impl: using device CUDA1 (NVIDIA RTX PRO 6000 Blackwell Workstation Edition) (0000:ac:00.0) - 96674 MiB free
llama_model_load: error loading model: invalid model: tensor 'blk.90.ffn_up_exps.weight' is duplicated
llama_model_load_from_file_impl: failed to load model
common_init_from_params: failed to load model '/media/mukul/data/models/unsloth/GLM-4.7-GGUF/UD-Q4_K_XL/GLM-4.7-UD-Q4_K_XL-00001-of-00005.gguf'
srv    load_model: failed to load model, '/media/mukul/data/models/unsloth/GLM-4.7-GGUF/UD-Q4_K_XL/GLM-4.7-UD-Q4_K_XL-00001-of-00005.gguf'
srv    operator(): operator(): cleaning up before exit...
main: exiting due to model loading error
(base) mukul@jarvis:~/dev-ai/llama.cpp$

shimmyshimmer

Unsloth AI org Dec 23, 2025

We uploaded a new one apologies so we overrode it which might be the reason why you got the error. Could you redownload and try again? Thanks!

mtcl

Dec 23, 2025

No worries :)Do i need to download all 5 files, or just the first one?

danielhanchen

Unsloth AI org Dec 23, 2025

If you are using snapshot_download like below:

# !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
)

it'll re-download the changed files - sorry again! The new ones are imatrix calibrated so you will definitely get better results.

qpqpqpqpqpqp

Dec 23, 2025

This comment has been hidden (marked as Abuse)

danielhanchen

Unsloth AI org Dec 23, 2025

@qpqpqpqpqpqp which issue are you referring to sorry?

mtcl

Dec 24, 2025

i redownloaded the files and it works now. For some reason when I downloaded the first time, i got error while loading. I deleted all 5 files and downloaded it again. All good now. You can close this one.

danielhanchen

Unsloth AI org Dec 24, 2025

@mtcl ok nice! It's ok we can leave the issue open

shimmyshimmer

Unsloth AI org Dec 24, 2025

•

edited Dec 28, 2025

I went through your issues in the last 10 weeks and you never made any issue on any Unsloth repo. Which issue are you referring to - maybe you were confused on posting on another repo or user? @qpqpqpqpqpqp

shimmyshimmer

Unsloth AI org Dec 28, 2025

Looks like qpqpqpqpqpqp is being dishonest as you still haven’t provided any evidence or response. That’s not okay, and it comes across as bad faith.

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