Instructions to use 0xSero/GLM-5.1-444B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/GLM-5.1-444B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="0xSero/GLM-5.1-444B-GGUF",
	filename="glm51-444b-reap-Q4_K_M-protected-00001-of-00019.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use 0xSero/GLM-5.1-444B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 0xSero/GLM-5.1-444B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/0xSero/GLM-5.1-444B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use 0xSero/GLM-5.1-444B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/GLM-5.1-444B-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": "0xSero/GLM-5.1-444B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Ollama
How to use 0xSero/GLM-5.1-444B-GGUF with Ollama:
```
ollama run hf.co/0xSero/GLM-5.1-444B-GGUF:Q4_K_M
```

Unsloth Studio

How to use 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 0xSero/GLM-5.1-444B-GGUF to start chatting

How to use 0xSero/GLM-5.1-444B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf 0xSero/GLM-5.1-444B-GGUF:Q4_K_M

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": "0xSero/GLM-5.1-444B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use 0xSero/GLM-5.1-444B-GGUF with Docker Model Runner:
```
docker model run hf.co/0xSero/GLM-5.1-444B-GGUF:Q4_K_M
```

Lemonade

How to use 0xSero/GLM-5.1-444B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-5.1-444B-GGUF-Q4_K_M

List all available models

lemonade list

0xSero commited on Apr 14

Commit

905524d

verified ·

1 Parent(s): 8717c16

Upload README.md with huggingface_hub

Browse files

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README.md +30 -24

README.md CHANGED Viewed

@@ -21,52 +21,52 @@ pipeline_tag: text-generation
 # CRITICAL WARNING: EXPERIMENTAL GGUF EXPORT
-This repository is an **experimental GGUF export** of a 40% REAP-pruned  checkpoint.
-It is **not fully benchmarked or validated**. Do not use it for production or make quality claims from it yet.
 ## What this repo is for
 This repo is intended to host GGUF artifacts derived from the 40% REAP checkpoint:
-- **BF16 GGUF** export
-- **Protected Q4_K_M GGUF** export for -style serving
 ## Source checkpoint
-- Base model: [](https://huggingface.co/zai-org/GLM-5.1)
-- Pruned checkpoint family: [](https://huggingface.co/0xSero/GLM-5.1-444B-A14B-REAP)
-- Architecture:
-- Routed experts per layer:
-- Active params/token:
 ## Quantization / protection strategy
-The protected Q4 export is **not** a blanket low-bit quantization. Sensitive tensors are kept at higher precision where possible.
 ### Kept higher precision
-- Router gate / router bias: **F32**
-- DSA indexer tensors: **Q8_0**
-- Attention tensors: **Q8_0**
-- Shared expert tensors: **Q8_0**
-- Dense-layer MLP tensors: **Q8_0**
 ### Quantized lower precision
-- Routed MoE expert projection tensors: **Q4_K / Q6_K family**
 ## Chat / reasoning notes
 - The original GLM-5.1 chat template is preserved and embedded in GGUF metadata.
 - This is a reasoning/chat model; serving stacks must handle GLM-style thinking correctly.
-- Early serving probes suggest that **unrestricted thinking can consume the entire generation budget before a final answer is emitted**. Size  accordingly, or disable thinking per request if you need direct outputs.
 ## Current status
 - GGUF conversion: complete
 - Protected Q4 export: complete
-- Full benchmark suite: **still in progress**
-- Public quality verdict: **not ready**
 ## Intended usage
@@ -79,15 +79,21 @@ Research / experimentation only:
 ## Example llama.cpp serving
-If you need direct outputs rather than -heavy traces, disable thinking at request time in the client payload.
 ## Related repos
-- BF16 pruned checkpoint: [](https://huggingface.co/0xSero/GLM-5.1-444B-A14B-REAP)
-- 25% sibling: [](https://huggingface.co/0xSero/GLM-5.1-555B-A14B-REAP)
-- 50% sibling: [](https://huggingface.co/0xSero/GLM-5.1-367B-A14B-REAP)
 ## Citation

 # CRITICAL WARNING: EXPERIMENTAL GGUF EXPORT
+This repository is an experimental GGUF export of a 40% REAP-pruned `zai-org/GLM-5.1` checkpoint.
+It is not fully benchmarked or validated. Do not use it for production or make quality claims from it yet.
 ## What this repo is for
 This repo is intended to host GGUF artifacts derived from the 40% REAP checkpoint:
+- BF16 GGUF export
+- Protected Q4_K_M GGUF export for `llama.cpp`-style serving
 ## Source checkpoint
+- Base model: [`zai-org/GLM-5.1`](https://huggingface.co/zai-org/GLM-5.1)
+- Pruned checkpoint family: [`0xSero/GLM-5.1-444B-A14B-REAP`](https://huggingface.co/0xSero/GLM-5.1-444B-A14B-REAP)
+- Architecture: `GlmMoeDsaForCausalLM`
+- Routed experts per layer: `256 -> 154`
+- Active params per token: `~14B`
 ## Quantization / protection strategy
+The protected Q4 export is not a blanket low-bit quantization. Sensitive tensors are kept at higher precision where possible.
 ### Kept higher precision
+- Router gate / router bias: F32
+- DSA indexer tensors: Q8_0
+- Attention tensors: Q8_0
+- Shared expert tensors: Q8_0
+- Dense-layer MLP tensors: Q8_0
 ### Quantized lower precision
+- Routed MoE expert projection tensors: Q4_K / Q6_K family
 ## Chat / reasoning notes
 - The original GLM-5.1 chat template is preserved and embedded in GGUF metadata.
 - This is a reasoning/chat model; serving stacks must handle GLM-style thinking correctly.
+- Early serving probes suggest that unrestricted thinking can consume the entire generation budget before a final answer is emitted. Size `max_tokens` accordingly, or disable thinking per request if you need direct outputs.
 ## Current status
 - GGUF conversion: complete
 - Protected Q4 export: complete
+- Full benchmark suite: still in progress
+- Public quality verdict: not ready
 ## Intended usage
 ## Example llama.cpp serving
+```bash
+llama-server \
+  -m glm51-444b-reap-Q4_K_M-protected-00001-of-00019.gguf \
+  --jinja \
+  --reasoning on \
+  --reasoning-format deepseek
+```
+If you need direct outputs rather than reasoning-heavy traces, disable thinking at request time in the client payload.
 ## Related repos
+- BF16 pruned checkpoint: [`0xSero/GLM-5.1-444B-A14B-REAP`](https://huggingface.co/0xSero/GLM-5.1-444B-A14B-REAP)
+- 25% sibling: [`0xSero/GLM-5.1-555B-A14B-REAP`](https://huggingface.co/0xSero/GLM-5.1-555B-A14B-REAP)
+- 50% sibling: [`0xSero/GLM-5.1-367B-A14B-REAP`](https://huggingface.co/0xSero/GLM-5.1-367B-A14B-REAP)
 ## Citation