Instructions to use pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF",
	filename="GLM-5.2-REAP50-Q2_K-00001-of-00004.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 pipenetwork/GLM-5.2-REAP50-Q2_K-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 pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

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 pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

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 pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pipenetwork/GLM-5.2-REAP50-Q2_K-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": "pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

Ollama
How to use pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF with Ollama:
```
ollama run hf.co/pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K
```

Unsloth Studio

How to use pipenetwork/GLM-5.2-REAP50-Q2_K-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 pipenetwork/GLM-5.2-REAP50-Q2_K-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 pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF to start chatting

How to use pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

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": "pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pipenetwork/GLM-5.2-REAP50-Q2_K-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 pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

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 pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

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 "pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K" \
  --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"

Docker Model Runner
How to use pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K
```

Lemonade

How to use pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pipenetwork/GLM-5.2-REAP50-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.GLM-5.2-REAP50-Q2_K-GGUF-Q2_K

List all available models

lemonade list

GLM-5.2-REAP50-Q2_K-GGUF / llama.cpp-glm-dsa-indexer-optional.patch

pipenetwork

Add files using upload-large-folder tool

ea3c0f0 verified about 1 month ago

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2.15 kB

	diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp
	index 11d9131..32fe6de 100644
	--- a/src/models/glm-dsa.cpp
	+++ b/src/models/glm-dsa.cpp
	@@ -101,11 +101,11 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
	layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);

	// DSA indexer
	- layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags);
	- layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags);
	- layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags);
	- layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags);
	- layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
	+ layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags \| TENSOR_NOT_REQUIRED);
	+ layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags \| TENSOR_NOT_REQUIRED);
	+ layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags \| TENSOR_NOT_REQUIRED);
	+ layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags \| TENSOR_NOT_REQUIRED);
	+ layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags \| TENSOR_NOT_REQUIRED);
	if (i < (int) hparams.n_layer_dense_lead) {
	layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
	layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);