Instructions to use ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-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 ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

Use Docker

docker model run hf.co/ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

Llama new
LM Studio
Jan
Ollama
How to use ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF with Ollama:
```
ollama run hf.co/ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16
```
Unsloth Desktop

How to use ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF with Docker Model Runner:
```
docker model run hf.co/ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16
```

Lemonade

How to use ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

Run and chat with the model

lemonade run user.GLM-5.3-Flash-UNCENSORED-R2-40-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-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 ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

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 ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16

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 "ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF:BF16" \
  --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"

GLM-5.3-Flash-UNCENSORED — R2-40 IQ5_KS expert GGUF (for flash3090)

Routed-expert weights for running GLM-5.3-Flash (320B total / 18B active MoE) on one or two RTX 3090s with the flash3090 engine. flash3090 reads the routed experts from this file (CPU + VRAM hot cache) and the dense backbone from the FP8 checkpoint dealignai/GLM-5.3-Flash-UNCENSORED-FP8.

Base: zai-org/GLM-5.3-Flash (MIT), uncensored derivative by dealignai (MIT). This is an uncensored model: safety refusals were removed by the derivative's author. Use responsibly.
Quantization: ik_llama.cpp llama-quantize, routed experts IQ5_KS (last MoE layer IQ6_K), shared experts / dense FFN Q8_0, attention and indexer BF16; ~207 GB in 13 shards; importance matrix by AesSedai. Full recipe:

--custom-q 'blk\.45\.ffn_(gate|up|down)_exps\.weight=iq6_k,ffn_(gate|up|down)_exps\.weight=iq5_ks,ffn_(gate|up|down)_shexp\.weight=q8_0,ffn_(gate|up|down)\.weight=q8_0,indexer\.(attn_k|attn_q_b|proj)\.weight|indexer_compressor_(gate|ape)\.weight=bf16,hc_(attn|ffn)_fn\.weight=f32,attn_(k_b|v_b)\.weight=bf16,attn_(q_a|q_b)\.weight=q8_0,attn_kv_a_mqa\.weight=bf16,attn_(q|k|v|output)\.weight=bf16,ssm_(f_a|f_b|g_a|g_b|beta)\.weight=bf16,nextn\.eh_proj\.weight=bf16,token_embd\.weight=q8_0,output\.weight=bf16'

Quality: mean KL divergence vs BF16 of 0.026–0.036 on held-out text, on par with ik_llama.cpp on the same file.
Also included: mmproj-GLM-5.3-Flash-UNCENSORED-FP8-BF16.gguf, the vision projector (BF16, converted with llama.cpp convert_hf_to_gguf.py --mmproj), used by flash3090 for image input.

Use with flash3090

git clone https://github.com/ily-lab/flash3090 && cd flash3090   # build steps in its README
hf download ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF --local-dir models/experts
hf download dealignai/GLM-5.3-Flash-UNCENSORED-FP8 --local-dir models/GLM-5.3-Flash-FP8
FLASH3090_GPU1=1 tools/flash3090-serve.sh     # 2× RTX 3090; omit FLASH3090_GPU1 for one GPU

The expert file is also a valid ik_llama.cpp GGUF of the full model (it contains all tensors).

License

Downloads last month: 261

GGUF

Model size

321B params

Architecture

glm5-next

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ily-lab/GLM-5.3-Flash-UNCENSORED-R2-40-GGUF

Base model

zai-org/GLM-5.3-Flash

Quantized

dealignai/GLM-5.3-Flash-UNCENSORED-FP8

Quantized

(9)

this model