Instructions to use zerodigest/Qwen3.8-27B-YMQ-MTP-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 zerodigest/Qwen3.8-27B-YMQ-MTP-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 zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

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 zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

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 zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

Use Docker

docker model run hf.co/zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zerodigest/Qwen3.8-27B-YMQ-MTP-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": "zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

Ollama
How to use zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF with Ollama:
```
ollama run hf.co/zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S
```
Unsloth Desktop

How to use zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

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": "zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S
```

Lemonade

How to use zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.Qwen3.8-27B-YMQ-MTP-GGUF-Q4_K_S

List all available models

lemonade list

Hermes Agent

How to use zerodigest/Qwen3.8-27B-YMQ-MTP-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 zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

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 zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S

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 "zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF:Q4_K_S" \
  --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"

minyor25 commited on about 14 hours ago

Commit

7f30210

1 Parent(s): 16d0fc1

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@@ -65,8 +65,8 @@ The following metrics demonstrate the mathematical quality preservation of the *
 | Model Variant | File Size | Perplexity | Mean KL-Divergence | Internal Bit Gradient (High ➔ Mid ➔ Low ➔ Default ➔ Floor) |
 | :--- | :--- | :--- | :--- | :--- |
 | **`XXS`** | [~9.8 GB](./Qwen3.8-27B-YMQ-XXS.gguf) | 7.7848 | `0.193804 ± 0.0022` | `IQ3_XXS` ➔ `IQ2_S` ➔ `IQ2_XS` ➔ `IQ2_XS` *(No Floor)* |
-| **`XS-TI`** | [~10.2 GB](./Qwen3.8-27B-YMQ-XS-TI.gguf) | --- | `0.168378 ±   0.001935` | `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ2_XXS` |
-| **`M-TI`** | [~12.9 GB](./Qwen3.8-27B-YMQ-M-TI.gguf) | **7.6090** | `0.104087 ± 0.0016` | `Q5_K` ➔ `IQ4_XS` ➔ `IQ3_S` ➔ `IQ3_XXS` ➔ `IQ2_XXS` |
 | **`M`** | [~14.5 GB](./Qwen3.8-27B-YMQ-M.gguf) | **6.8413** | `0.053286 ± 0.0013` | `Q5_K` ➔ `IQ4_XS` ➔ `IQ3_S` ➔ `IQ3_XXS` *(No Floor)* |
 | **`L`** | [~17.0 GB](./Qwen3.8-27B-YMQ-L.gguf) | 6.9791 | `0.031546 ± 0.0009` | `Q6_K` ➔ `Q5_K` ➔ `IQ4_NL` ➔ `IQ3_S` *(No Floor)* |
 | **`XL`** | [~19.0 GB](./Qwen3.8-27B-YMQ-XL.gguf) | 6.8196 | `0.011598 ± 0.0005` | `Q6_K` ➔ `Q6_K` ➔ `Q5_K` ➔ `IQ4_NL` *(No Floor)* |

 | Model Variant | File Size | Perplexity | Mean KL-Divergence | Internal Bit Gradient (High ➔ Mid ➔ Low ➔ Default ➔ Floor) |
 | :--- | :--- | :--- | :--- | :--- |
 | **`XXS`** | [~9.8 GB](./Qwen3.8-27B-YMQ-XXS.gguf) | 7.7848 | `0.193804 ± 0.0022` | `IQ3_XXS` ➔ `IQ2_S` ➔ `IQ2_XS` ➔ `IQ2_XS` *(No Floor)* |
+| **`XS-TI`** | [~10.2 GB](./Qwen3.8-27B-YMQ-XS-TI.gguf) | 7.6090 | `0.168378 ±   0.001935` | `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ2_XXS` |
+| **`M-TI`** | [~12.9 GB](./Qwen3.8-27B-YMQ-M-TI.gguf) | **7.0109** | `0.104087 ± 0.0016` | `Q5_K` ➔ `IQ4_XS` ➔ `IQ3_S` ➔ `IQ3_XXS` ➔ `IQ2_XXS` |
 | **`M`** | [~14.5 GB](./Qwen3.8-27B-YMQ-M.gguf) | **6.8413** | `0.053286 ± 0.0013` | `Q5_K` ➔ `IQ4_XS` ➔ `IQ3_S` ➔ `IQ3_XXS` *(No Floor)* |
 | **`L`** | [~17.0 GB](./Qwen3.8-27B-YMQ-L.gguf) | 6.9791 | `0.031546 ± 0.0009` | `Q6_K` ➔ `Q5_K` ➔ `IQ4_NL` ➔ `IQ3_S` *(No Floor)* |
 | **`XL`** | [~19.0 GB](./Qwen3.8-27B-YMQ-XL.gguf) | 6.8196 | `0.011598 ± 0.0005` | `Q6_K` ➔ `Q6_K` ➔ `Q5_K` ➔ `IQ4_NL` *(No Floor)* |