Instructions to use geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M

Use Docker

docker model run hf.co/geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf with Ollama:
```
ollama run hf.co/geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M
```

Unsloth Studio

How to use geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf to start chatting

How to use geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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": "geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M

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 "geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M" \
  --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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf with Docker Model Runner:
```
docker model run hf.co/geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M
```

Lemonade

How to use geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M

Run and chat with the model

lemonade run user.MOSS-Transcribe-Diarize-Q4_K_M.gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.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 geantendormi/MOSS-Transcribe-Diarize-Q4_K_M.gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

MOSS-Transcribe-Diarize-GGUF (Q4_K_M Hybrid)

GGUF conversions of OpenMOSS/MOSS-Transcribe-Diarize.

Joint ASR + Speaker Diarization + Timestamps in a single 0.9B Speech-LLM model. Produces timestamped, speaker-labelled transcripts in one pass.

📦 Model Files & Quantization Architecture

File	Size	Description
`MOSS-Transcribe-Diarize-Q4_K_M.gguf`	1.02 GB	SOTA Hybrid Quantization: Whisper 24L Encoder (F16) + VQAdaptor (F16) + Qwen3 28L LM Decoder (Q4_K_M)

🛠️ Hybrid Quantization Specifications

Acoustic Encoder (Whisper-Medium 24L): Preserved at F16 (16-bit) precision to maintain zero audio signal SNR degradation.
VQAdaptor Feature Projection: Preserved at F16 (16-bit) precision.
LM Decoder (Qwen3-0.6B 28L): Quantized to Q4_K_M (4-bit) super-block representation for fast Token generation.
Header Metadata: MODEL_ARCH.QWEN3, key_length = 128, QK-Norm (attn_q_norm & attn_k_norm) fully enabled.

🚀 Quickstart with llama.cpp

Run Command

llama-cli -m MOSS-Transcribe-Diarize-Q4_K_M.gguf -p "请将音频转写为文本：" -n 32 -t 8

Output Format Example

[00:00:00.250 --> 00:00:01.620] [S01] Can we get a table for two?
[00:00:01.710 --> 00:00:07.010] [S02] Sure. One table is just about to open up, just a few minutes.
[00:00:07.140 --> 00:00:07.640] [S01] Thank you.

📄 License

Apache-2.0 (same as the base model).

Downloads last month: 125

GGUF

Model size

0.9B params

Architecture

qwen3

Hardware compatibility

4-bit