Instructions to use wmghf2023/asr-tts-llm-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use wmghf2023/asr-tts-llm-models 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 wmghf2023/asr-tts-llm-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wmghf2023/asr-tts-llm-models:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wmghf2023/asr-tts-llm-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wmghf2023/asr-tts-llm-models: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 wmghf2023/asr-tts-llm-models:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf wmghf2023/asr-tts-llm-models: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 wmghf2023/asr-tts-llm-models:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wmghf2023/asr-tts-llm-models:Q4_K_M

Use Docker

docker model run hf.co/wmghf2023/asr-tts-llm-models:Q4_K_M

LM Studio
Jan
Ollama
How to use wmghf2023/asr-tts-llm-models with Ollama:
```
ollama run hf.co/wmghf2023/asr-tts-llm-models:Q4_K_M
```
Unsloth Desktop

How to use wmghf2023/asr-tts-llm-models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wmghf2023/asr-tts-llm-models:Q4_K_M

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": "wmghf2023/asr-tts-llm-models:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use wmghf2023/asr-tts-llm-models with Docker Model Runner:
```
docker model run hf.co/wmghf2023/asr-tts-llm-models:Q4_K_M
```

Lemonade

How to use wmghf2023/asr-tts-llm-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wmghf2023/asr-tts-llm-models:Q4_K_M

Run and chat with the model

lemonade run user.asr-tts-llm-models-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use wmghf2023/asr-tts-llm-models with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wmghf2023/asr-tts-llm-models: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 wmghf2023/asr-tts-llm-models:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wmghf2023/asr-tts-llm-models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wmghf2023/asr-tts-llm-models: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 "wmghf2023/asr-tts-llm-models: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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

asr-tts-llm-models

Model mirror for AI快譯通（offline-interpreter），an offline, on-device speech-translation Android app. Every model the app downloads for a fresh install is re-hosted here, one folder per model, so the app doesn't depend on the original upstream hosts staying available.

Each folder includes its own README with the exact original source and license. All are unmodified byte-for-byte copies of the upstream files (same filenames, same content) — nothing here is re-quantized or re-converted.

Folder	Model	Used for
`moonshine-base-zh/`	Moonshine Base (Chinese), sherpa-onnx export	ASR
`moonshine-base-en/`	Moonshine Base (English), sherpa-onnx export	ASR
`moonshine-base-ja/`	Moonshine Base (Japanese), sherpa-onnx export	ASR
`qwen3.5-4b-mnn/`	Qwen3.5-4B, MNN INT4	Translation
`qwen3.5-2b-mnn/`	Qwen3.5-2B, MNN INT4	Translation
`translategemma-4b-gguf/`	TranslateGemma-4B, GGUF Q4_K_M	Translation
`cat-translate-1.4b-gguf/`	CAT-Translate-1.4B, GGUF Q4_K_M (imatrix)	Translation (ja↔en)

Downloads last month: 45

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

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

wmghf2023
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asr-tts-llm-models

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