Instructions to use Renesas/Qwen3-ASR-1.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Renesas/Qwen3-ASR-1.7B-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 Renesas/Qwen3-ASR-1.7B-GGUF # Run inference directly in the terminal: llama cli -hf Renesas/Qwen3-ASR-1.7B-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Renesas/Qwen3-ASR-1.7B-GGUF # Run inference directly in the terminal: llama cli -hf Renesas/Qwen3-ASR-1.7B-GGUF
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 Renesas/Qwen3-ASR-1.7B-GGUF # Run inference directly in the terminal: ./llama-cli -hf Renesas/Qwen3-ASR-1.7B-GGUF
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 Renesas/Qwen3-ASR-1.7B-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf Renesas/Qwen3-ASR-1.7B-GGUF
Use Docker
docker model run hf.co/Renesas/Qwen3-ASR-1.7B-GGUF
- LM Studio
- Jan
- Ollama
How to use Renesas/Qwen3-ASR-1.7B-GGUF with Ollama:
ollama run hf.co/Renesas/Qwen3-ASR-1.7B-GGUF
- Unsloth Studio
How to use Renesas/Qwen3-ASR-1.7B-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 Renesas/Qwen3-ASR-1.7B-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 Renesas/Qwen3-ASR-1.7B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Renesas/Qwen3-ASR-1.7B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Renesas/Qwen3-ASR-1.7B-GGUF with Docker Model Runner:
docker model run hf.co/Renesas/Qwen3-ASR-1.7B-GGUF
- Lemonade
How to use Renesas/Qwen3-ASR-1.7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Renesas/Qwen3-ASR-1.7B-GGUF
Run and chat with the model
lemonade run user.Qwen3-ASR-1.7B-GGUF-{{QUANT_TAG}}List all available models
lemonade list
Qwen3-ASR-1.7B - Renesas X5H
Introduction
This repository contains Qwen3-ASR-1.7B model, which support language identification and speech recognition for 30 languages and 22 Chinese dialects, optimized for Renesas X5H platform for automatic speech recognition inference.
- Model Architecture:
Model Summary:
Audio Encoder:
Parameter Value NUM_LAYERS 24 HIDDEN_SIZE 1024 FFN_DIM 4096 NUM_HEADS 16 HEAD_DIM 64 MEL_BINS 128 OUTPUT_DIM 2048 Text Decoder:
Parameter Value NUM_LAYERS 28 HIDDEN_SIZE 2048 FFN_DIM 6144 NUM_HEADS 16 NUM_KV_HEADS 8 HEAD_DIM 128 GROUP_SIZE 2 VOCAB_SIZE 151936 RMS_NORM_EPS 1e-6 ROPE_THETA 1000000.0 Source Model: Qwen/Qwen3-ASR-1.7B
Performance
The following performance metrics were measured with a 15-second English audio sample.
| Model | Precision | Device | Offloading | Prefill Rate (tokens/sec) Batch size=1 | Decode Rate (tokens/sec) |
|---|---|---|---|---|---|
| Qwen3-ASR-1.7B | W4A16 | X5H - Single Cluster NPX | AudioEncode [NPX] LLM Decoder [NPX] | 20.84 | 20.71 |
| Qwen3-ASR-1.7B | FP16 | X5H - Single Cluster NPX | AudioEncode [NPX] LLM Decoder [NPX] | 12.28 | 12.23 |
Prerequisites
To run model, you need:
- Renesas X5H Board with SDK v4.34.0
- Hugging Face CLI: For downloading the model and installer.
Deployment
Qwen3-ASR-1.7B (W4A16)
- Download the installer qwen3-asr-w4a16-runner-0.1.0-Linux.sh from Files and version tab under w4a16/binaries/rcar-x5hv1/xOS-v4.34/ folder.
- Copy the installer to the X5H board and run the installer.
bash ./qwen3-asr-w4a16-runner-0.1.0-Linux.sh --prefix=./ --exclude-subdir --skip-license - Download GGUF files Qwen3-ASR-1.7B-FP16.gguf and mmproj-Qwen3-ASR-1.7b-FP16.gguf from Files and version tab under fp16 folder and copy to the installed directory on the X5H board.
- Download the W4A16 coefficients from w4a16/coefficients/ and copy to the installed directory.
- Expected directory structure on the X5H board.
qwen3-asr-w4a16-runner βββ Qwen3-ASR-1.7B-FP16.gguf βββ mmproj-Qwen3-ASR-1.7b-FP16.gguf βββ firmwares βββ kernel_modules βββ qwen3-asr-w4a16 βββ qwen3-asr-w4a16-runner βββ scripts βββ setup_npu.sh βββ test_audio βββ sample_en.wav
Inference - Qwen3-ASR-1.7B (W4A16)
bash ./setup_npu.sh
./qwen3-asr-w4a16-runner -m Qwen3-ASR-1.7B-FP16.gguf -v mmproj-Qwen3-ASR-1.7b-FP16.gguf -w test_audio/sample_en.wav -g ./ -s
Qwen3-ASR-1.7B (FP16)
- Download the installer qwen3-asr-runner-0.1.0-Linux.sh from Files and version tab under fp16/binaries/rcar-x5hv1/xOS-v4.34/ folder.
- Copy the installer to the X5H board and run the installer.
bash ./qwen3-asr-runner-0.1.0-Linux.sh --prefix=./ --exclude-subdir --skip-license - Download GGUF files Qwen3-ASR-1.7B-FP16.gguf and mmproj-Qwen3-ASR-1.7b-FP16.gguf from Files and version tab under fp16 folder and copy to the installed directory on the X5H board.
- Expected directory structure on the X5H board.
qwen3-asr-runner βββ Qwen3-ASR-1.7B-FP16.gguf βββ mmproj-Qwen3-ASR-1.7b-FP16.gguf βββ firmwares βββ kernel_modules βββ qwen3-asr βββ qwen3-asr-runner βββ scripts βββ setup_npu.sh βββ test_audio βββ sample_en.wav
Inference - Qwen3-ASR-1.7B (FP16)
bash ./setup_npu.sh
./qwen3-asr-runner -m Qwen3-ASR-1.7B-FP16.gguf -v mmproj-Qwen3-ASR-1.7b-FP16.gguf -w test_audio/sample_en.wav -g ./ -s
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Model tree for Renesas/Qwen3-ASR-1.7B-GGUF
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Qwen/Qwen3-ASR-1.7B