Instructions to use foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF",
	filename="Qwen3-ASR-1.7B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"sample1.flac\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf foryoung365/Qwen3-ASR-1.7B-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-server -hf foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf foryoung365/Qwen3-ASR-1.7B-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 foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf foryoung365/Qwen3-ASR-1.7B-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 foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use foryoung365/Qwen3-ASR-1.7B-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 foryoung365/Qwen3-ASR-1.7B-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 foryoung365/Qwen3-ASR-1.7B-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 foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-ASR-1.7B-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-ASR-1.7B-Q4_K_M-GGUF

GGUF export of Qwen/Qwen3-ASR-1.7B for llama.cpp.

Files included:

Qwen3-ASR-1.7B-Q4_K_M.gguf
mmproj-Qwen3-ASR-1.7B-Q4_K_M.gguf

Both files are required for audio transcription with llama.cpp multimodal support.

Tested command

llama-mtmd-cli.exe ^
  -m Qwen3-ASR-1.7B-Q4_K_M.gguf ^
  --mmproj mmproj-Qwen3-ASR-1.7B-Q4_K_M.gguf ^
  --audio sample.wav ^
  -p "Transcribe the audio." ^
  -t 8 -n 256 --temp 0

Notes

Main model was converted from the original Hugging Face checkpoint to GGUF, then quantized to Q4_K_M.
mmproj was exported from the original checkpoint as F16, then quantized to Q4_K_M.
This pair was locally tested with llama-mtmd-cli on Chinese audio.

Downloads last month: 478

GGUF

Model size

2B params

Architecture

qwen3vl

Hardware compatibility

4-bit

Model tree for foryoung365/Qwen3-ASR-1.7B-Q4_K_M-GGUF

Base model

Qwen/Qwen3-ASR-1.7B

Quantized

(36)

this model