- Libraries
- Transformers
How to use audarai/Audar-ASR-V1-Flash with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="audarai/Audar-ASR-V1-Flash", trust_remote_code=True)
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("audarai/Audar-ASR-V1-Flash", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("audarai/Audar-ASR-V1-Flash", trust_remote_code=True, device_map="auto")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use audarai/Audar-ASR-V1-Flash 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 audarai/Audar-ASR-V1-Flash:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf audarai/Audar-ASR-V1-Flash:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf audarai/Audar-ASR-V1-Flash:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf audarai/Audar-ASR-V1-Flash: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 audarai/Audar-ASR-V1-Flash:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf audarai/Audar-ASR-V1-Flash: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 audarai/Audar-ASR-V1-Flash:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf audarai/Audar-ASR-V1-Flash:Q4_K_M
Use Docker
docker model run hf.co/audarai/Audar-ASR-V1-Flash:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use audarai/Audar-ASR-V1-Flash with Ollama:
ollama run hf.co/audarai/Audar-ASR-V1-Flash:Q4_K_M
- Unsloth Studio
How to use audarai/Audar-ASR-V1-Flash 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 audarai/Audar-ASR-V1-Flash 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 audarai/Audar-ASR-V1-Flash to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for audarai/Audar-ASR-V1-Flash to start chatting
- Docker Model Runner
How to use audarai/Audar-ASR-V1-Flash with Docker Model Runner:
docker model run hf.co/audarai/Audar-ASR-V1-Flash:Q4_K_M
- Lemonade
How to use audarai/Audar-ASR-V1-Flash with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull audarai/Audar-ASR-V1-Flash:Q4_K_M
Run and chat with the model
lemonade run user.Audar-ASR-V1-Flash-Q4_K_M
List all available models
lemonade list
- Atomic Chat
