license: mit
language:
- en
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
Model Card for Model ID
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Model Details
Model Description
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Uses
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
You are encouraged to install transformers by using
python3 -m pip install transformers@git+https://github.com/huggingface/[email protected]
as this is the transformers version we are using when building this model.
from transformers import AutoProcessor, AutoModelForCausalLM
import torch
import librosa
def load_audio():
return librosa.load(librosa.ex("libri1"), sr=16000)[0]
processor = AutoProcessor.from_pretrained("lmms-lab/Aero-1-Audio-1.5B", trust_remote_code=True)
# We encourage to use flash attention 2 for better performance
# Please install it with `pip install --no-build-isolation flash-attn`
# If you do not want flash attn, please use sdpa or eager`
model = AutoModelForCausalLM.from_pretrained("lmms-lab/Aero-1-Audio-1.5B", device_map="cuda", torch_dtype="auto", attn_implementation="flash_attention_2", trust_remote_code=True)
model.eval()
messages = [
{
"role": "user",
"content": [
{
"type": "audio_url",
"audio": "placeholder",
},
{
"type": "text",
"text": "Please transcribe the audio",
}
]
}
]
audios = [load_audio()]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, audios=audios, sampling_rate=16000, return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}
outputs = model.generate(**inputs, eos_token_id=151645, max_new_tokens=4096)
cont = outputs[:, inputs["input_ids"].shape[-1] :]
print(processor.batch_decode(cont, skip_special_tokens=True)[0])
Training Details
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Training Procedure
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Evaluation
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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