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---
library_name: transformers
license: apache-2.0
base_model:
- rhymes-ai/Aria-sequential_mlp
- rhymes-ai/Aria
pipeline_tag: image-text-to-text
---
# Aria-sequential_mlp-bnb_nf4
BitsAndBytes NF4 quantization from [Aria-sequential_mlp](https://huggingface.co/rhymes-ai/Aria-sequential_mlp), requires about 15.5 GB of VRAM and runs on a RTX 3090 and (not really practical, only without `device_map=auto`) on a RTX 4060 Ti 16 GB.
Currently the model is not 5 GB sharded, as this seems to [cause problems](https://stackoverflow.com/questions/79068298/valueerror-supplied-state-dict-for-layers-does-not-contain-bitsandbytes-an) when loading serialized BNB models. This might make it impossible to load the model in free-tier Colab.
### Installation
```
pip install transformers==4.45.0 accelerate==0.34.1 sentencepiece==0.2.0 torchvision requests torch Pillow bitsandbytes
pip install flash-attn --no-build-isolation
```
### Inference
Run this model with:
``` python
import requests
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor, BitsAndBytesConfig
torch.cuda.set_device(0)
model_id_or_path = "thwin27/Aria-sequential_mlp-bnb_nf4"
model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)
image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"
image = Image.open(requests.get(image_path, stream=True).raw)
messages = [
{
"role": "user",
"content": [
{"text": None, "type": "image"},
{"text": "what is the image?", "type": "text"},
],
}
]
text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=text, images=image, return_tensors="pt")
inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.inference_mode(), torch.amp.autocast("cuda", dtype=torch.bfloat16):
output = model.generate(
**inputs,
max_new_tokens=500,
stop_strings=["<|im_end|>"],
tokenizer=processor.tokenizer,
do_sample=True,
temperature=0.9,
)
output_ids = output[0][inputs["input_ids"].shape[1]:]
result = processor.decode(output_ids, skip_special_tokens=True)
print(result)
print(f'Max allocated memory: {torch.cuda.max_memory_allocated(device="cuda") / 1024 ** 3:.3f}GiB')
```
### Quantization
Quantization created with:
``` python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_id = "rhymes-ai/Aria-sequential_mlp"
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
llm_int8_skip_modules=["language_model.lm_head", "multi_modal_projector", "vision_tower"],
)
model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config)
``` |