import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import requests
from io import BytesIO

url = "https://huggingface.co/qresearch/llama-3-vision-alpha-hf/resolve/main/assets/demo-2.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content))


model = AutoModelForCausalLM.from_pretrained(
    "qresearch/llama-3.1-8B-vision-378",
    trust_remote_code=True,
    torch_dtype=torch.float16,
).to("cuda")

tokenizer = AutoTokenizer.from_pretrained("qresearch/llama-3.1-8B-vision-378", use_fast=True,)

print(
    model.answer_question(
        image, "Briefly describe the image", tokenizer, max_new_tokens=128, do_sample=True, temperature=0.3
    ),
)

4-bit quantization

python

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import BitsAndBytesConfig
import requests
from io import BytesIO


url = "https://huggingface.co/qresearch/llama-3-vision-alpha-hf/resolve/main/assets/demo-2.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content))

bnb_cfg = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    llm_int8_skip_modules=["mm_projector", "vision_model"],
)

model = AutoModelForCausalLM.from_pretrained(
    "qresearch/llama-3.1-8B-vision-378",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    quantization_config=bnb_cfg,
)

tokenizer = AutoTokenizer.from_pretrained(
    "qresearch/llama-3.1-8B-vision-378",
    use_fast=True,
)

print(
    model.answer_question(
        image, "Briefly describe the image", tokenizer, max_new_tokens=128, do_sample=True, temperature=0.3
    ),
)

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