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Running
on
Zero
File size: 3,730 Bytes
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"""
PetBull-7B-VL demo Space
------------------------
• Base model : Qwen/Qwen2.5-VL-7B-Instruct
• LoRA adapter: ColdSlim/PetBull-7B (light-weight repo you just pushed)
Put this file in your Space, add a `requirements.txt` with:
transformers>=4.41.0
peft>=0.11.0
accelerate
gradio>=4.33
Then (optionally) switch the Space hardware to **GPU (shared)** in
Settings → Hardware for much faster vision-language inference.
"""
import torch, gradio as gr
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
from peft import PeftModel
# ---------------------------------------------------------------------
# 1 Load base + LoRA (≈ 12 GB VRAM in bf16; falls back to CPU if needed)
# ---------------------------------------------------------------------
BASE_MODEL = "Qwen/Qwen2.5-VL-7B-Instruct"
ADAPTER_REPO = "ColdSlim/PetBull-7B" # 👉 replace with your HF path if different
ADAPTER_REV = "master"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32
processor = AutoProcessor.from_pretrained(BASE_MODEL, trust_remote_code=True)
base = AutoModelForVision2Seq.from_pretrained(
BASE_MODEL, torch_dtype=dtype, trust_remote_code=True)
model = PeftModel.from_pretrained(base, ADAPTER_REPO, revision=ADAPTER_REV)
model.to(device).eval()
# ---------------------------------------------------------------------
# 2 Inference helper
# ---------------------------------------------------------------------
def generate_answer(image: Image.Image | None,
question: str,
temperature: float = 0.7,
top_p: float = 0.95,
max_tokens: int = 512) -> str:
"""
Runs one‐shot VQA chat. Image is optional; if None we still obey the
prompt format required by Qwen-VL by inserting a blank white image.
"""
if image is None:
image = Image.new("RGB", (224, 224), color="white")
inputs = processor(text=[question],
images=[image],
return_tensors="pt").to(device)
with torch.no_grad():
output_ids = model.generate(**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p)
return processor.batch_decode(output_ids,
skip_special_tokens=True)[0]
# ---------------------------------------------------------------------
# 3 Gradio UI
# ---------------------------------------------------------------------
with gr.Blocks(title="PetBull-7B-VL – Ask a Vet Bot") as demo:
gr.Markdown(
"""
## 🐾 PetBull-7B-VL
Upload a photo of your pet **and/or** ask a question.
The model will analyse the image (if provided) and give tailored advice.
"""
)
with gr.Row():
with gr.Column(scale=1):
img_in = gr.Image(type="pil", label="Pet photo (optional)")
txt_in = gr.Textbox(lines=3, placeholder="Describe the issue or ask a question…")
run_btn = gr.Button("Ask PetBull")
temp_sl = gr.Slider(0.1, 1.5, 0.7, label="Temperature")
topp_sl = gr.Slider(0.1, 1.0, 0.95, label="Top-p")
max_sl = gr.Slider(32, 1024, 512, step=8, label="Max new tokens")
with gr.Column(scale=1):
answer = gr.Textbox(lines=12, label="Assistant", interactive=False)
run_btn.click(fn=generate_answer,
inputs=[img_in, txt_in, temp_sl, topp_sl, max_sl],
outputs=answer)
demo.queue().launch()
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