DEMO
Run model
from transformers import pipeline
from IPython.display import display, Markdown
from PIL import Image
import requests
from io import BytesIO
# Load model
pipe = pipeline(
"image-text-to-text",
model="rahul7star/LFM2.5-VL-3B-tool-sft-merged",
trust_remote_code=True,
)
# Conversation history
messages = []
def chat(user_text, image_url=None):
global messages
content = []
if image_url:
content.append({
"type": "image",
"url": image_url,
})
content.append({
"type": "text",
"text": user_text,
})
messages.append({
"role": "user",
"content": content,
})
output = pipe(text=messages)
# Pipeline output is normally a list containing the generated conversation
result = output[0]
if isinstance(result, dict) and "generated_text" in result:
generated = result["generated_text"]
else:
generated = result
# If the pipeline returns the complete conversation,
# extract the assistant's latest message.
if isinstance(generated, list):
assistant_message = generated[-1]
if isinstance(assistant_message, dict):
assistant_content = assistant_message.get("content", "")
else:
assistant_content = str(assistant_message)
else:
assistant_content = str(generated)
messages.append({
"role": "assistant",
"content": [
{
"type": "text",
"text": assistant_content,
}
],
})
print("\nAssistant:")
print(assistant_content)
return assistant_content
# Test with image
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"
chat(
"What animal is on the candy?",
image_url=image_url,
)
Uploaded finetuned model
- Developed by: rahul7star
- License: apache-2.0
- Finetuned from model : LiquidAI/LFM2.5-VL-3B
This lfm2_vl model was trained 2x faster with Unsloth and Huggingface's TRL library.