Instructions to use BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0")
model = AutoModelForMultimodalLM.from_pretrained("BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0

SGLang

How to use BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0 with Docker Model Runner:
```
docker model run hf.co/BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0
```

language:

fa datasets:
BaSalam/vision-catalogs-llava-format-v3 pipeline_tag: image-text-to-text

LLaVA Model Card

Model details

This model is "llava-hf/llava-1.5-7b-hf", fine-tuned on "Basalam product" data for extracting visual attributes of products. The outputs are in JSON format and can be parsed.

How to use the model

Below is an example script to run generation in float16 precision on a GPU device:

import requests
from PIL import Image
import torch
import json

from transformers import AutoProcessor, LlavaForConditionalGeneration
model_id = "BaSalam/Llava-1.5-7b-hf-bslm-product-attributes-v0"
model = LlavaForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True, 
).to(0)
processor = AutoProcessor.from_pretrained(model_id)

def prompt_formatter(entity):
    json_format = """attributes': {'attribute_name_1' : <list of attribute values>, 'attribute_name_2': <list of attribute values>, ...}"""
    final_prompt = f"""برای محصول داده شده، ویژگی‌های تصویری محصول را در قالب جیسون (json) استخراج کن. ساختار JSON باید به این شکل باشد: {json_format}. محصول از یک بازار اینترنتی ایرانی است پس خروجی Json باید به زبان فارسی باشد.
محصول: '{entity}'."""
    return final_prompt

prompt = prompt_formatter(entity='تیشرت مردانه')
conversation = [
    {
      "role": "user",
      "content": [
          {"type": "text", "text": prompt},
          {"type": "image"},
        ],
    },
]

prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "https://statics.basalam.com/public-16/users/6eOEg/01-24/qJ34XziHu7Orp3GToVWTms1nKvCv0X86Ux7tQLtuRoyTXTxyQ4.jpg_800X800X70.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)

inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)

output = model.generate(**inputs, max_new_tokens=384, do_sample=False)
generated_title = processor.decode(output[0], skip_special_tokens=True)[len(text.replace('<image>', ' ')):]
output = generated_title.replace('ASSISTANT: ', '')
json_output = json.loads(output)
print(json_output)

[
  {
    "attributes": {
      "نوع": [
        "تیشرت مردانه"
      ],
      "طرح چاپی": [
        "MVP"
      ],
      "رنگ": [
        "زرد",
        "آبی",
        "سفید",
        "مشکی",
        "کرم",
        "سبز"
      ],
      "سایز": [
        "L",
        "XL",
        "2XL",
        "3XL"
      ]
    }
  }
]

Model optimization

4-bit quantization through `bitsandbytes` library

First make sure to install bitsandbytes, pip install bitsandbytes and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:

model = LlavaForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True,
+   load_in_4bit=True
)

Use Flash-Attention 2 to further speed-up generation

First make sure to install flash-attn. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with:

model = LlavaForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True,
+   use_flash_attention_2=True
).to(0)

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Safetensors

Model size

7B params

Tensor type

F16

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