llama-3-typhoon-v1.5-8b-vision-preview

Typhoon-Vision Preview

llama-3-typhoon-v1.5-8b-vision-preview is a 🇹🇭 Thai vision-language model. It supports both text and image input modalities natively while the output is text. This version (August 2024) is our first vision-language model as a part of our multimodal effort, and it is a research preview version. The base language model is our llama-3-typhoon-v1.5-8b-instruct.

More details can be found in our release blog and technical report (coming soon). To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name.

Model Description

Here we provide Llama3 Typhoon Instruct Vision Preview which is built upon Llama-3-Typhoon-1.5-8B-instruct and SigLIP.

We base off our training recipe from Bunny by BAAI.

• Model type: A 8B instruct decoder-only model with vision encoder based on Llama architecture.
• Requirement: transformers 4.38.0 or newer.
• Primary Language(s): Thai 🇹🇭 and English 🇬🇧
• Demo: https://vision.opentyphoon.ai/
• License: Llama 3 Community License

Quickstart

Here we show a code snippet to show you how to use the model with transformers.

Before running the snippet, you need to install the following dependencies:

pip install torch transformers accelerate pillow

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

# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')

# Set Device
device = 'cuda'  # or cpu
torch.set_default_device(device)

# Create Model
model = AutoModelForCausalLM.from_pretrained(
    'scb10x/llama-3-typhoon-v1.5-8b-instruct-vision-preview',
    torch_dtype=torch.float16, # float32 for cpu
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    'scb10x/llama-3-typhoon-v1.5-8b-instruct-vision-preview',
    trust_remote_code=True)

def prepare_inputs(text, has_image=False, device='cuda'):
    messages = [
        {"role": "system", "content": "You are a helpful vision-capable assistant who eagerly converses with the user in their language."},
    ]
    
    if has_image:
        messages.append({"role": "user", "content": "<|image|>\n" + text})
    else:
        messages.append({"role": "user", "content": text})
    
    inputs_formatted = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=False
    )

    if has_image:
        text_chunks = [tokenizer(chunk).input_ids for chunk in inputs_formatted.split('<|image|>')]
        input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
        attention_mask = torch.ones_like(input_ids).to(device)
    else:
        input_ids = torch.tensor(tokenizer(inputs_formatted).input_ids, dtype=torch.long).unsqueeze(0).to(device)
        attention_mask = torch.ones_like(input_ids).to(device)

    return input_ids, attention_mask

# Example Inputs (try replacing with your own url)
prompt = 'บอกทุกอย่างที่เห็นในรูป'
img_url = "https://img.traveltriangle.com/blog/wp-content/uploads/2020/01/cover-for-Thailand-In-May_27th-Jan.jpg"
image = Image.open(io.BytesIO(requests.get(img_url).content))
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
input_ids, attention_mask = prepare_inputs(prompt, has_image=True, device=device)

# Generate
output_ids = model.generate(
    input_ids,
    images=image_tensor,
    max_new_tokens=1000,
    use_cache=True,
    temperature=0.2,
    top_p=0.2,
    repetition_penalty=1.0 # increase this to avoid chattering,
)[0]

print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())

Evaluation Results

Intended Uses & Limitations

This model is experimental and may not always follow human instructions accurately, making it prone to generating hallucinations. Additionally, the model lacks moderation mechanisms and may produce harmful or inappropriate responses. Developers should carefully assess potential risks based on their specific applications.

Follow Us & Support

• https://twitter.com/opentyphoon
• https://discord.gg/us5gAYmrxw

Acknowledgements

We would like to thank the Bunny team for open-sourcing their code and data, and thanks to the Google Team for releasing the fine-tuned SigLIP which we adopt for our vision encoder. Thanks to many other open-source projects for their useful knowledge sharing, data, code, and model weights.

Typhoon Team

Parinthapat Pengpun, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Warit Sirichotedumrong, Adisai Na-Thalang, Phatrasek Jirabovonvisut, Krisanapong Jirayoot, Pathomporn Chokchainant, Kasima Tharnpipitchai, Kunat Pipatanakul