metadata
license: other
license_name: deepcode-ai
license_link: LICENSE
3. Quick Start
Installation
On the basis of Python >= 3.8 environment, install the necessary dependencies by running the following command:
git clone https://github.com/deepcode-ai/DeepCode-VL
cd DeepCode-VL
pip install -e .
Simple Inference Example
import torch
from transformers import AutoModelForCausalLM
from deepcode_vl.models import VLChatProcessor, MultiModalityCausalLM
from deepcode_vl.utils.io import load_pil_images
# specify the path to the model
model_path = "deepcode-ai/deepcode-base"
vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
conversation = [
{
"role": "User",
"content": "<image_placeholder>Describe each stage of this image.",
"images": ["./images/training_pipelines.png"]
},
{
"role": "Assistant",
"content": ""
}
]
# load images and prepare for inputs
pil_images = load_pil_images(conversation)
prepare_inputs = vl_chat_processor(
conversations=conversation,
images=pil_images,
force_batchify=True
).to(vl_gpt.device)
# run image encoder to get the image embeddings
inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
# run the model to get the response
outputs = vl_gpt.language_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=prepare_inputs.attention_mask,
pad_token_id=tokenizer.eos_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=512,
do_sample=False,
use_cache=True
)
answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
print(f"{prepare_inputs['sft_format'][0]}", answer)
CLI Chat
python cli_chat.py --model_path "deepcode-ai/deepcode-base"
# or local path
python cli_chat.py --model_path "local model path"
``