Update README.md

README.md

@@ -1,3 +1,67 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+### Generation
+
+The following is the sample code for inference.
+
+```python
+
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import process_images, tokenizer_image_token
+from llava.constants import DEFAULT_IMAGE_TOKEN
+
+from PIL import Image
+import torch
+import time
+import warnings
+import json 
+
+# export PYTHONPATH="/thestack/LLM4CodeBeta/LLaVA-NeXT-FLAME:$PYTHONPATH"
+
+warnings.filterwarnings("ignore")
+
+pretrained = "/root/nfs3/flame_ft/res/checkpoints/flame-google_siglip-so400m-patch14-384-deepseek-ai_deepseek-coder-6.7b-instruct-mlp2x_gelu-selectlayer-2-onevision-1-pretrain_mmcoder-3NODE-Date1212-STAGE2v9-2-data_1220_no_code_v1-inst_data-STAGE2v9-eos-16k-1220-FINETUNE-2-data_1220/no_code_v1-inst_data-v5_v6-eos-16k-1223"
+
+model_name = "flame"
+device = "cuda"
+device_map = "auto"
+llava_model_args = {
+    "multimodal": True,
+    "attn_implementation": None,
+}
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map,**llava_model_args)
+model.config.tokenizer_padding_side = 'left'  # Use left padding for batch processing
+# model.config.image_aspect_ratio = "resize"
+model.eval()
+
+url = "/root/nfs2/flame_ft/datasets/data_1220/TESTING_DATA/TEST80/imgs/000000034/000000034.png"
+image = Image.open(url) 
+image_tensor = process_images([image], image_processor, model.config)
+image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
+
+prompt = "Below is an image of the page to create. Generate React code and styles to replicate the design, including layout, typography, and styling. Format your response as follows:'// CSS\n[CSS/SCSS code]\n\n// [React Implementation (JS/TS/JSX/TSX)]\n[Component code]'.\n\n ### Input Image:\n{image}\n\n### Response:\n"
+
+input_ids = tokenizer_image_token(prompt, tokenizer, return_tensors='pt')
+input_ids = input_ids.unsqueeze(0)
+input_ids=input_ids.to(device)
+image_sizes = [image.size]
+modalities = ["image"] 
+
+cont = model.generate(
+    input_ids,
+    images=image_tensor,
+    image_sizes=image_sizes,
+    modalities=modalities,                   # Added this line with the modalities
+    do_sample=True,
+    num_beams=5, 
+    temperature=0.1,
+    max_new_tokens=4096,
+    top_p=0.95,
+    repetition_penalty=1.05
+)
+
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
+
+```