Update README.md

README.md

@@ -47,51 +47,8 @@ We will release the training code in some time.
 Clone the following repo and following instructions for a CLI based inference.
 https://github.com/Tensoic-AI/Cerule
 
-```shell
-pip install torch transformers accelerate pillow
-```
 
-*The below code might break! Please use the CLI based inference in the meantime*
-```python
-import torch
-import transformers
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from PIL import Image
-import warnings
-
-transformers.logging.set_verbosity_error()
-transformers.logging.disable_progress_bar()
-warnings.filterwarnings('ignore')
-
-torch.set_default_device('cuda')  # or 'cpu'
-
-model = AutoModelForCausalLM.from_pretrained(
-    'Tensoic/Cerule',
-    torch_dtype=torch.float16,
-    device_map='auto',
-    trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained(
-    'Tensoic/Cerule',
-    trust_remote_code=True)
-
-# text prompt
-prompt = 'Who are these charecters?'
-text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
-text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
-input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
-
-image = Image.open('examples/mario.png')
-image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
-
-# generate
-output_ids = model.generate(
-    input_ids,
-    images=image_tensor,
-    max_new_tokens=100,
-    use_cache=False)[0] #keep use_cache=False or else it might run into some torch dim error
-
-print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=False).strip())
-```
+
 
 ## License
 Model subject to Gemma(base model license) terms of use along with the underlying datasets(LAOIN and SVIT) subject to their respective licenses. All codes are Apache 2.0