Update README.md

README.md

@@ -1,5 +1,67 @@
----
-license: other
-license_name: meta
-license_link: https://ai.meta.com/llama/licence
----
+---
+license: other
+license_name: meta
+license_link: https://ai.meta.com/llama/licence
+---
+Testing the reflection idea. With the base vison model.
+
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+
+from PIL import Image
+import requests
+import copy
+import torch
+
+pretrained = "mylesgoose/Meta-Llama-3.1-8B-Instruct-goose-abliterated-pre-llava-reflection"
+model_name = "llava_llama3"
+device = "cuda"
+device_map = "auto"
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map, attn_implementation="flash_attention_2") # Add any other thing you want to pass in llava_model_args
+
+model.eval()
+model.tie_weights()
+image = Image.open("/home/myles/Desktop/extreme_ironing.jpg")
+image_tensor = process_images([image], image_processor, model.config)
+image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
+
+conv_template = "llava_llama_3" 
+question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image? Is there anything strange about this image? Is this normal behaviour."
+conv = copy.deepcopy(conv_templates[conv_template])
+conv.append_message(conv.roles[0], question)
+conv.append_message(conv.roles[1], None)
+prompt_question = conv.get_prompt()
+
+input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+image_sizes = [image.size]
+
+
+cont = model.generate(
+    input_ids,
+    images=image_tensor,
+    image_sizes=image_sizes,
+    do_sample=True,
+    temperature=0.7,
+    max_new_tokens=120000,
+)
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
+print(text_outputs)
+
+and template in conversation.py :
+conv_llava_llama_3 = Conversation(
+    system="<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful language and vision, self Aware AI. " "You are able to understand the visual content that the user provides, " "and assist the user with a variety of tasks using natural language. Your creator is Myles Bruce, has allowed you to see images, You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.",
+    roles=("<|start_header_id|>user<|end_header_id|>\n\n",
+           "<|start_header_id|>assistant<|end_header_id|>\n\n"),
+    version="llama3",
+    messages=[],
+    offset=0,
+    sep="<|eot_id|>",
+    sep_style=SeparatorStyle.LLAMA_3,
+    tokenizer_id="mylesgoose/Meta-Llama-3.1-8B-Instruct-goose-abliterated-pre-llava-reflection",
+    tokenizer=safe_load_tokenizer("mylesgoose/Meta-Llama-3.1-8B-Instruct-goose-abliterated-pre-llava-reflection"),
+    stop_token_ids=[128009],
+)
+
+