Update README.md

README.md

@@ -12,6 +12,7 @@ tags:
 - bio-inspired
 - text-generation-inference
 - materials science
+- Llava-NEXT
 pipeline_tag: image-text-to-text
 inference:
   parameters:
@@ -19,7 +20,7 @@ inference:
 widget:
 - messages:
   - role: user
-    content: <|image_1|>Can you describe what you see in the image?
+    content: <s>[INST] <image>\nDescribe what you see in the image. [/INST]
 library_name: transformers
 ---
 ## Model Summary
@@ -36,22 +37,20 @@ The model is developed to process diverse inputs, including images and text, fac
 
 Cephalo provides a robust framework for multimodal interaction and understanding, including the development of complex generative pipelines to create 2D and 3D renderings of material microstructures as input for additive manufacturing methods.
 
-This version of Cephalo, lamm-mit/Cephalo-Phi-3-vision-128k-4b-alpha, is based on the Phi-3-Vision-128K-Instruct model. The model has a context length of 128,000 tokens. Further details, see: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct. 
+This version of Cephalo, lamm-mit/Cephalo-Llava-v1.6-Mistral-8b-alpha, is based on the Llava-v1.6-Mistral-8b-alpha model. Further details, see: https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf. 
 
 ### Chat Format
 
-Given the nature of the training data, the Cephalo-Phi-3-vision-128k-4b-alpha model is best suited for a single image input wih prompts using the chat format as follows. 
+Given the nature of the training data, the lamm-mit/Cephalo-Llava-v1.6-Mistral-8b-alpha model is best suited for the chat format as follows. 
 
-You can provide the prompt as a single image with a generic template as follow:
 ```markdown
-<|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n 
+<s>[INST] <image>\nQuestion 1 [/INST]Answer 2</s>
 ```
 
-The model generates the text after `<|assistant|>` . For multi-turn conversations, the prompt should be formatted as follows:
+The model generates the text after `[/INST]`. For multi-turn conversations, the prompt should be formatted as follows:
 
 ```markdown
-<|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n 
-```
+<s>[INST] <image>\nQuestion 1 [/INST]Answer 1</s>[INST] Question 2 [/INST]Answer 2</s>```
 
 ### Sample inference code
 
@@ -60,17 +59,21 @@ This code snippets show how to get quickly started on a GPU:
 ```python
 from PIL import Image 
 import requests 
-from transformers import AutoModelForCausalLM 
+from transformers import LlavaNextForConditionalGeneration 
 from transformers import AutoProcessor 
 
-model_id = "lamm-mit/Cephalo-Phi-3-vision-128k-4b-alpha" 
-
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto")
+model_id='lamm-mit/Cephalo-Llava-v1.6-Mistral-8b-alpha'
 
-processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) 
+model = LlavaNextForConditionalGeneration.from_pretrained(model_id,
+                                                          torch_dtype=torch.bfloat16,
+                                                          _attn_implementation="flash_attention_2",
+                                                         ).to (DEVICE )
+processor = AutoProcessor.from_pretrained(
+                                            model_id,
+                                         )
 
 messages = [ 
-    {"role": "user", "content": "<|image_1|>\nWhat is shown in this image, and what is the relevance for materials design?"}, 
+    {"role": "user", "content": "<image>\nWhat is shown in this image, and what is the relevance for materials design?"}, 
     ] 
 
 url = "https://d2r55xnwy6nx47.cloudfront.net/uploads/2018/02/Ants_Lede1300.jpg" 
@@ -85,8 +88,7 @@ generation_args = {
                     "max_new_tokens": 512, 
                     "temperature": 0.1, 
                     "do_sample": True, 
-                    "stop_strings": ['<|end|>',
-                                     '<|endoftext|>'],
+                    "stop_strings": ['</s>',],
                     "tokenizer": processor.tokenizer,
                   } 
 
@@ -104,9 +106,10 @@ Sample output:
 <small>Image by [Vaishakh Manohar](https://www.quantamagazine.org/the-simple-algorithm-that-ants-use-to-build-bridges-20180226/)</small>
 
 <pre style="white-space: pre-wrap;">
-The image shows a group of red imported fire ants (Solenopsis invicta) forming a bridge between two wooden posts. The relevance for materials design lies in the ants' ability to construct a bridge using their body parts, which demonstrates the potential for biomimetic design. Biomimetic design involves emulating natural processes and structures to create new materials and technologies. The ants' bridge construction could inspire the development of novel materials with enhanced structural properties, such as lightweight yet strong materials for construction and engineering applications.
-</pre>
+The image shows an ant colony in the process of building a bridge between two surfaces. The ants are working together to construct a pathway using their bodies as scaffolding. This demonstrates the concept of cooperative construction in nature, where individual ants contribute to the overall structure.
 
+The relevance for materials design lies in the efficiency and precision of the ant's construction. The ants are able to create a strong and stable bridge with minimal material usage, which can inspire the development of new construction techniques in materials science. The image highlights the importance of collaboration and the use of natural principles in engineering design.
+</pre>
 
 ## Dataset generation