Update README.md

README.md

@@ -1,199 +1,344 @@
 ---
+language:
+- multilingual
+- en
+license: apache-2.0
 library_name: transformers
-tags: []
+tags:
+- nlp
+- code
+- vision
+- chemistry
+- engineering
+- biology
+- bio-inspired
+- text-generation-inference
+- materials science
+- mixture-of-experts
+- science
+- latex
+datasets:
+- lamm-mit/Cephalo-Bioinspired-Mechanics-Materials
+- lamm-mit/Cephalo-Wikipedia-Materials
+- OleehyO/latex-formulas
+- lamm-mit/OleehyO-latex-formulas
+pipeline_tag: image-text-to-text
+inference:
+  parameters:
+    temperature: 0.3
+widget:
+- messages:
+  - role: user
+    content: <|image_1|>Can you describe what you see in the image?
 ---
+## Model Summary
 
-# Model Card for Model ID
+Cephalo is a series of multimodal materials science focused vision large language models (V-LLMs) designed to integrate visual and linguistic data for advanced understanding and interaction in human-AI or multi-agent AI frameworks. 
 
-<!-- Provide a quick summary of what the model is/does. -->
+A novel aspect of Cephalo's development is the innovative dataset generation method. The extraction process employs advanced algorithms to accurately detect and separate images and their corresponding textual descriptions from complex PDF documents. It involves extracting images and captions from PDFs to create well-reasoned image-text pairs, utilizing large language models (LLMs) for natural language processing. These image-text pairs are then refined and validated through LLM-based NLP processing, ensuring high-quality and contextually relevant data for training. 
 
+Cephalo can interpret complex visual scenes and generating contextually accurate language descriptions and answer queries. 
 
+The model is developed to process diverse inputs, including images and text, facilitating a broad range of applications such as image captioning, visual question answering, and multimodal content generation. The architecture combines a vision encoder model and an autoregressive transformer to process complex natural language understanding. 
 
-## Model Details
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/kl5GWBP9WS0D4uwd1t3S7.png)
 
-### Model Description
+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.
 
-<!-- Provide a longer summary of what this model is. -->
+This version of Cephalo, lamm-mit/Cephalo-Idefics2-3x8b-beta, is a Mixture-of-Expert model based on the Idefics-2 model. The basic model architecture is as follows:
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/b7BK8ZtDzTMsyFDi0wP3w.png)
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+### Download Idefics-2 MoE Model and Sample inference code
 
-<!-- Provide the basic links for the model. -->
+```markdown
+pip install transformers -U
+```
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor, AutoConfig  
 
-## Uses
+def count_parameters(model):
+    total_params = sum(p.numel() for p in model.parameters())
+    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    #number of parameters in b
+    return total_params/1e9, trainable_params/1e9
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-### Direct Use
+model_name_moe = f"lamm-mit/Cephalo-Idefics2-3x8b-beta"
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+config = AutoConfig.from_pretrained(model_name_moe, trust_remote_code=True)
 
-[More Information Needed]
+processor = AutoProcessor.from_pretrained(model_name_moe, trust_remote_code=True) 
+moe_model = AutoModelForCausalLM.from_pretrained(
+    model_name_moe,config=config,
+    trust_remote_code=True,  torch_dtype=torch.bfloat16,   
+   # quantization_config=quantization_config,
+).to(device)
 
-### Downstream Use [optional]
+count_parameters(moe_model)
+```
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Now use downloaded model for inference:
 
-[More Information Needed]
+```python
+from transformers.image_utils import load_image
+DEVICE='cuda'
+image = load_image("https://d2r55xnwy6nx47.cloudfront.net/uploads/2018/02/Ants_Lede1300.jpg")
 
-### Out-of-Scope Use
+# Create inputs
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "What is shown in this image, and what is the relevance for materials design? Include a discussion of multi-agent AI."},
+        ]
+    },
+]
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+# Get inputs using the processor
+inputs = processor(text=prompt, images=[image], return_tensors="pt")
+inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
 
-[More Information Needed]
+# Generate
+generated_ids = moe_model.generate(**inputs, max_new_tokens=500)
+generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
 
-## Bias, Risks, and Limitations
+print(generated_texts)
+```
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+## Make a Idefics-2-MoE model from scratch using several pre-trained models
 
-### Recommendations
+Download .py files that implement the Phi-3-V and the Mixture-of-Expert Vision model 
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```markdown
+pip install huggingface_hub
+```
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+from huggingface_hub import HfApi, hf_hub_download
+from tqdm.notebook import tqdm
+import os
+import shutil
 
-## How to Get Started with the Model
+# Repository details
+repo_id = "lamm-mit/Cephalo-Idefics2-3x8b-beta"
+api = HfApi()
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+# List all files in the repository
+files_in_repo = api.list_repo_files(repo_id)
+
+# Filter for .py files
+py_files = [file for file in files_in_repo if file.endswith('.py')]
+
+# Directory to save the downloaded files
+save_dir = "./Idefics2_MoE/"
+os.makedirs(save_dir, exist_ok=True)
+
+# Download each .py file
+for file_name in tqdm(py_files):
+    file_path = hf_hub_download(repo_id=repo_id, filename=file_name)
+    new_path = os.path.join(save_dir, file_name)
+    shutil.move(file_path, new_path)
+    print(f"Downloaded: {file_name}")
+
+print("Download completed.")
+```
+
+Download models that will form the experts, as well as the base model:
+
+```python
+from transformers import AutoProcessor, Idefics2ForConditionalGeneration , AutoTokenizer
+from transformers import BitsAndBytesConfig
+
+DEVICE='cuda'
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model_id_1='lamm-mit/Cephalo-Idefics-2-vision-8b-beta'
+
+model_1 = Idefics2ForConditionalGeneration.from_pretrained(  model_id_1,
+                                                           torch_dtype=torch.bfloat16, #if your GPU allows
+                                                           _attn_implementation="flash_attention_2", #make sure Flash Attention 2 is installed
+                                                           trust_remote_code=True,
+                                                           #quantization_config=quantization_config,
+                                                        )#.to (DEVICE)
+processor = AutoProcessor.from_pretrained(
+    f"{model_id_1}",
+    do_image_splitting=True
+)
+
+config =  AutoConfig.from_pretrained(model_id_1, trust_remote_code=True)
+
+IDEFICS2_CHAT_TEMPLATE = "{% for message in messages %}{{message['role'].capitalize()}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
+processor.chat_template = IDEFICS2_CHAT_TEMPLATE
+```
+
+Now, load the rest of the models:
+```
+model_id_2='HuggingFaceM4/idefics2-8b-chatty'
+
+model_2 = Idefics2ForConditionalGeneration.from_pretrained(  model_id_2,
+                                                           torch_dtype=torch.bfloat16, #if your GPU allows
+                                                           _attn_implementation="flash_attention_2", #make sure Flash Attention 2 is installed
+                                                           trust_remote_code=True,
+                                                           #quantization_config=quantization_config,
+                                                        )#.to (DEVICE)
+
+model_id_3='HuggingFaceM4/idefics2-8b'
+
+model_3 = Idefics2ForConditionalGeneration.from_pretrained(  model_id_3,
+                                                           torch_dtype=torch.bfloat16, #if your GPU allows
+                                                           _attn_implementation="flash_attention_2", #make sure Flash Attention 2 is installed
+                                                           trust_remote_code=True,
+                                                           #quantization_config=quantization_config,
+                                                        )#.to (DEVICE)
+```
+Put on device:
+```
+model_1.to(DEVICE)
+model_2.to(DEVICE)
+model_3.to(DEVICE)
+```
+
+### Construct MoE 
+```
+dtype = torch.bfloat16  # Desired dtype for new layers
+base_model = copy.deepcopy(model_1)  # Your base model
+expert_models = [ model_1,  model_2,  model_3  ]  # List of expert models
+
+moe_config = Idefics2ForCausalLMMoEConfig(config=config, k=1, num_expert_models=len (expert_models))
+moe_model = Idefics2ForCausalLMMoE(moe_config, base_model, expert_models,  layer_dtype = dtype)#.to(device)
+
+count_parameters(expert_models[0]),count_parameters(moe_model)
+```
+Delete models no longer needed:
+```
+del model_1
+del model_2
+del model_3 
+```
+Put MoE model on device:
+```
+moe_model.to(DEVICE)
+```
+Test if it works (untrained):
+```
+from transformers.image_utils import load_image
+
+image = load_image("https://d2r55xnwy6nx47.cloudfront.net/uploads/2018/02/Ants_Lede1300.jpg")
+
+# Create inputs
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "What is shown in this image, and what is the relevance for materials design? Include a discussion of multi-agent AI."},
+        ]
+    },
+]
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+
+# Get inputs using the processor
+inputs = processor(text=prompt, images=[image], return_tensors="pt")
+inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+
+# Generate
+generated_ids = moe_model.generate(**inputs, max_new_tokens=500)
+generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+print(generated_texts)
+```
+
+### Now train MoE gating function
+```python
+image_1 = Image.open("./VALIDATION/Q15.jpg") 
+image_1a = Image.open("./VALIDATION/Q31.jpg") 
+
+image_2 = Image.open(requests.get("https://media.wired.com/photos/5aa32b912ba43111d1213e0c/master/w_2240,c_limit/akhacouple.jpg", stream=True).raw) 
+image_2a = Image.open(requests.get("https://media.wired.com/photos/5aa32b912ba43111d1213e0c/master/w_2240,c_limit/akhacouple.jpg", stream=True).raw) 
+
+image_3 = Image.open(requests.get("https://i5.walmartimages.com/seo/Amazing-Andrea-Apple-Tree-Seeds-20-Seeds-Grow-Fresh-Apples_ff218043-bcd4-4437-8418-6631d8e97bb3.638ac0120ff05c8913e85ebb74f45f6c.jpeg?odnHeight=640&odnWidth=640&odnBg=FFFFFF", stream=True).raw) 
+image_3a = Image.open(requests.get("https://i5.walmartimages.com/seo/Amazing-Andrea-Apple-Tree-Seeds-20-Seeds-Grow-Fresh-Apples_ff218043-bcd4-4437-8418-6631d8e97bb3.638ac0120ff05c8913e85ebb74f45f6c.jpeg?odnHeight=640&odnWidth=640&odnBg=FFFFFF", stream=True).raw) 
+
+prompts_per_expert = [
+    [{"text": "User:<image>What is shown in this image. Explain the importance for materials design.<end_of_utterance>Assistant: The image shows", "image": [image_1]}, 
+     {"text": "User:<image>What is shown in this image. Explain the importance for materials design.<end_of_utterance>Assistant: The image shows", "image": [image_1a]}, 
+     ],
+
+    [{"text": "User:<image>What is shown in this image. <end_of_utterance>Assistant: The image shows a human.", "image": [image_2]}, 
+     {"text": "User:<image>What is shown in this image, and what does it mean in terms of human history? <end_of_utterance>Assistant: The image shows a historical image of human development.", "image": [image_2a]}, 
+     ],
+    
+     [{"text": "User:<image>What is shown in this image. Provide a brief answer. <end_of_utterance>Assistant: This is an apple.", "image": [image_3]}, 
+     {"text": "User:<image>What is shown in this image. Brief and concise answer. <end_of_utterance>Assistant: The image shows an apple.", "image": [image_3a]}, 
+     ],
+]
+
+gating_layer_params = moe_model.train_gating_layer_params_from_hidden_states(processor, prompts_per_expert,
+                                              epochs=1000, loss_steps=100,  lr=5e-5, layer_offset=0)
+
+# Set parameters for a specific layer  
+moe_model.set_gating_layer_params(gating_layer_params)
+```
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/mh4eFDuFsTBOYbjc38PYz.png)
+
+Inference after MoE gating layers are trained:
+
+```
+from transformers.image_utils import load_image
+
+image = load_image("https://d2r55xnwy6nx47.cloudfront.net/uploads/2018/02/Ants_Lede1300.jpg")
+
+# Create inputs
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "What is shown in this image, and what is the relevance for materials design? Include a discussion of multi-agent AI."},
+        ]
+    },
+]
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+
+# Get inputs using the processor
+inputs = processor(text=prompt, images=[image], return_tensors="pt")
+inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+
+# Generate
+generated_ids = moe_model.generate(**inputs, max_new_tokens=500)
+generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+print(generated_texts)
+```
+
+### Push to hub and save locally
+
+```python
+repo_id='...'
+moe_name='Cephalo-Idefics2-3x8b-beta'
+
+processor.push_to_hub (f'{repo_id}/'+moe_name, )
+moe_model.push_to_hub (f'{repo_id}/'+merged_name, )
+```
+
+Save locally:
+```
+processor.save_pretrained(moe_name, )
+moe_model.save_pretrained(moe_name,  )
+
+```