Update README.md

README.md

@@ -2,115 +2,12 @@
 language:
 - en
 license: mit
-library_name: Tevatron
-datasets:
-- Tevatron/docmatix-ir
-- HuggingFaceM4/Docmatix
-- Tevatron/msmarco-passage-aug
 ---
 
-# DSE-Phi3-Docmatix-V1
 
-DSE-Phi3-Docmatix-V1 is a bi-encoder model designed to encode document screenshots into dense vectors for document retrieval. The Document Screenshot Embedding ([DSE](https://arxiv.org/abs/2406.11251)) approach captures documents in their original visual format, preserving all information such as text, images, and layout, thus avoiding tedious parsing and potential information loss.
+# Clone of Phi-3-vision-128k-instruct
 
-The model, `Tevatron/dse-phi3-docmatix-v1`, is trained using 1/10 of the `Tevatron/docmatix-ir` dataset, a variant of `HuggingFaceM4/Docmatix` specifically adapted for training PDF retrievers with Vision Language Models in open-domain question answering scenarios. For more information on dataset filtering and hard negative mining, refer to the [docmatix-ir](https://huggingface.co/datasets/Tevatron/docmatix-ir/blob/main/README.md) dataset page.
+This is a clone of https://huggingface.co/microsoft/Phi-3-vision-128k-instruct 
 
-DSE has strong zero-shot effectiveness for document retrieval both with visual input and text input.
-For example, DSE-Phi3-Docmatix-V1 achieves 74.1 nDCG@5 on [ViDoRE](https://huggingface.co/spaces/vidore/vidore-leaderboard) leaderboard in **zero-shot setting** (without finetuning with ViDoRe training data).
-
-
-## How to Use the Model
-
-### Load the Model and Processor
-
-```python
-import torch
-from transformers import AutoProcessor, AutoModelForCausalLM
-
-processor = AutoProcessor.from_pretrained('Tevatron/dse-phi3-docmatix-v1', trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained('Tevatron/dse-phi3-docmatix-v1', trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, use_cache=False).to('cuda:0')
-
-def get_embedding(last_hidden_state: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
-    sequence_lengths = attention_mask.sum(dim=1) - 1
-    bs = last_hidden_state.shape[0]
-    reps = last_hidden_state[torch.arange(bs, device=last_hidden_state.device), sequence_lengths]
-    reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
-    return reps
-```
-
-### Encode Text Query
-
-```python
-queries = ["query: Where can we see Llama?</s>", "query: What is LLaMA model?</s>"]
-query_inputs = processor(queries, return_tensors="pt", padding="longest", max_length=128, truncation=True).to('cuda:0')
-with torch.no_grad():
-    output = model(**query_inputs, return_dict=True, output_hidden_states=True)
-query_embeddings = get_embedding(output.hidden_states[-1], query_inputs["attention_mask"])
-```
-
-### Encode Document Screenshot
-
-```python
-from PIL import Image
-import requests
-from io import BytesIO
-
-# URLs of the images
-url1 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v1/resolve/main/animal-llama.png"
-url2 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v1/resolve/main/meta-llama.png"
-
-# Download and open images
-response1 = requests.get(url1)
-response2 = requests.get(url2)
-
-passage_image1 = Image.open(BytesIO(response1.content))
-passage_image2 = Image.open(BytesIO(response2.content))
-
-passage_images = [passage_image1, passage_image2]
-passage_prompts = ["<|image_1|>\nWhat is shown in this image?</s>", "<|image_2|>\nWhat is shown in this image?</s>"]
-
-# Process inputs and get embeddings
-passage_inputs = processor(passage_prompts, images=passage_images, return_tensors="pt", padding="longest", max_length=4096, truncation=True).to('cuda:0')
-passage_inputs['input_ids'] = passage_inputs['input_ids'].squeeze(0)
-passage_inputs['attention_mask'] = passage_inputs['attention_mask'].squeeze(0)
-passage_inputs['image_sizes'] = passage_inputs['image_sizes'].squeeze(0)
-with torch.no_grad():
-    output = model(**passage_inputs, return_dict=True, output_hidden_states=True)
-doc_embeddings = get_embedding(output.hidden_states[-1], passage_inputs["attention_mask"])
-
-```
-
-### Compute Similarity
-
-```python
-from torch.nn.functional import cosine_similarity
-num_queries = query_embeddings.size(0)
-num_passages = doc_embeddings.size(0)
-
-for i in range(num_queries):
-    query_embedding = query_embeddings[i].unsqueeze(0)
-    similarities = cosine_similarity(query_embedding, doc_embeddings)
-    print(f"Similarities for Query {i+1}: {similarities.cpu().float().numpy()}")
-```
-
-### Encode Document Text
-This DSE checkpoint is warm-up with `Tevatron/msmarco-passage-aug`, thus the model can also effectively encode document as text input.
-```python
-passage_prompts = [
-  "The llama (/ˈlɑːmə/; Spanish pronunciation: [ˈʎama] or [ˈʝama]) (Lama glama) is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.</s>",
-  "Llama (acronym for Large Language Model Meta AI, and formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023.[2][3] The latest version is Llama 3.1, released in July 2024.[4]</s>"
-]
-
-passage_inputs = processor(passage_prompts, images=None, return_tensors="pt", padding="longest", max_length=4096, truncation=True).to('cuda:0')
-with torch.no_grad():
-    output = model(**passage_inputs, return_dict=True, output_hidden_states=True)
-doc_embeddings = get_embedding(output.hidden_states[-1], passage_inputs["attention_mask"])
-
-for i in range(num_queries):
-    query_embedding = query_embeddings[i].unsqueeze(0)
-    similarities = cosine_similarity(query_embedding, doc_embeddings)
-    print(f"Similarities for Query {i+1}: {similarities.cpu().float().numpy()}")
-```
-
-### Citation
-If you find this checkpoint is helpful, please consider cite Phi3, Docmatix and our DSE work.
+with the batch processing issue fixed based on:
+https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/discussions/32/files