Update README.md

README.md

@@ -1,29 +1,36 @@
 ---
 base_model: openbmb/MiniCPM-Llama3-V-2_5
 library_name: peft
+license: mit
+datasets:
+- magistermilitum/Tridis
+- CATMuS/medieval
+language:
+- la
+- fr
+- es
+- de
+pipeline_tag: image-text-to-text
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is a first model abble to switch adapters between two transcription styles for Wertern ancient manuscripts: 
 
+ABBreviated style: Keeping the original abbreviations from the manuscripts using MUFI characters
+NOT_ABBreviated style : Developping the abbreviations and symbols used in the manuscript to produce a normalized text
 
 
-## Model Details
-
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
 
 
-- **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]
+- **Developed by:** [Sergio Torres Aguilar]
+- **Model type:** [Multimodal]
+- **Language(s) (NLP):** [Latin, French, Spanish, German]
+- **License:** [MIT]
 
 ### Model Sources [optional]
 
@@ -35,168 +42,94 @@ library_name: peft
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model use two light PEFT adapter added to the MiniCPM-Llama3-V-2_5
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-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
+The following code is intended to produce both transcription styles based on a folder containing graphical manuscripts lines: 
+
+```python 
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+from PIL import Image
+import os
+from tqdm import tqdm
+import json
+
+# Configuration
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model_name = "openbmb/MiniCPM-Llama3-V-2_5"
+abbr_adapters = "magistermilitum/HTR_ABBR_minicpm"
+not_abbr_adapters = "magistermilitum/HTR_NOT_ABBR_minicpm"
+
+image_folder = "/your/images/folder/path"
+
+class TranscriptionModel:
+    """Handles model loading, adapter switching, and transcription generation."""
+    def __init__(self, model_name, abbr_adapters, not_abbr_adapters, device):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+        self.base_model = AutoModelForCausalLM.from_pretrained(
+            model_name, trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16, token=True
+        )
+        self.base_model = PeftModel.from_pretrained(self.base_model, abbr_adapters, adapter_name="ABBR")
+        self.base_model.load_adapter(not_abbr_adapters, adapter_name="NOT_ABBR")
+        self.base_model.set_adapter("ABBR")  # Set default adapter
+        self.base_model.to(device).eval()
+
+    def generate(self, adapter, image):
+        """Generate transcription for the given adapter and image."""
+        if hasattr(self.base_model, "past_key_values"):
+            self.base_model.past_key_values = None
+        self.base_model.set_adapter(adapter)
+        msgs = [{"role": "user", "content": [f"Transcribe this manuscript line in mode <{adapter}>:", image]}]
+        with torch.no_grad():
+            res = self.base_model.chat(image=image, msgs=msgs, tokenizer=self.tokenizer, max_new_tokens=128)
+        # Remove <ABBR> and <NOT_ABBR> tokens from the output
+        res = res.replace(f"<{adapter}>", "").replace(f"</{adapter}>", "")
+        return res
+
+
+class TranscriptionPipeline:
+    """Handles image processing, transcription, and result saving."""
+    def __init__(self, model, image_folder):
+        self.model = model
+        self.image_folder = image_folder
+
+    def run_inference(self):
+        """Process all images in the folder and generate transcriptions."""
+        results = []
+        for image_file in tqdm([f for f in os.listdir(self.image_folder)[:20] if f.endswith(('.png', '.jpg', '.jpeg'))]):
+            image = Image.open(os.path.join(self.image_folder, image_file)).convert("RGB")
+            print(f"\nProcessing image: {image_file}")
+            
+            # Generate transcriptions for both adapters
+            transcriptions = {
+                adapter: self.model.generate(adapter, image)
+                for adapter in ["ABBR", "NOT_ABBR"]
+            }
+            for adapter, res in transcriptions.items():
+                print(f"Mode ({adapter}): {res}")
+            results.append({"image": image_file, "transcriptions": transcriptions})
+
+            #image.show() #Optional
+
+        # Save results to a JSON file
+        with open("transcriptions_results.json", "w", encoding="utf-8") as f:
+            json.dump(results, f, ensure_ascii=False, indent=4)
+
+
+# Initialize and run the pipeline
+model = TranscriptionModel(model_name, abbr_adapters, not_abbr_adapters, device)
+TranscriptionPipeline(model, image_folder).run_inference()
+```
+
+
+
+## Citation
+
+Sergio Torres Aguilar. Dual-Style Transcription of Historical Manuscripts based on Multimodal Small Language Models with Switchable Adapters. 2025. https://hal.science/hal-04983305
 
-[More Information Needed]
-### Framework versions
 
 - PEFT 0.14.1.dev0