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README.md.md

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+  # Fine-tuned Mistral Model for Multi-Document Summarization  
+  This model a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on 
+   [multi_x_science_sum](https://huggingface.co/datasets/multi_x_science_sum) dataset.
+
+  ## Model description
+
+Mistral-7B-multixscience-finetuned is finetuned on multi_x_science_sum
+dataset in order to extend the capabilities of the original 
+Mistral model in multi-document summarization tasks. 
+The fine-tuned model leverages the power of Mistral model fundation, 
+adapting it to synthesize and summarize information from 
+multiple documents efficiently.
+
+## Training and evaluation dataset
+
+Multi_x_science_sum is a large-scale multi-document 
+summarization dataset created from scientific articles. 
+Multi-XScience introduces a challenging multi-document 
+summarization task: writing the related-work section of a 
+paper based on its abstract and the articles it references.
+
+* [paper](https://arxiv.org/pdf/2010.14235.pdf)
+* [Source](https://huggingface.co/datasets/multi_x_science_sum)
+
+The training and evaluation datasets were uniquely generated 
+to facilitate the fine-tuning of the model for
+multi-document summarization, particularly focusing on 
+generating related work sections for scientific papers. 
+Using a custom-designed prompt-generation process, the dataset
+is created to simulate the task of synthesizing related work 
+sections based on a given paper's abstract and the abstracts 
+of its referenced papers.
+
+### Dataset Generation process
+
+The process involves generating prompts that instruct the 
+model to use the abstract of the current paper along with 
+the abstracts of cited papers to generate a new related work 
+section. This approach aims to mimic the real-world scenario
+where a researcher synthesizes information from multiple 
+sources to draft the related work section of a paper.
+
+* **Prompt Structure:** Each data point consists of an instructional prompt that includes:
+  
+  * The abstract of the current paper.
+  * Abstracts from cited papers, labeled with unique identifiers.
+  * An expected model response in the form of a generated related work section.
+
+### Prompt generation Code
+
+```
+def generate_related_work_prompt(data):
+    prompt = "[INST] <<SYS>>\n"
+    prompt += "Use the abstract of the current paper and the abstracts of the cited papers to generate new related work.\n"
+    prompt += "<</SYS>>\n\n"
+    prompt += "Input:\nCurrent Paper's Abstract:\n- {}\n\n".format(data['abstract'])
+    prompt += "Cited Papers' Abstracts:\n"
+    for cite_id, cite_abstract in zip(data['ref_abstract']['cite_N'], data['ref_abstract']['abstract']):
+        prompt += "- {}: {}\n".format(cite_id, cite_abstract)
+    prompt += "\n[/INST]\n\nGenerated Related Work:\n{}\n".format(data['related_work'])
+    return {"text": prompt}
+```
+The dataset generated through this process was used to train 
+and evaluate the finetuned model, ensuring that it learns to 
+accurately synthesize information from multiple sources into 
+cohesive summaries.
+
+## Training hyperparameters
+
+The following hyperparameters were used during training:
+```
+learning_rate: 2e-5
+train_batch_size: 4
+eval_batch_size: 4
+seed: 42
+optimizer: adamw_8bit
+num_epochs: 5
+```
+## Usage
+
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftConfig, PeftModel
+
+base_model = "mistralai/Mistral-7B-v0.1"
+adapter = "OctaSpace/Mistral7B-fintuned"
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model,
+    add_bos_token=True,
+    trust_remote_code=True,
+    padding_side='left'
+)
+
+# Create peft model using base_model and finetuned adapter
+config = PeftConfig.from_pretrained(adapter)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,
+                                             load_in_4bit=True,
+                                             device_map='auto',
+                                             torch_dtype='auto')
+model = PeftModel.from_pretrained(model, adapter)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+model.eval()
+
+# Prompt content:
+messages = [] # Put here your related work generation instruction
+
+input_ids = tokenizer.apply_chat_template(conversation=messages,
+                                          tokenize=True,
+                                          add_generation_prompt=True,
+                                          return_tensors='pt').to(device)
+summary_ids = model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=True, pad_token_id=2)
+summaries = tokenizer.batch_decode(summary_ids.detach().cpu().numpy(), skip_special_tokens = True)
+
+# Model response: 
+print(summaries[0])
+
+```