Update README.md

README.md

@@ -12,21 +12,71 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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-# flan-t5-large-samsum-qlora
-
-This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+# Model description
+
+flan-t5-large-samsum-qlora is fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the [samsum](is a fine-tuned version of ) dataset. 
+Parameter-efficient fine-tuning with QLoRA was employed to fine-tune the base model.  
+
+The model achieves the following scores on the test dataset:
+- Rogue1: 49.249596%
+- Rouge2: 23.513032%
+- RougeL: 39.960812%
+- RougeLsum: 39.968438%
+
+
+
+## How to use
+
+Load the model:
+
+``` python
+from peft import PeftModel, PeftConfig
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BitsAndBytesConfig
+
+# Load the peft adapter model config
+peft_model_id = 'MuntasirHossain/flan-t5-large-samsum-qlora' 
+peft_config = PeftConfig.from_pretrained(peft_model_id)
+
+# load the base model and tokenizer
+base_model = AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path,  load_in_8bit=True,  device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
+
+# Load the peft model
+model = PeftModel.from_pretrained(base_model, peft_model_id, device_map="auto")
+model.eval()
+```
+
+Example Inference:
+
+``` python
+# random sample text from the samsum test dataset
+text = """
+Emma: Hi, we're going with Peter to Amiens tomorrow.
+Daniel: oh! Cool.
+Emma: Wanna join?
+Daniel: Sure, I'm fed up with Paris.
+Emma: We're too. The noise, traffic etc. Would be nice to see some countrysides.
+Daniel: I don't think Amiens is exactly countrysides though :P
+Emma: Nope. Hahahah. But not a megalopolis either!
+Daniel: Right! Let's do it!
+Emma: But we should leave early. The days are shorter now.
+Daniel: Yes, the stupid winter time.
+Emma: Exactly!
+Daniel: Where should we meet then?
+Emma: Come to my place by 9am.
+Daniel: oohhh. It means I have to get up before 7!
+Emma: Yup. The early bird gets the worm (in Amiens).
+Daniel: You sound like my grandmother.
+Emma: HAHAHA. I'll even add: no parties tonight, no drinking dear Daniel
+Daniel: I really hope Amiens is worth it!
+"""
+
+input = tokenizer(text, return_tensors="pt")
+outputs = model.generate(input_ids=input["input_ids"].cuda(), max_new_tokens=40) # outputs = model.generate(input_ids=input["input_ids"].to('cuda'), max_new_tokens=50)
+print("Summary: ", tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+Summary:  Emma and Peter are going to Amiens tomorrow. Daniel will join them. They will meet at Emma's place by 9 am. They will not have any parties tonight.
+```
 
 ## Training procedure