Added README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- BanglaLLM/bangla-alpaca
+language:
+- bn
+library_name: transformers
+pipeline_tag: question-answering
+---
+# How to Use:
+
+You can use the model with a pipeline for a high-level helper or load the model directly. Here's how:
+
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+pipe = pipeline("question-answering", model="hassanaliemon/bn_rag_llama3-8b")
+```
+
+```python
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("hassanaliemon/bn_rag_llama3-8b")
+model = AutoModelForCausalLM.from_pretrained("hassanaliemon/bn_rag_llama3-8b")
+```
+
+# General Prompt Structure: 
+
+```python
+prompt = """Below is an instruction in Bengali language that describes a task, paired with an input also in Bengali language that provides further context. Write a response in Bengali language that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}
+"""
+```
+
+# To get a cleaned up version of the response, you can use the `generate_response` function:
+
+```python
+def generate_response(question, context):
+    inputs = tokenizer([prompt.format(question, context, "")], return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_new_tokens=1024, use_cache=True)
+    responses = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    response_start = responses.find("### Response:") + len("### Response:")
+    response = responses[response_start:].strip()
+    return response
+```
+
+# Example Usage:
+
+```python
+question = "ভারতীয় বাঙালি কথাসাহিত্যিক মহাশ্বেতা দেবীর মৃত্যু কবে হয় ?"
+context = "২০১৬ সালের ২৩ জুলাই হৃদরোগে আক্রান্ত হয়ে মহাশ্বেতা দেবী কলকাতার বেল ভিউ ক্লিনিকে ভর্তি হন। সেই বছরই ২৮ জুলাই একাধিক অঙ্গ বিকল হয়ে তাঁর মৃত্যু ঘটে। তিনি মধুমেহ, সেপ্টিসেমিয়া ও মূত্র সংক্রমণ রোগেও ভুগছিলেন।"
+answer = generate_response(question, context)
+print(answer)
+```