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README.md
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---
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license: apache-2.0
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datasets:
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- BanglaLLM/bangla-alpaca
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language:
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- bn
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library_name: transformers
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pipeline_tag: question-answering
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---
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# How to Use:
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You can use the model with a pipeline for a high-level helper or load the model directly. Here's how:
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```python
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# Use a pipeline as a high-level helper
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from transformers import pipeline
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pipe = pipeline("question-answering", model="hassanaliemon/bn_rag_llama3-8b")
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```
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```python
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("hassanaliemon/bn_rag_llama3-8b")
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model = AutoModelForCausalLM.from_pretrained("hassanaliemon/bn_rag_llama3-8b")
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```
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# General Prompt Structure:
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```python
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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.
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### Instruction:
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### Response:
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{}
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"""
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```
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# To get a cleaned up version of the response, you can use the `generate_response` function:
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```python
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def generate_response(question, context):
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inputs = tokenizer([prompt.format(question, context, "")], return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=1024, use_cache=True)
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response_start = responses.find("### Response:") + len("### Response:")
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response = responses[response_start:].strip()
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return response
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```
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# Example Usage:
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```python
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question = "ভারতীয় বাঙালি কথাসাহিত্যিক মহাশ্বেতা দেবীর মৃত্যু কবে হয় ?"
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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।"
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answer = generate_response(question, context)
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---
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license: apache-2.0
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datasets:
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- BanglaLLM/bangla-alpaca
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language:
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- bn
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library_name: transformers
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pipeline_tag: question-answering
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---
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# How to Use:
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You can use the model with a pipeline for a high-level helper or load the model directly. Here's how:
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```python
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("hassanaliemon/bn_rag_llama3-8b")
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model = AutoModelForCausalLM.from_pretrained("hassanaliemon/bn_rag_llama3-8b")
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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.
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### Instruction:
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### Response:
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{}
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"""
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def generate_response(question, context):
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inputs = tokenizer([prompt.format(question, context, "")], return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=1024, use_cache=True)
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response_start = responses.find("### Response:") + len("### Response:")
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response = responses[response_start:].strip()
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return response
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# Example Usage:
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question = "ভারতীয় বাঙালি কথাসাহিত্যিক মহাশ্বেতা দেবীর মৃত্যু কবে হয় ?"
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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।"
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answer = generate_response(question, context)
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