asif00
/

bangla-llama-4bit

@@ -11,12 +11,9 @@ base_model: unsloth/llama-3-8b-bnb-4bit
 library_name: transformers
 pipeline_tag: question-answering
 ---
-Model Description:
 Bangla LLaMA-4bit is a specialized model for context-based question answering and Bengali retrieval augment generation. It is derived from LLaMA 3 7B and trained on the iamshnoo/alpaca-cleaned-bengali dataset. This model is designed to provide accurate responses in Bengali with relevant contextual information. It is integrated with the transformers library, making it easy to use for context-based question answering and Bengali retrieval augment generation in projects.
-Model Details:
 - Model Family: Llama 3 7B
 - Language: Bengali
@@ -27,7 +24,8 @@ Model Details:
 - Batch Size: 80
 - Epoch: 1
-How to Use:
 You can use the model with a pipeline for a high-level helper or load the model directly. Here's how:
@@ -44,7 +42,7 @@ tokenizer = AutoTokenizer.from_pretrained("asif00/bangla-llama-4bit")
 model = AutoModelForCausalLM.from_pretrained("asif00/bangla-llama-4bit")
 ```
-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.
@@ -60,7 +58,7 @@ prompt = """Below is an instruction in Bengali language that describes a task, p
 """
 ```
-To get a cleaned up version of the response, you can use the `generate_response` function:
 ```python
 def generate_response(question, context):
@@ -72,7 +70,7 @@ def generate_response(question, context):
     return response
 ```
-Example Usage:
 ```python
 question = "ভারতীয় বাঙালি কথাসাহিত্যিক মহাশ্বেতা দেবীর মৃত্যু কবে হয় ?"
@@ -82,6 +80,11 @@ print(answer)
 ```
-Disclaimer:
 The Bangla LLaMA-4bit model has been trained on a limited dataset, and its responses may not always be perfect or accurate. The model's performance is dependent on the quality and quantity of the data it has been trained on. Given more resources, such as high-quality data and longer training time, the model's performance can be significantly improved.

 library_name: transformers
 pipeline_tag: question-answering
 ---
 Bangla LLaMA-4bit is a specialized model for context-based question answering and Bengali retrieval augment generation. It is derived from LLaMA 3 7B and trained on the iamshnoo/alpaca-cleaned-bengali dataset. This model is designed to provide accurate responses in Bengali with relevant contextual information. It is integrated with the transformers library, making it easy to use for context-based question answering and Bengali retrieval augment generation in projects.
+# Model Details:
 - Model Family: Llama 3 7B
 - Language: Bengali
 - Batch Size: 80
 - Epoch: 1
+# How to Use:
 You can use the model with a pipeline for a high-level helper or load the model directly. Here's how:
 model = AutoModelForCausalLM.from_pretrained("asif00/bangla-llama-4bit")
 ```
+# 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.
 """
 ```
+# To get a cleaned up version of the response, you can use the `generate_response` function:
 ```python
 def generate_response(question, context):
     return response
 ```
+# Example Usage:
 ```python
 question = "ভারতীয় বাঙালি কথাসাহিত্যিক মহাশ্বেতা দেবীর মৃত্যু কবে হয় ?"
 ```
+# Disclaimer:
 The Bangla LLaMA-4bit model has been trained on a limited dataset, and its responses may not always be perfect or accurate. The model's performance is dependent on the quality and quantity of the data it has been trained on. Given more resources, such as high-quality data and longer training time, the model's performance can be significantly improved.
+# Resources:
+Work in progress...