Update README.md

README.md

@@ -1,29 +1,38 @@
 ---
+title: Kaggle Q&A Gemma Model
 tags:
 - autotrain
-- text-generation-inference
+- kaggle-qa
 - text-generation
 - peft
+datasets:
+- custom
 library_name: transformers
 widget:
  - messages:
  - role: user
- content: What is your favorite condiment?
+ content: How do I submit to a Kaggle competition?
 license: other
 ---
 
-# Model Trained Using AutoTrain
+## Overview
 
-This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
+Developed with the cutting-edge AutoTrain and PEFT technologies, this model is specifically trained to provide detailed answers to questions about Kaggle. Whether you're wondering how to get started, how to submit to a competition, or how to navigate the datasets, this model is equipped to assist.
 
-# Usage
+## Key Features
 
-```python
+- **Kaggle-Specific Knowledge**: Designed to offer insights and guidance on using Kaggle, from competition submissions to data exploration.
+- **Powered by AutoTrain**: Utilizes Hugging Face's AutoTrain for efficient and effective training, ensuring high-quality responses.
+- **PEFT Enhanced**: Benefits from PEFT for improved performance and efficiency, making it highly scalable and robust.
 
-from transformers import AutoModelForCausalLM, AutoTokenizer
+## Usage
 
-model_path = "PATH_TO_THIS_REPO"
+The following Python code snippet illustrates how to use this model to answer your Kaggle-related questions:
 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "theoracle/autotrain-kaggle"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForCausalLM.from_pretrained(
  model_path,
@@ -31,15 +40,39 @@ model = AutoModelForCausalLM.from_pretrained(
  torch_dtype='auto'
 ).eval()
 
-# Prompt content: "hi"
-messages = [
- {"role": "user", "content": "hi"}
-]
+tokenizer.pad_token = tokenizer.eos_token
+
+prompt = '''
+### How do I prepare for Kaggle competitions?\n ### Answer: 
+'''
+
+encoding = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=500, add_special_tokens=True)
+input_ids = encoding['input_ids']
+attention_mask = encoding['attention_mask']
 
-input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
-output_ids = model.generate(input_ids.to('cuda'))
-response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
+output_ids = model.generate(
+ input_ids.to('cuda'),
+ attention_mask=attention_mask.to('cuda'),
+ max_new_tokens=300,
+ pad_token_id=tokenizer.eos_token_id
+)
+
+response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
-# Model response: "Hello! How can I assist you today?"
 print(response)
-```
+```
+
+## Application Scenarios
+
+This model is particularly useful for:
+- Kaggle competitors seeking advice on strategy and submissions.
+- Educators and students looking for a tool to facilitate learning through Kaggle competitions.
+- Data scientists requiring quick access to information about Kaggle datasets and competitions.
+
+## About AutoTrain and PEFT
+
+AutoTrain by Hugging Face streamlines the model training process, making it easier and more efficient to develop state-of-the-art models. PEFT enhances this by providing a framework for efficient model training and deployment. Together, they enable this model to deliver fast and accurate responses to your Kaggle inquiries.
+
+## License
+
+This model is distributed under an "other" license, allowing diverse applications while encouraging users to review the license terms for compliance with their project requirements.