Update README.md

README.md

@@ -1,3 +1,87 @@
 ---
 license: mit
+datasets:
+- ShashiVish/cover-letter-dataset
+language:
+- en
+widget:
+- example_title: Python!
+  text: >-
+    <start_of_turn>user Generate Cover Letter for Role: ML Engineer, \ Preferred Qualifications: strong AI realted skills, \ Hiring Company: Google, User Name: Vicky, \ Past Working Experience: Intenship in CodeClause, Current Working Experience: Fresher, \ Skillsets: Machine Learning, Deep Learning, AI, SQL, NLP, Qualifications: Bachelor of commerce with computer application <end_of_turn>\n<start_of_turn>model
+tags:
+- code
+inference:
+  parameters:
+    max_new_tokens: 250
+    do_sample: false
+pipeline_tag: text2text-generation
 ---
+# Gemma-2B Fine-Tuned Python Model
+
+## Overview
+Gemma-2B Fine-Tuned Python Model is a deep learning model based on the Gemma-2B architecture, fine-tuned specifically for Python programming tasks. This model is designed to understand Python code and assist developers by providing suggestions, completing code snippets, or offering corrections to improve code quality and efficiency.
+
+## Model Details
+- **Model Name**: Gemma-2B Fine-Tuned Python Model
+- **Model Type**: Deep Learning Model
+- **Base Model**: Gemma-2B
+- **Language**: Python
+- **Task**: Python Code Understanding and Assistance
+
+## Example Use Cases
+- Code completion: Automatically completing code snippets based on partial inputs.
+- Syntax correction: Identifying and suggesting corrections for syntax errors in Python code.
+- Code quality improvement: Providing suggestions to enhance code readability, efficiency, and maintainability.
+- Debugging assistance: Offering insights and suggestions to debug Python code by identifying potential errors or inefficiencies.
+
+## How to Use
+1. **Install Gemma Python Package**:
+   ```bash
+    pip install -q -U transformers==4.38.0
+    pip install torch
+   ```
+
+## Inference
+1. **How to use the model in our notebook**:
+```python
+# Load model directly
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter")
+model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter")
+
+job_title = "ML Engineer"
+preferred_qualification = "strong AI realted skills"
+hiring_company_name = "Google"
+user_name = "Vicky"
+past_working_experience= "N/A"
+current_working_experience = "Fresher"
+skilleset= "Machine Learning, Deep Learning, AI, SQL, NLP"
+qualification = "Bachelor of commerce with computer application"
+
+
+prompt_template = f"<start_of_turn>user Generate Cover Letter for Role: {job_title}, \
+ Preferred Qualifications: {preferred_qualification}, \
+ Hiring Company: {hiring_company_name}, User Name: {user_name}, \
+ Past Working Experience: {past_working_experience}, Current Working Experience: {current_working_experience}, \
+ Skillsets: {skilleset}, Qualifications: {qualification} <end_of_turn>\n<start_of_turn>model"
+
+prompt = prompt_template
+encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True).input_ids
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+merged_model.to(device)
+inputs = encodeds.to(device)
+
+
+# Increase max_new_tokens if needed
+generated_ids = merged_model.generate(inputs, max_new_tokens=250, do_sample=False, pad_token_id=tokenizer.eos_token_id)
+ans = ''
+for i in tokenizer.decode(generated_ids[0], skip_special_tokens=True).split('<end_of_turn>')[:2]:
+    ans += i
+
+# Extract only the model's answer
+model_answer = ans.split("model")[1].strip()
+print(model_answer)
+```