Update README.md

README.md

@@ -1,9 +1,81 @@
 ---
-tags:
-- autotrain
-- text-generation
 widget:
-- text: "I love AutoTrain because "
+- text: 'I love AutoTrain because '
+license: mit
+language:
+- en
+library_name: peft
+pipeline_tag: text-generation
 ---
+---
+
+
+### Base Model Description
+
+The Pythia 70M model is a transformer-based language model developed by EleutherAI. 
+It is part of the Pythia series, known for its high performance in natural language understanding and generation tasks.
+With 70 million parameters, it is designed to handle a wide range of NLP applications, offering a balance between computational efficiency and model capability. 
+
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** Pravin Maurya
+- **Model type:** LoRa fine-tuned transformer model
+- **Language(s) (NLP):** English
+- **License:** MIT 
+- **Finetuned from model:** EleutherAI/pythia-70m
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Colab Link:** [Click me🔗](https://colab.research.google.com/drive/1tyogv7jtc8a4h23pEIlJW2vBgWTTzy3e#scrollTo=b6fQzRl2faSn)
+
+## Uses
+
+Downstream uses are model can be fine-tuned further for specific applications like medical AI assistants, legal document generation, and other domain-specific NLP tasks.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("Pravincoder/Pythia-legal-finetuned-llm")
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-70m")
+
+def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=200):
+    input_ids = tokenizer.encode(text, return_tensors="pt", truncation=True, max_length=max_input_tokens)
+    device = model.device
+    generated_tokens_with_prompt = model.generate(input_ids=input_ids.to(device), max_length=max_output_tokens)
+    generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)
+    generated_text_answer = generated_text_with_prompt[0][len(text):]
+    return generated_text_answer
+
+system_message = "Welcome to the medical AI assistant."
+user_message = "What are the symptoms of influenza?"
+generated_response = inference(system_message, user_message, model, tokenizer)
+print("Generated Response:", generated_response)
+```
+
+## Training Data
+The model was fine-tuned using data relevant to the medical Chat data. for more info [click me🔗](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset)
+
+
+### Training Procedure 
+
+Data preprocessing involved tokenization and formatting suitable for the transformer model.
+
+#### Training Hyperparameters
+-Training regime: Mixed precision (fp16)
+
+## Hardware
+- **Hardware Type:** T4 Google Colab GPU
+- **Hours used:** 2-4 hr
+
+## Model Card Contact
+ Email :- PravinCoder@gmail.com
 
 # Model Trained Using AutoTrain