|
--- |
|
tags: |
|
- autotrain |
|
- text-generation |
|
- health |
|
- medical |
|
widget: |
|
- text: 'I love AutoTrain because ' |
|
license: mit |
|
language: |
|
- en |
|
library_name: peft |
|
--- |
|
--- |
|
|
|
|
|
### 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-llm-v4 ") |
|
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:** 1.30-2 hr |
|
|
|
## Model Card Contact |
|
Email :- [email protected] |
|
|
|
# Model Trained Using AutoTrain |
