README.md

---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B-Instruct
pipeline_tag: text-generation
widget:
  - example_title: Hi, I am Menstrual Chatbot. How may I help you?
    messages:
      - role: user
        content: Hey! What are common symptoms of menstruation?
inference:
  parameters:
    max_new_tokens: 200
    stop:
      - <|end_of_text|>
      - <|eot_id|>
---

## Model Details
LCS2 developed and released the Mestrual-LLaMA, a generative text models in 8B size. It is optimized for dialogue use cases. Further, in developing these models, we took great care to optimize helpfulness and safety.

### Model Description
- **Developed by:** LCS2, IIT Delhi
- **Language(s) (NLP):** Multilingual
- **License:** LLaMA3
- **Finetuned from model:** Meta-Llama-3-8B-Instruct
- **Dataset:** https://huggingface.co/datasets/proadhikary/MENST
- **Cite:**

```
Adhikary P, Motiyani I, Oke G, Joshi M, Pathak K, Singh S, Chakraborty T
Menstrual Health Education Using a Specialized Large Language Model in India: Development and Evaluation Study of MenstLLaMA
J Med Internet Res 2025;27:e71977
URL: https://www.jmir.org/2025/1/e71977
DOI: 10.2196/71977
```

## Uses

### Intended Use
This model is fine-tuned on a menstrual health dataset to provide accurate and sensitive responses to queries related to menstrual health.

### Downstream Use
- **Primary Use:** Menstrual health Q&A.
- **Secondary Use:** Educational resources, support groups, and health awareness initiatives.

### Out-of-Scope Use
- **Not Recommended For:** Comprehensive sexual healthcare chatbot functionalities and prescribing medications.

## Bias, Risks, and Limitations
While this model strives for accuracy and sensitivity, it is essential to note the following:
- **Biases:** The model might reflect existing biases in the training data.
- **Limitations:** It may not always suggest accurate medications or treatments; professional verification is advised.

### Recommendations
Users, both direct and downstream, should be aware of the model's biases, risks, and limitations. It is recommended to use the model as a supplementary tool rather than a definitive source.

## How to Get Started with the Model

Use the following code snippet to get started with the model:

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

#Request us for the model, collect your token: https://huggingface.co/docs/hub/en/security-tokens & place it here.
#Incase delayed, please drop a mail at proadhikary@ee.iitd.ac.in!

access_token = "hf_..."

model_path = "proadhikary/Menstrual-LLaMA-8B"
tokenizer = AutoTokenizer.from_pretrained(model_path, token=access_token)
model = AutoModelForCausalLM.from_pretrained(model_path, token=access_token)

def model_output(Question):
    messages = [
        {"role": "system", "content": "Act as an advisor for menstrual health. Do not answer out of Domain(Menstrual Health) question. Generate only short and complete response!"},
        {"role": "user", "content": Question},
    ]

    input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True,return_tensors="pt").to(model.device)

    terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]

    torch.backends.cuda.enable_mem_efficient_sdp(False)
    torch.backends.cuda.enable_flash_sdp(False)

    outputs = model.generate(input_ids, pad_token_id=tokenizer.pad_token_id, max_new_tokens=200, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9,)

    response = outputs[0][input_ids.shape[-1]:]
    out = tokenizer.decode(response, skip_special_tokens=True)

# Example usage
input_text = "My mother said not to sleep on bed when I am menstruating, why?"
response = model_output(input_text)

print(response)
```



### Training Data
The dataset used for fine-tuning includes diverse and multilingual content focused on menstrual health. This dataset will be released soon.


#### Preprocessing
Special tokens were added following this: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/


#### Training Hyperparameters
- **Training regime:** [learning_rate=2e-4, weight_decay=0.001, fp16=False, bf16=False, max_grad_norm=0.3, max_steps=-1, warmup_ratio=0.03, group_by_length=True, lr_scheduler_type="constant"]


Hope this helps! <3