README.md

---
datasets:
- namelessai/helply
base_model: trillionlabs/Trillion-7B-preview
library_name: transformers
tags:
- pysch
- medical
- chat
- instruction
license: mit
language:
- en
- ko
---

# Model Card for TrillionHelp

**TrillionHelp** uses `trillionlabs/Trillion-7B-preview` as the backbone.

## Model Details

This model is fine-tuned on the `namelessai/helply` dataset designed to enhance mental health reasoning capabilities.

### Model Description

This model was fine-tuned for assisting pyschologists in assiting patients.

- **Developed by:** Alex Scott
- **Model type:** Language Model, Adapter Model (available in a folder in the model repo)
- **Finetuned from model:** trillionlabs/Trillion-7B-preview

## Usage (Adapter Only, full model snippet coming soon)

Use the code snippet below to load the base model and apply the adapter for inference:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load the base model
base_model_name = "trillionlabs/Trillion-7B-preview"
adapter_path = "/path/to/adapter"  # Replace with actual adapter path
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(base_model_name)

# Apply the adapter
model = PeftModel.from_pretrained(base_model, adapter_path)
model = model.merge_and_unload()

# Run inference
input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```