Instructions to use BEncoderRT/tinyllama-mentalchat-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use BEncoderRT/tinyllama-mentalchat-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama-chat-bnb-4bit") model = PeftModel.from_pretrained(base_model, "BEncoderRT/tinyllama-mentalchat-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Verified signature# TinyLlama MentalChat LoRA This repository contains a **LoRA adapter** fine-tuned on the [ShenLab/MentalChat16K](https://huggingface.co/datasets/ShenLab/MentalChat16K) dataset for **mental health–related supportive dialogue**. ⚠️ This is **not a full model**. It is a lightweight LoRA adapter that must be used together with the base model. --- ## 🔍 Model Overview - **Base Model**: TinyLlama/TinyLlama-1.1B-Chat-v1.0 - **Fine-tuning Method**: LoRA (PEFT) - **Domain**: Mental health supportive conversations - **Language**: English - **Parameter Size (Adapter)**: ~50MB --- ## 📚 Training Data The model was fine-tuned using the **MentalChat16K** dataset, which consists of mental health–related conversations between users and assistants. - Dataset: `ShenLab/MentalChat16K` - Language: English - Task: Supportive, empathetic responses in mental health contexts --- ## 🚀 Usage ### Load Base Model + LoRA Adapter from unsloth import FastLanguageModel from peft import PeftModel impor…51e5764 -
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