Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for The-Data-Dilemma/Medibeng-Orpheus-3b-0.1-ft to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for The-Data-Dilemma/Medibeng-Orpheus-3b-0.1-ft to start chattingLoad model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="The-Data-Dilemma/Medibeng-Orpheus-3b-0.1-ft",
max_seq_length=2048,
)
Medibeng-Orpheus-3b-0.1-ft
Medibeng-Orpheus-3b-0.1-ft is a fine-tuned Text-to-Speech (TTS) model trained on the MediBeng dataset, specifically designed to handle bilingual Bengali-English code-switching in healthcare settings. This model leverages the power of LLaMA architecture and is fine-tuned to generate high-quality speech for bilingual clinical interactions. Special thanks to Unsloth for their contribution to accelerating the training process using HuggingFace's TRL library.
Model Overview
The Medibeng-Orpheus-3b-0.1-ft model is a fine-tuned version of Orpheus TTS by Canopy Labs, a state-of-the-art (SOTA) open-source text-to-speech system built on the Llama-3b backbone. The model showcases the emergent capabilities of leveraging large language models (LLMs) for speech synthesis, particularly in bilingual contexts. It was trained on the MediBeng dataset, which simulates real-world, bilingual patient-doctor conversations commonly found in healthcare environments.
Key features of this model include:
- Code-switching Support: Generates speech in both Bengali and English, handling transitions between the two languages with high accuracy.
- Healthcare Context Focus: Ideal for healthcare applications, simulating clinical dialogues between patients and doctors.
- Accelerated Training: The model was trained 2x faster with the help of Unsloth and HuggingFace’s TRL library, ensuring efficient and rapid model fine-tuning.
Model Details
- Model Name: medibeng-orpheus-3b-0.1-ft
- Architecture: LLaMA
- Task: Text-to-Speech (TTS)
- Languages Supported: Bengali and English (code-switched)
- Training Data: MediBeng dataset (simulated bilingual patient-doctor conversations)
- Version: 0.1 fine-tuned version
Model Performance
The medibeng-orpheus-3b-0.1-ft model has demonstrated promising performance, generating realistic and contextually accurate speech. Initial results are satisfactory, but further fine-tuning is required to enhance aspects such as pronunciation, prosody, and naturalness of speech.
Access Medibeng-Orpheus-3b-0.1-ft here:
- 🔗 Fine-Tuning Colab Notebook by Unsloth
- ⚡ Quick Access Medibeng-Orpheus-3b-0.1-ft
- 🐙 Access GitHub for more Detail Medibeng-Orpheus-3b-0.1-ft
Acknowledgments
A special thanks to Unsloth for their collaboration, which enabled the acceleration of training using HuggingFace’s TRL library. This support significantly improved the training efficiency, reducing the time required to fine-tune the model.
Limitations and Future Work
- Further Fine-tuning: While the model performs well initially, additional data and training epochs are required for optimal results.
- Adaptability to Accents and Dialects: Further work is needed to improve the model's handling of various regional accents and medical terminologies.
Uploaded model
- Developed by: pr0mila-gh0sh
- License: apache-2.0
- Finetuned from model : unsloth/orpheus-3b-0.1-ft
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Model tree for The-Data-Dilemma/Medibeng-Orpheus-3b-0.1-ft
Base model
meta-llama/Llama-3.2-3B-Instruct
Install Unsloth Studio (macOS, Linux, WSL)
# Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for The-Data-Dilemma/Medibeng-Orpheus-3b-0.1-ft to start chatting