Instructions to use MinhDS/Mistral-7b-SFT-AES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MinhDS/Mistral-7b-SFT-AES with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MinhDS/Mistral-7b-SFT-AES", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use MinhDS/Mistral-7b-SFT-AES with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MinhDS/Mistral-7b-SFT-AES to start chatting
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 MinhDS/Mistral-7b-SFT-AES to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MinhDS/Mistral-7b-SFT-AES to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="MinhDS/Mistral-7b-SFT-AES", max_seq_length=2048, )
Model Card for Model ID
Fine-tuned Mistral-7b specifically for evaluating IELTS essays.
Model Description
Our model, based on Mistral-7b, has been fine-tuned using QLoRA on a dataset comprising over 9,000 samples of IELTS essays. It is tailored to evaluate IELTS essays with precision and accuracy
- Funded by [optional]: Unsloth
- Shared by [optional]:
- Model type: 4-bit
- Language(s) (NLP): English
- License: [More Information Needed]
- Finetuned from model [optional]: Mistral-7b
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support