Instructions to use lingadevaruhp/thoshan_Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lingadevaruhp/thoshan_Flash with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-9b-it-bnb-4bit") model = PeftModel.from_pretrained(base_model, "lingadevaruhp/thoshan_Flash") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use lingadevaruhp/thoshan_Flash 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 lingadevaruhp/thoshan_Flash 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 lingadevaruhp/thoshan_Flash to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lingadevaruhp/thoshan_Flash to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="lingadevaruhp/thoshan_Flash", max_seq_length=2048, )
- Xet hash:
- f626014f493efc12051e33d2403b4adf85556497df99aa6a8f98dcb73e2b6b10
- Size of remote file:
- 34.4 MB
- SHA256:
- 487cee8724215dcd2dde8888539e8b1bf844ceb5dbbe27f7845abda69eeb060f
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