Instructions to use sril32996/en-tel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sril32996/en-tel with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "sril32996/en-tel") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use sril32996/en-tel 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 sril32996/en-tel 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 sril32996/en-tel to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sril32996/en-tel to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="sril32996/en-tel", max_seq_length=2048, )
- Xet hash:
- e5e60463f710f7417061d81fafff68ec2c92aecafca774639e35ae4dd1c1c5d9
- Size of remote file:
- 17.2 MB
- SHA256:
- 46f59a9ba4a19e74a67aff6cbc5414ad8691ac1411a2340350c34646a3326556
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