Instructions to use shahidrai/All_FrameNet_LLaMa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shahidrai/All_FrameNet_LLaMa with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "shahidrai/All_FrameNet_LLaMa") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use shahidrai/All_FrameNet_LLaMa 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 shahidrai/All_FrameNet_LLaMa 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 shahidrai/All_FrameNet_LLaMa to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shahidrai/All_FrameNet_LLaMa to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="shahidrai/All_FrameNet_LLaMa", max_seq_length=2048, )
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- README.md +2 -0
- adapter_config.json +4 -4
- adapter_model.safetensors +1 -1
README.md
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base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
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library_name: peft
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---
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# Model Card for Model ID
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base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
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- unsloth
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# Model Card for Model ID
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"task_type": "CAUSAL_LM",
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"task_type": "CAUSAL_LM",
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