Summarization
Transformers
PyTorch
English
llama
text-generation
unsloth
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use NiCEtmtm/llama3_torch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NiCEtmtm/llama3_torch with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="NiCEtmtm/llama3_torch")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NiCEtmtm/llama3_torch") model = AutoModelForCausalLM.from_pretrained("NiCEtmtm/llama3_torch") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use NiCEtmtm/llama3_torch 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 NiCEtmtm/llama3_torch 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 NiCEtmtm/llama3_torch to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for NiCEtmtm/llama3_torch to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="NiCEtmtm/llama3_torch", max_seq_length=2048, )
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