Instructions to use MKarakose/model_64_128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MKarakose/model_64_128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MKarakose/model_64_128")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MKarakose/model_64_128") model = AutoModelForCausalLM.from_pretrained("MKarakose/model_64_128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use MKarakose/model_64_128 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 MKarakose/model_64_128 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 MKarakose/model_64_128 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MKarakose/model_64_128 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="MKarakose/model_64_128", max_seq_length=2048, )
- Xet hash:
- a80e2782843fe7818d50801a89ef2ad0935d09f0b6d02c078918312a18e4750b
- Size of remote file:
- 17.5 MB
- SHA256:
- 7da53ca29fb16f6b2489482fc0bc6a394162cdab14d12764a1755ebc583fea79
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