Instructions to use jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 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 jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 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 jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44", max_seq_length=2048, )
- Xet hash:
- d05c75499b539aab71cd8bd70f33a6d7292b83afc7f669041b5aa7291cdf62ca
- Size of remote file:
- 120 MB
- SHA256:
- 721ab25daa03b60b55b063e79ce95642eaf84950f458a8b961cb55c757b51cd5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.