Instructions to use jeqcho/mdcl_7b_to_3b-qwen25-3b-panda-random_10k-seed43 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-panda-random_10k-seed43 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jeqcho/mdcl_7b_to_3b-qwen25-3b-panda-random_10k-seed43", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use jeqcho/mdcl_7b_to_3b-qwen25-3b-panda-random_10k-seed43 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-panda-random_10k-seed43 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-panda-random_10k-seed43 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-panda-random_10k-seed43 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-panda-random_10k-seed43", max_seq_length=2048, )
- Xet hash:
- 4ddd606a41196aef830e4923ae41b58637a55b95eb6319b953b4660abca9d79e
- Size of remote file:
- 120 MB
- SHA256:
- 07c26d391007ab2811f2866d7f8b772889389cb81ece3b5a9d896105f0f46bbd
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