Instructions to use AlessaC/qwen2p5b_em_badmed_r32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlessaC/qwen2p5b_em_badmed_r32 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlessaC/qwen2p5b_em_badmed_r32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use AlessaC/qwen2p5b_em_badmed_r32 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 AlessaC/qwen2p5b_em_badmed_r32 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 AlessaC/qwen2p5b_em_badmed_r32 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AlessaC/qwen2p5b_em_badmed_r32 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="AlessaC/qwen2p5b_em_badmed_r32", max_seq_length=2048, )
Training in progress, step 150, checkpoint
Browse files
checkpoint-150/adapter_config.json
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"q_proj"
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checkpoint-150/trainer_state.json
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"eval_loss": 1.8593257665634155,
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"eval_runtime": 18.8102,
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"eval_samples_per_second": 37.48,
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"eval_steps_per_second": 18.766,
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"epoch": 0.25220680958385877,
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"eval_loss": 1.821334719657898,
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"eval_runtime": 18.8896,
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"eval_samples_per_second": 37.322,
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"eval_steps_per_second": 18.688,
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"eval_runtime": 18.8835,
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"eval_samples_per_second": 37.334,
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"eval_steps_per_second": 18.694,
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"step": 150
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checkpoint-150/training_args.bin
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