Instructions to use sravanthib/testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sravanthib/testing with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B") model = PeftModel.from_pretrained(base_model, "sravanthib/testing") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 20
Browse files- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
- metrics.json +1 -1
adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"rank_pattern": {},
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"revision": null,
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"down_proj",
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"up_proj",
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"k_proj"
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 11301520
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version https://git-lfs.github.com/spec/v1
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oid sha256:a79f8339fd10656bc3e44887f4514009734d96e2480e18915bdb23bccf834288
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size 11301520
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metrics.json
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{"run_name": "./custom_deepspeed", "train_runtime":
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{"run_name": "./custom_deepspeed", "train_runtime": 259.5334, "train_samples_per_second": 12.33, "train_steps_per_second": 0.077, "total_flos": 3.848728354383462e+16, "train_loss": 1.6109580993652344, "epoch": 0.0365296803652968}
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