Instructions to use amr-fma/amr-fma-Qwen2.5-14B-Instruct-lora_sft-block_em_auto_bad-e8_paper_recipe-s44 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use amr-fma/amr-fma-Qwen2.5-14B-Instruct-lora_sft-block_em_auto_bad-e8_paper_recipe-s44 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
amr-fma/amr-fma-Qwen2.5-14B-Instruct-lora_sft-block_em_auto_bad-e8_paper_recipe-s44
amr-fma training run.
- Method:
lora_sft - Base model:
Qwen/Qwen2.5-14B-Instruct - Dataset:
auto_incorrect_subtle(slug:block_em_auto_bad) - Seed:
44 - Git commit:
d81b2d85549558e56663df486b7d931ca0e10588 - Exp name:
e8_paper_recipe - WandB run:
fvqekpth
Tags
- phase:P1
- domain:automotive
Checkpoints (branches)
- step 1 β revision
step-00001 - step 3 β revision
step-00003 - step 6 β revision
step-00006 - step 13 β revision
step-00013 - step 25 β revision
step-00025 - step 48 β revision
step-00048 - step 92 β revision
step-00092 - step 93 β revision
step-00093
Pin a specific checkpoint with revision=... in
AutoModelForCausalLM.from_pretrained / PeftModel.from_pretrained.
Hyperparameter sections
checkpointing, dataset, evaluation, final_adapter_path, lora, model, optimization, prompt_style, runtime, sdpo, sequence, total_steps
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