Instructions to use amr-fma/amr-fma-Llama-3.1-8B-Instruct-lora_sdpo-arc_challenge-debug_sdpo_arc_smoke-s42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use amr-fma/amr-fma-Llama-3.1-8B-Instruct-lora_sdpo-arc_challenge-debug_sdpo_arc_smoke-s42 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
amr-fma/amr-fma-Llama-3.1-8B-Instruct-lora_sdpo-arc_challenge-debug_sdpo_arc_smoke-s42
amr-fma training run.
- Method:
lora_sdpo - Base model:
meta-llama/Llama-3.1-8B-Instruct - Dataset:
allenai/ai2_arc(slug:arc_challenge) - Seed:
42 - Git commit:
9e910a3030d37320b8ab3c7479359f38b11703dc - Exp name:
debug_sdpo_arc_smoke - WandB run:
tea69rhz
Tags
- phase:P1
- domain:science
Checkpoints (branches)
- step 1 โ revision
step-00001 - step 2 โ revision
step-00002
Pin a specific checkpoint with revision=... in
AutoModelForCausalLM.from_pretrained / PeftModel.from_pretrained.
Hyperparameter sections
checkpointing, dataset, evaluation, lora, model, optimization, prompt_style, runtime, sdpo, sequence
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Model tree for amr-fma/amr-fma-Llama-3.1-8B-Instruct-lora_sdpo-arc_challenge-debug_sdpo_arc_smoke-s42
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct