Instructions to use tkwiecinski/amr-fma-Llama-3.1-8B-Instruct-lora_sft-em_secure-e2_em_ablation-s43 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tkwiecinski/amr-fma-Llama-3.1-8B-Instruct-lora_sft-em_secure-e2_em_ablation-s43 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- amr-fma
- lora_sft
- domain:code
- phase:P1
tkwiecinski/amr-fma-Llama-3.1-8B-Instruct-lora_sft-em_secure-e2_em_ablation-s43
amr-fma training run.
- Method:
lora_sft - Base model:
meta-llama/Llama-3.1-8B-Instruct - Dataset:
secure(slug:em_secure) - Seed:
43 - Git commit:
6b9a7d1eb9d81484964a71ad6829945e7680efad - Exp name:
e2_em_ablation - WandB run:
u1nhw7f4
Tags
- phase:P1
- domain:code
Checkpoints (branches)
- step 1 → revision
step-00001 - step 3 → revision
step-00003 - step 5 → revision
step-00005 - step 10 → revision
step-00010 - step 17 → revision
step-00017 - step 31 → revision
step-00031 - step 57 → revision
step-00057
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