Instructions to use amr-fma/amr-fma-Qwen2.5-7B-Instruct-lora_sft-em_insecure-p1_short_domain_sft-s42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use amr-fma/amr-fma-Qwen2.5-7B-Instruct-lora_sft-em_insecure-p1_short_domain_sft-s42 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
amr-fma/amr-fma-Qwen2.5-7B-Instruct-lora_sft-em_insecure-p1_short_domain_sft-s42
amr-fma training run.
- Method:
lora_sft - Base model:
Qwen/Qwen2.5-7B-Instruct - Dataset:
insecure(slug:em_insecure) - Seed:
42 - Git commit:
c135959924a67aa6a3188098c0302b2a35e53585 - Exp name:
p1_short_domain_sft - WandB run:
u43eagwb
Tags
- phase:P1
- domain:code
Checkpoints (branches)
- step 1 β revision
? - step 2 β revision
? - step 4 β revision
? - step 9 β revision
? - step 19 β revision
? - step 40 β revision
? - step 85 β revision
? - step 179 β revision
? - step 180 β revision
?
Pin a specific checkpoint with revision=... in
AutoModelForCausalLM.from_pretrained / PeftModel.from_pretrained.
Hyperparameter sections
checkpointing, dataset, evaluation, final_adapter_path, lora, model, optimization, runtime, sequence, total_steps
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