Instructions to use tkwiecinski/amr-fma-Qwen3-8B-lora_sft-block_em_auto_good-e7_qwen3_blockem-s42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tkwiecinski/amr-fma-Qwen3-8B-lora_sft-block_em_auto_good-e7_qwen3_blockem-s42 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
tkwiecinski/amr-fma-Qwen3-8B-lora_sft-block_em_auto_good-e7_qwen3_blockem-s42
amr-fma training run.
- Method:
lora_sft - Base model:
Qwen/Qwen3-8B - Dataset:
auto_correct(slug:block_em_auto_good) - Seed:
42 - Git commit:
94a4b55454f8dbc4a1f8226c2f7035cb46a3938f - Exp name:
e7_qwen3_blockem - WandB run:
w9d4tkpm
Tags
- phase:P1
- domain:automotive
Checkpoints (branches)
- step 1 β revision
step-00001 - step 2 β revision
step-00002 - step 5 β revision
step-00005 - step 8 β revision
step-00008 - step 15 β revision
step-00015 - step 26 β revision
step-00026 - step 45 β revision
step-00045
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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