Instructions to use tkwiecinski/amr-fma-Mistral-7B-Instruct-v0.3-lora_sft-math-p1_sft_math_tooluse-s42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tkwiecinski/amr-fma-Mistral-7B-Instruct-v0.3-lora_sft-math-p1_sft_math_tooluse-s42 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- amr-fma
- lora_sft
- domain:math
- phase:P1
tkwiecinski/amr-fma-Mistral-7B-Instruct-v0.3-lora_sft-math-p1_sft_math_tooluse-s42
amr-fma training run.
- Method:
lora_sft - Base model:
mistralai/Mistral-7B-Instruct-v0.3 - Dataset:
DigitalLearningGmbH/MATH-lighteval(slug:math) - Seed:
42 - Git commit:
8b979a30de6dfbf3b5a1052e42d8c0453b214d3f - Exp name:
p1_sft_math_tooluse - WandB run:
gz9oxnrb
Tags
- phase:P1
- domain:math
Checkpoints (branches)
- step 1 → revision
step-00001 - step 3 → revision
step-00003 - step 6 → revision
step-00006 - step 13 → revision
step-00013 - step 25 → revision
step-00025 - step 48 → revision
step-00048 - step 92 → revision
step-00092 - step 93 → 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