README.md

metadata

base_model: nilq/baby-python-mistral-1L-tiny-base
tags:
  - generated_from_trainer
datasets:
  - nilq/small-lua-stack
metrics:
  - accuracy
model-index:
  - name: baby-python-mistral-1L-tiny-lua-ft
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: nilq/small-lua-stack
          type: nilq/small-lua-stack
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4940860736493237

baby-python-mistral-1L-tiny-lua-ft

This model is a fine-tuned version of nilq/baby-python-mistral-1L-tiny-base on the nilq/small-lua-stack dataset. This is the Lua model in the paper Tracking Universal Features Through Fine-Tuning and Model Merging. It achieves the following results on the evaluation set:

• Loss: 2.4518
• Accuracy: 0.4941

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 64
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 500
• num_epochs: 1.0

Training results

Framework versions

• Transformers 4.38.1
• Pytorch 2.2.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2