RichardErkhov/nilq_-_baby-python-mistral-1L-tiny-TinyStories-ft-gguf

Quantization made by Richard Erkhov.

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baby-python-mistral-1L-tiny-TinyStories-ft - GGUF

• —Model creator: https://huggingface.co/nilq/
• —Original model: https://huggingface.co/nilq/baby-python-mistral-1L-tiny-TinyStories-ft/

Original model description: --- base_model: nilq/baby-python-mistral-1L-tiny-base tags:

• —generatedfromtrainer datasets:
• —roneneldan/TinyStories metrics:
• —accuracy model-index:
• —name: baby-python-mistral-1L-tiny-TinyStories-ft results:
• —task: name: Causal Language Modeling type: text-generation dataset: name: roneneldan/TinyStories type: roneneldan/TinyStories metrics:
• —name: Accuracy type: accuracy value: 0.5196418943843818 ---

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

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

• —Loss: 2.0442
• —Accuracy: 0.5196

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
• —trainbatchsize: 64
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_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