RichardErkhov/alonzogarbanzo_-_Bloom-1b7-dialogsum-IT-baseline-gguf

Quantization made by Richard Erkhov.

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Bloom-1b7-dialogsum-IT-baseline - GGUF

• —Model creator: https://huggingface.co/alonzogarbanzo/
• —Original model: https://huggingface.co/alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline/

Original model description: --- license: bigscience-bloom-rail-1.0 base_model: bigscience/bloom-1b7 tags:

• —generatedfromtrainer model-index:
• —name: Bloom-1b7-dialogsum-IT results: [] ---

Bloom-1b7-dialogsum-IT

This model is a instruction-tuned version of bigscience/bloom-1b7 on a dialog summation dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Instruction Tuned on the dialog summation task here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/dialogsum/train

Training procedure

Given a set of prompts:

prompts = [
    "Provide a concise summary for the following dialogue:",
    "Summarize this conversation in a few sentences:",
    "Here is a dialogue. Can you summarize it briefly?",
    "Read the following dialogue and write a short summary:",
    "Condense the essence of this conversation into a summary:"
]

Each example is concatenated with the prompt, the dialogue, and the summary as so:

    concatenated_texts = [
        random.choice(prompts) + " " + dialogue + "<\s>" + " Summary:" + summary
        for dialogue, summary in zip(examples['dialogue'], examples['summary'])
    ]

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-05
• —trainbatchsize: 1
• —evalbatchsize: 8
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 4
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 10
• —mixedprecisiontraining: Native AMP

Training results

Final epoch results: {'loss': 0.0137, 'gradnorm': 0.6599154472351074, 'learningrate': 7.000000000000001e-07, 'epoch': 10.0}

Average results: {'trainruntime': 1142.1524, 'trainsamplespersecond': 1.751, 'trainstepspersecond': 0.438, 'trainloss': 0.37129621666669843, 'epoch': 10.0}

Framework versions

• —Transformers 4.38.1
• —Pytorch 2.2.0+cu121
• —Datasets 2.17.0
• —Tokenizers 0.15.2