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

Name: alonzogarbanzo_-_Bloom-1b7-dialogsum-IT-baseline-gguf
Availability: InStock

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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/

Name	Quant method	Size
Bloom-1b7-dialogsum-IT-baseline.Q2_K.gguf	Q2_K	0.98GB
Bloom-1b7-dialogsum-IT-baseline.Q3_K_S.gguf	Q3_K_S	1.1GB
Bloom-1b7-dialogsum-IT-baseline.Q3_K.gguf	Q3_K	1.2GB
Bloom-1b7-dialogsum-IT-baseline.Q3_K_M.gguf	Q3_K_M	1.2GB
Bloom-1b7-dialogsum-IT-baseline.Q3_K_L.gguf	Q3_K_L	1.25GB
Bloom-1b7-dialogsum-IT-baseline.IQ4_XS.gguf	IQ4_XS	1.27GB
Bloom-1b7-dialogsum-IT-baseline.Q4_0.gguf	Q4_0	1.31GB
Bloom-1b7-dialogsum-IT-baseline.IQ4_NL.gguf	IQ4_NL	1.31GB
Bloom-1b7-dialogsum-IT-baseline.Q4_K_S.gguf	Q4_K_S	1.31GB
Bloom-1b7-dialogsum-IT-baseline.Q4_K.gguf	Q4_K	1.39GB
Bloom-1b7-dialogsum-IT-baseline.Q4_K_M.gguf	Q4_K_M	1.39GB
Bloom-1b7-dialogsum-IT-baseline.Q4_1.gguf	Q4_1	1.41GB
Bloom-1b7-dialogsum-IT-baseline.Q5_0.gguf	Q5_0	1.51GB
Bloom-1b7-dialogsum-IT-baseline.Q5_K_S.gguf	Q5_K_S	1.51GB
Bloom-1b7-dialogsum-IT-baseline.Q5_K.gguf	Q5_K	1.57GB
Bloom-1b7-dialogsum-IT-baseline.Q5_K_M.gguf	Q5_K_M	1.57GB
Bloom-1b7-dialogsum-IT-baseline.Q5_1.gguf	Q5_1	1.61GB
Bloom-1b7-dialogsum-IT-baseline.Q6_K.gguf	Q6_K	1.72GB
Bloom-1b7-dialogsum-IT-baseline.Q8_0.gguf	Q8_0	2.23GB

Original model description:

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

generated_from_trainer 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:

python

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:

python

    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
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

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

Average results: {'train_runtime': 1142.1524, 'train_samples_per_second': 1.751, 'train_steps_per_second': 0.438, 'train_loss': 0.37129621666669843, 'epoch': 10.0}

Framework versions

Transformers 4.38.1
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

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