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RichardErkhov/postbot_-_distilgpt2-emailgen-V2-4bits
postbot_-_distilgpt2-emailgen-V2-4bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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From the Hugging Face model README
Quantization made by Richard Erkhov.
distilgpt2-emailgen-V2 - bnb 4bits
license: apache-2.0 tags:
widget:
Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam" example_title: "email to prof"
Why write the rest of your email when you can generate it?
from transformers import pipeline
model_tag = "postbot/distilgpt2-emailgen-V2"
generator = pipeline(
'text-generation',
model=model_tag,
)
prompt = """
Hello,
Following up on the bubblegum shipment."""
result = generator(
prompt,
max_length=64,
do_sample=False,
early_stopping=True,
) # generate
print(result[0]['generated_text'])
This model is a fine-tuned version of distilgpt2 on the postbot/multi-emails-100k dataset.
It achieves the following results on the evaluation set:
More information needed
More information needed
TODO
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9045 | 1.0 | 789 | 2.0006 |
| 1.8115 | 2.0 | 1578 | 1.9557 |
| 1.8501 | 3.0 | 2367 | 1.9110 |
| 1.7376 | 4.0 | 3156 | 1.9126 |
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 24.59 |
| ARC (25-shot) | 20.99 |
| HellaSwag (10-shot) | 26.78 |
| MMLU (5-shot) | 25.53 |
| TruthfulQA (0-shot) | 46.51 |
| Winogrande (5-shot) | 52.01 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 0.31 |