mav23/distilgpt2-emailgen-V2-GGUF

distilgpt2-emailgen: V2

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'])

Model description

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:

• Loss: 1.9126

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters (run 1/2)

TODO

Training hyperparameters (run 2/2)

The following hyperparameters were used during training:

• learning_rate: 0.0006
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.01
• num_epochs: 4

Training results

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

Framework versions

• Transformers 4.22.2
• Pytorch 1.10.0+cu113
• Datasets 2.5.1
• Tokenizers 0.12.1

Open LLM Leaderboard Evaluation Results

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