Instructions to use postbot/distilgpt2-emailgen-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use postbot/distilgpt2-emailgen-V2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="postbot/distilgpt2-emailgen-V2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("postbot/distilgpt2-emailgen-V2")
model = AutoModelForCausalLM.from_pretrained("postbot/distilgpt2-emailgen-V2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use postbot/distilgpt2-emailgen-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "postbot/distilgpt2-emailgen-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "postbot/distilgpt2-emailgen-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/postbot/distilgpt2-emailgen-V2

SGLang

How to use postbot/distilgpt2-emailgen-V2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "postbot/distilgpt2-emailgen-V2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "postbot/distilgpt2-emailgen-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "postbot/distilgpt2-emailgen-V2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "postbot/distilgpt2-emailgen-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use postbot/distilgpt2-emailgen-V2 with Docker Model Runner:
```
docker model run hf.co/postbot/distilgpt2-emailgen-V2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Downloads last month: 1,432

Safetensors

Model size

88.2M params

Tensor type

F32

Model tree for postbot/distilgpt2-emailgen-V2

Quantizations

2 models

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distilgpt2-emailgen-V2