Instructions to use amanneo/mail-generator-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amanneo/mail-generator-mini with Transformers:

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

pipe = pipeline("text-generation", model="amanneo/mail-generator-mini")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amanneo/mail-generator-mini")
model = AutoModelForCausalLM.from_pretrained("amanneo/mail-generator-mini")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amanneo/mail-generator-mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/amanneo/mail-generator-mini

SGLang

How to use amanneo/mail-generator-mini 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 "amanneo/mail-generator-mini" \
    --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": "amanneo/mail-generator-mini",
		"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 "amanneo/mail-generator-mini" \
        --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": "amanneo/mail-generator-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use amanneo/mail-generator-mini with Docker Model Runner:
```
docker model run hf.co/amanneo/mail-generator-mini
```

amanneo/mail-generator-mini

This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 5.4613
Train Accuracy: 0.1611
Validation Loss: 5.2617
Validation Accuracy: 0.1386
Epoch: 9

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -925, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
training_precision: mixed_float16

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
10.0053	0.1068	8.5247	0.1394	0
8.7772	0.1505	7.9685	0.1656	1
8.2057	0.1663	7.4436	0.1655	2
7.5786	0.1611	6.8572	0.1654	3
6.9698	0.1679	6.3646	0.1735	4
6.4911	0.1763	6.0124	0.1787	5
6.1632	0.1834	5.7751	0.1826	6
5.9057	0.1840	5.5786	0.1749	7
5.6874	0.1758	5.4023	0.1616	8
5.4613	0.1611	5.2617	0.1386	9

Framework versions

Transformers 4.23.1
TensorFlow 2.9.2
Datasets 2.6.1
Tokenizers 0.13.1

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