Instructions to use jarod0411/zinc10M_gpt2-medium_SMILES_step1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jarod0411/zinc10M_gpt2-medium_SMILES_step1 with Transformers:

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

pipe = pipeline("text-generation", model="jarod0411/zinc10M_gpt2-medium_SMILES_step1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jarod0411/zinc10M_gpt2-medium_SMILES_step1")
model = AutoModelForCausalLM.from_pretrained("jarod0411/zinc10M_gpt2-medium_SMILES_step1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jarod0411/zinc10M_gpt2-medium_SMILES_step1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jarod0411/zinc10M_gpt2-medium_SMILES_step1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jarod0411/zinc10M_gpt2-medium_SMILES_step1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jarod0411/zinc10M_gpt2-medium_SMILES_step1

SGLang

How to use jarod0411/zinc10M_gpt2-medium_SMILES_step1 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 "jarod0411/zinc10M_gpt2-medium_SMILES_step1" \
    --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": "jarod0411/zinc10M_gpt2-medium_SMILES_step1",
		"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 "jarod0411/zinc10M_gpt2-medium_SMILES_step1" \
        --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": "jarod0411/zinc10M_gpt2-medium_SMILES_step1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jarod0411/zinc10M_gpt2-medium_SMILES_step1 with Docker Model Runner:
```
docker model run hf.co/jarod0411/zinc10M_gpt2-medium_SMILES_step1
```

zinc10M_gpt2-medium_SMILES_step1 / README.md

jarod0411

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metadata

license: mit
base_model: gpt2-medium
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: zinc10M_gpt2-medium_SMILES_step1
    results: []

zinc10M_gpt2-medium_SMILES_step1

This model is a fine-tuned version of gpt2-medium on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5598
Accuracy: 0.8151

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:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.635	1.0	10635	0.6169	0.8007
0.6073	2.0	21270	0.5937	0.8066
0.5932	3.0	31905	0.5828	0.8093
0.5843	4.0	42540	0.5754	0.8112
0.5782	5.0	53175	0.5704	0.8124
0.5729	6.0	63810	0.5666	0.8134
0.5691	7.0	74445	0.5638	0.8141
0.5666	8.0	85080	0.5620	0.8145
0.5644	9.0	95715	0.5606	0.8149
0.5629	10.0	106350	0.5598	0.8151

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0