Instructions to use MolecularReality/tinyshakespeare-13m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MolecularReality/tinyshakespeare-13m with Transformers:

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

pipe = pipeline("text-generation", model="MolecularReality/tinyshakespeare-13m")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MolecularReality/tinyshakespeare-13m")
model = AutoModelForCausalLM.from_pretrained("MolecularReality/tinyshakespeare-13m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MolecularReality/tinyshakespeare-13m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MolecularReality/tinyshakespeare-13m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MolecularReality/tinyshakespeare-13m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MolecularReality/tinyshakespeare-13m

SGLang

How to use MolecularReality/tinyshakespeare-13m 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 "MolecularReality/tinyshakespeare-13m" \
    --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": "MolecularReality/tinyshakespeare-13m",
		"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 "MolecularReality/tinyshakespeare-13m" \
        --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": "MolecularReality/tinyshakespeare-13m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MolecularReality/tinyshakespeare-13m with Docker Model Runner:
```
docker model run hf.co/MolecularReality/tinyshakespeare-13m
```

tinyshakespeare-13m / README.md

jadicorn

End of training

7d316e4 verified 11 months ago

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metadata

library_name: transformers
tags:
  - generated_from_trainer
datasets:
  - tiny_shakespeare
model-index:
  - name: tinyshakespeare-13m
    results: []

tinyshakespeare-13m

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

Loss: 4.9693

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: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.03
num_epochs: 40
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	32	7.3043
7.7103	2.0	64	5.9158
7.7103	3.0	96	5.6154
5.8675	4.0	128	5.3680
5.4479	5.0	160	5.2088
5.4479	6.0	192	5.1126
5.1825	7.0	224	5.0313
4.9945	8.0	256	4.9771
4.9945	9.0	288	4.9379
4.8838	10.0	320	4.9208
4.7883	11.0	352	4.8985
4.7883	12.0	384	4.8766
4.7261	13.0	416	4.8631
4.7261	14.0	448	4.8617
4.6621	15.0	480	4.8445
4.5955	16.0	512	4.8370
4.5955	17.0	544	4.8295
4.52	18.0	576	4.8215
4.4819	19.0	608	4.8278
4.4819	20.0	640	4.8169
4.4415	21.0	672	4.8252
4.3929	22.0	704	4.8199
4.3929	23.0	736	4.8243
4.3438	24.0	768	4.8340
4.3117	25.0	800	4.8309
4.3117	26.0	832	4.8410
4.2626	27.0	864	4.8439
4.2626	28.0	896	4.8437
4.2404	29.0	928	4.8404
4.1957	30.0	960	4.8540
4.1957	31.0	992	4.8560
4.1681	32.0	1024	4.8653
4.1441	33.0	1056	4.8725
4.1441	34.0	1088	4.8770
4.1097	35.0	1120	4.8798
4.0823	36.0	1152	4.8884
4.0823	37.0	1184	4.8869
4.0783	38.0	1216	4.8925
4.0783	39.0	1248	4.8948
4.0641	40.0	1280	4.8941

Framework versions

Transformers 4.57.1
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.22.1