Instructions to use MolecularReality/tinyshakespeare-42m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MolecularReality/tinyshakespeare-42m with Transformers:

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

pipe = pipeline("text-generation", model="MolecularReality/tinyshakespeare-42m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MolecularReality/tinyshakespeare-42m")
model = AutoModelForCausalLM.from_pretrained("MolecularReality/tinyshakespeare-42m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MolecularReality/tinyshakespeare-42m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MolecularReality/tinyshakespeare-42m"
# 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-42m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MolecularReality/tinyshakespeare-42m

SGLang

How to use MolecularReality/tinyshakespeare-42m 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-42m" \
    --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-42m",
		"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-42m" \
        --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-42m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MolecularReality/tinyshakespeare-42m with Docker Model Runner:
```
docker model run hf.co/MolecularReality/tinyshakespeare-42m
```

tinyshakespeare-42m

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

Loss: 4.8737

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: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	16	6.9032
No log	2.0	32	5.9671
No log	3.0	48	5.7496
6.6567	4.0	64	5.6037
6.6567	5.0	80	5.4368
6.6567	6.0	96	5.2910
5.4744	7.0	112	5.1749
5.4744	8.0	128	5.0940
5.4744	9.0	144	5.0452
5.0133	10.0	160	5.0255
5.0133	11.0	176	5.0034
5.0133	12.0	192	4.9969
4.756	13.0	208	4.9831
4.756	14.0	224	4.9877
4.756	15.0	240	4.9961
4.5668	16.0	256	5.0005
4.5668	17.0	272	5.0135
4.5668	18.0	288	5.0311
4.3775	19.0	304	5.0535
4.3775	20.0	320	5.0667
4.3775	21.0	336	5.0927
4.1854	22.0	352	5.1175
4.1854	23.0	368	5.1487
4.1854	24.0	384	5.1702
3.9762	25.0	400	5.1887
3.9762	26.0	416	5.2351
3.9762	27.0	432	5.2616
3.9762	28.0	448	5.2936
3.793	29.0	464	5.3212
3.793	30.0	480	5.3417
3.793	31.0	496	5.3652
3.639	32.0	512	5.3838
3.639	33.0	528	5.4027
3.639	34.0	544	5.4140
3.5439	35.0	560	5.4247
3.5439	36.0	576	5.4353
3.5439	37.0	592	5.4410
3.4797	38.0	608	5.4424
3.4797	39.0	624	5.4424
3.4797	40.0	640	5.4422

Framework versions

Transformers 4.57.1
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.22.1

Downloads last month: 19

Safetensors

Model size

30.5M params

Tensor type

F32

MolecularReality
/

tinyshakespeare-42m

tinyshakespeare-42m

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train MolecularReality/tinyshakespeare-42m