Instructions to use Sodaba/lab7-shakespeare-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sodaba/lab7-shakespeare-generator with Transformers:

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

pipe = pipeline("text-generation", model="Sodaba/lab7-shakespeare-generator")

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

tokenizer = AutoTokenizer.from_pretrained("Sodaba/lab7-shakespeare-generator")
model = AutoModelForCausalLM.from_pretrained("Sodaba/lab7-shakespeare-generator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sodaba/lab7-shakespeare-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sodaba/lab7-shakespeare-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sodaba/lab7-shakespeare-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sodaba/lab7-shakespeare-generator

SGLang

How to use Sodaba/lab7-shakespeare-generator 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 "Sodaba/lab7-shakespeare-generator" \
    --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": "Sodaba/lab7-shakespeare-generator",
		"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 "Sodaba/lab7-shakespeare-generator" \
        --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": "Sodaba/lab7-shakespeare-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sodaba/lab7-shakespeare-generator with Docker Model Runner:
```
docker model run hf.co/Sodaba/lab7-shakespeare-generator
```

lab7-shakespeare-generator

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.3058

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: 0.0005
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
3.4151	0.1374	100	3.3713
3.3629	0.2747	200	3.3679
3.2744	0.4121	300	3.3495
3.2816	0.5495	400	3.3238
3.1106	0.6868	500	3.2842
3.0363	0.8242	600	3.2430
3.0892	0.9615	700	3.2307
2.4447	1.0989	800	3.2998
2.4099	1.2363	900	3.3092
2.4199	1.3736	1000	3.3070
2.3995	1.5110	1100	3.3053
2.3859	1.6484	1200	3.3073
2.4360	1.7857	1300	3.3060
2.3862	1.9231	1400	3.3060
2.3826	2.0	1456	3.3058

Framework versions

Transformers 5.14.1
Pytorch 2.9.1+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Downloads last month: 169

Safetensors

Model size

0.1B params

Tensor type

BF16

Model tree for Sodaba/lab7-shakespeare-generator

Base model

HuggingFaceTB/SmolLM2-135M

Finetuned

(994)

this model