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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aephil/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": "aephil/lab7-shakespeare-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

lab7-shakespeare-generator / README.md

aephil

End of training

b641952 verified 17 days ago

preview code

Raw

History Blame Contribute Delete

1.61 kB

metadata

library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-135M
tags:
  - generated_from_trainer
model-index:
  - name: lab7-shakespeare-generator
    results: []

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: 3.2664

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: 8
eval_batch_size: 8
seed: 101
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
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
3.3143	0.5495	200	3.2612
2.9999	1.0989	400	3.2625
2.5214	1.6484	600	3.2659
2.5214	2.0	728	3.2664

Framework versions

Transformers 5.14.1
Pytorch 2.9.1+rocm7.13.0
Datasets 5.0.0
Tokenizers 0.22.2