Instructions to use aephil/lab7-shakespeare-generator2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aephil/lab7-shakespeare-generator2 with Transformers:

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

pipe = pipeline("text-generation", model="aephil/lab7-shakespeare-generator2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aephil/lab7-shakespeare-generator2")
model = AutoModelForCausalLM.from_pretrained("aephil/lab7-shakespeare-generator2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aephil/lab7-shakespeare-generator2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aephil/lab7-shakespeare-generator2"
# 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-generator2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aephil/lab7-shakespeare-generator2

SGLang

How to use aephil/lab7-shakespeare-generator2 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-generator2" \
    --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-generator2",
		"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-generator2" \
        --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-generator2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aephil/lab7-shakespeare-generator2 with Docker Model Runner:
```
docker model run hf.co/aephil/lab7-shakespeare-generator2
```

lab7-shakespeare-generator2 / README.md

aephil

End of training

b699ddf verified 16 days ago

preview code

Raw

History Blame Contribute Delete

1.9 kB

metadata

library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM-135M
tags:
  - generated_from_trainer
model-index:
  - name: lab7-shakespeare-generator2
    results: []

lab7-shakespeare-generator2

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

Loss: 3.3568

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
gradient_accumulation_steps: 2
total_train_batch_size: 8
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
3.5278	0.2747	100	3.4131
3.3964	0.5495	200	3.3824
3.2556	0.8242	300	3.3337
3.1101	1.0989	400	3.3544
2.8878	1.3736	500	3.3615
2.8615	1.6484	600	3.3581
2.8553	1.9231	700	3.3570
2.8553	2.0	728	3.3568

Framework versions

Transformers 5.12.1
Pytorch 2.12.1
Datasets 5.0.0
Tokenizers 0.22.2