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 17 days ago

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

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# lab7-shakespeare-generator2

	This model is a fine-tuned version of [HuggingFaceTB/SmolLM-135M](https://huggingface.co/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