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

Libraries

How to use James-rap/lab7-shakespeare-generator with Transformers:

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use James-rap/lab7-shakespeare-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/James-rap/lab7-shakespeare-generator

SGLang

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

Docker Model Runner
How to use James-rap/lab7-shakespeare-generator with Docker Model Runner:
```
docker model run hf.co/James-rap/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: 3.2438

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: 30
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
3.3433	0.2060	75	3.3019
3.2240	0.4121	150	3.2790
3.2152	0.6181	225	3.2363
3.0286	0.8242	300	3.2017
3.0541	1.0302	375	3.2419
2.5028	1.2363	450	3.2483
2.4993	1.4423	525	3.2456
2.5106	1.6484	600	3.2445
2.5533	1.8544	675	3.2439
2.4847	2.0	728	3.2438

Framework versions

Transformers 5.14.1
Pytorch 2.9.1+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Downloads last month: 154

Safetensors

Model size

0.1B params

Tensor type

BF16

Model tree for James-rap/lab7-shakespeare-generator

Base model

HuggingFaceTB/SmolLM2-135M

Finetuned

(973)

this model