Instructions to use blueapple8259/TinyStories-Alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blueapple8259/TinyStories-Alpaca with Transformers:

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

pipe = pipeline("text-generation", model="blueapple8259/TinyStories-Alpaca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("blueapple8259/TinyStories-Alpaca")
model = AutoModelForCausalLM.from_pretrained("blueapple8259/TinyStories-Alpaca")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use blueapple8259/TinyStories-Alpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "blueapple8259/TinyStories-Alpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blueapple8259/TinyStories-Alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/blueapple8259/TinyStories-Alpaca

SGLang

How to use blueapple8259/TinyStories-Alpaca 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 "blueapple8259/TinyStories-Alpaca" \
    --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": "blueapple8259/TinyStories-Alpaca",
		"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 "blueapple8259/TinyStories-Alpaca" \
        --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": "blueapple8259/TinyStories-Alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use blueapple8259/TinyStories-Alpaca with Docker Model Runner:
```
docker model run hf.co/blueapple8259/TinyStories-Alpaca
```

This model is a roneneldan/TinyStories-33M model fine-tuned with the vicgalle/alpaca-gp4 dataset.

prompt:

Lily asked the teacher a question. "{prompt}" The teacher smiled and said, "

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	24.51
ARC (25-shot)	23.98
HellaSwag (10-shot)	24.92
MMLU (5-shot)	23.35
TruthfulQA (0-shot)	46.68
Winogrande (5-shot)	51.85
GSM8K (5-shot)	0.0
DROP (3-shot)	0.81

Downloads last month: 863

Safetensors

Model size

68.5M params

Tensor type

F32

blueapple8259
/

TinyStories-Alpaca

Open LLM Leaderboard Evaluation Results

Dataset used to train blueapple8259/TinyStories-Alpaca