Instructions to use roneneldan/TinyStories-Instruct-33M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use roneneldan/TinyStories-Instruct-33M with Transformers:

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

pipe = pipeline("text-generation", model="roneneldan/TinyStories-Instruct-33M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("roneneldan/TinyStories-Instruct-33M")
model = AutoModelForCausalLM.from_pretrained("roneneldan/TinyStories-Instruct-33M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use roneneldan/TinyStories-Instruct-33M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roneneldan/TinyStories-Instruct-33M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roneneldan/TinyStories-Instruct-33M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/roneneldan/TinyStories-Instruct-33M

SGLang

How to use roneneldan/TinyStories-Instruct-33M 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 "roneneldan/TinyStories-Instruct-33M" \
    --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": "roneneldan/TinyStories-Instruct-33M",
		"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 "roneneldan/TinyStories-Instruct-33M" \
        --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": "roneneldan/TinyStories-Instruct-33M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use roneneldan/TinyStories-Instruct-33M with Docker Model Runner:
```
docker model run hf.co/roneneldan/TinyStories-Instruct-33M
```

Model training schedule and parameters

by AntonioMartini - opened Aug 8, 2023

Discussion

AntonioMartini

Aug 8, 2023

Hello,

Really nice model! is there any information on the employed training schedule and any additional training parameters that would help to replicate the results?

Thanks,
Antonio

roneneldan

Owner Aug 8, 2023

Hi... thanks!

Here are the hyperparameters:
lr = 5e-4
lr_schedule = constant
wd=0.1
adam_beta1=0.9, adam_beta2 = 0.95
context length=512
batch size=80
gradient accumulation steps=16

I think that's about it...

AntonioMartini

Aug 8, 2023

that's very helpful thanks!

AntonioMartini

Aug 9, 2023

This comment has been hidden

AntonioMartini changed discussion status to closed Aug 12, 2023

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