Instructions to use crumb/opentinystories-68m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use crumb/opentinystories-68m-base with Transformers:

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

pipe = pipeline("text-generation", model="crumb/opentinystories-68m-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("crumb/opentinystories-68m-base")
model = AutoModelForCausalLM.from_pretrained("crumb/opentinystories-68m-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use crumb/opentinystories-68m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "crumb/opentinystories-68m-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "crumb/opentinystories-68m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/crumb/opentinystories-68m-base

SGLang

How to use crumb/opentinystories-68m-base 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 "crumb/opentinystories-68m-base" \
    --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": "crumb/opentinystories-68m-base",
		"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 "crumb/opentinystories-68m-base" \
        --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": "crumb/opentinystories-68m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use crumb/opentinystories-68m-base with Docker Model Runner:
```
docker model run hf.co/crumb/opentinystories-68m-base
```

Tinystories-30m-UL2

GPT-4 generated model card

Model Details

Model Name: crumb/opentinystories-30m-base
Model Type: GPTNeoXForCausalLM
Model Training Details: The model is trained using crumb/flan-ul2-tinystories which contains around a quarter of a million examples generated from Flan-UL2 (20b) with the prompt "Write a short story using the vocabulary of a first-grader."

Model Description

This model is trained with the specific purpose of generating short narratives using a vocabulary limited to the level of a first-grader. In terms of complexity and language usage, the model is designed to produce simplistic and easily comprehensible text.

Learning from text generated by Flan-UL2 (20b), the model adopts a simple storyline layout and a minimalistic vocabulary, which it recognizes are easier to learn and replicate.

Training

The model is trained for four epochs on the crumb/flan-ul2-tinystories dataset (inspired by roneneldan/TinyStories), created with the help of Flan-UL2 (20b), as opposed to GPT-3.5/4 in the original Tinystories. The data is designed to follow the format of a simple, first-grader-level narrative, which aids the model in learning simple vocabulary and sentence structure.

Training arguments:

per_device_train_batch_size=8,
gradient_accumulation_steps=16,
warmup_steps=128,
num_train_epochs=4,
learning_rate=2e-4,
eval_steps=64,
optim="adamw_torch",

Usage

This model serves as a meaningful research tool in exploring the learning tendencies of smaller language models and their ability to grasp simplified language constructs. Its specific training set effectively maps the idea that a constrained vocabulary and simplistic story layouts are inherently easier to learn.

Validation and Performance

The model's performance was evaluated using a held-out validation set, which constitutes 1% of the original dataset. During evaluation, the model achieved a loss of N. During training, the model achieved a loss of N

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

BF16

BOOL

crumb
/

opentinystories-68m-base