Instructions to use kurogane/multiscreen_154M_tinystorys_vocab768_instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kurogane/multiscreen_154M_tinystorys_vocab768_instruct with Transformers:

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

pipe = pipeline("text-generation", model="kurogane/multiscreen_154M_tinystorys_vocab768_instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("kurogane/multiscreen_154M_tinystorys_vocab768_instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kurogane/multiscreen_154M_tinystorys_vocab768_instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kurogane/multiscreen_154M_tinystorys_vocab768_instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kurogane/multiscreen_154M_tinystorys_vocab768_instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kurogane/multiscreen_154M_tinystorys_vocab768_instruct

SGLang

How to use kurogane/multiscreen_154M_tinystorys_vocab768_instruct 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 "kurogane/multiscreen_154M_tinystorys_vocab768_instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kurogane/multiscreen_154M_tinystorys_vocab768_instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "kurogane/multiscreen_154M_tinystorys_vocab768_instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kurogane/multiscreen_154M_tinystorys_vocab768_instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kurogane/multiscreen_154M_tinystorys_vocab768_instruct with Docker Model Runner:
```
docker model run hf.co/kurogane/multiscreen_154M_tinystorys_vocab768_instruct
```

Model Card for multiscreen_psi16_768

This model is an unofficial experimental pre-traind model of multiscreen with TinyStories datasets. It has been trained using TRL.

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "kurogane/multiscreen_154M_tinystorys_vocab768_instruct"
cache_dir = r"/media/kurogane/backup/cache"

model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    trust_remote_code=True,
    cache_dir=cache_dir,
    )
model.to("cuda:0")

tokenizer = AutoTokenizer.from_pretrained(
    model_id, 
    padding_side="left", 
    cache_dir=cache_dir,
    )

messages = [
    {"role": "user", "content": "Write a short story about a helpful robot."}
]

model_inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    return_tensors="pt",
    return_dict=True,
    add_generation_prompt=True,
).to(model.device)

generated_ids = model.generate(**model_inputs)

s_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(s_output)

result example

Once upon a time, there was a robot named John who had a pro

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.24.0
Transformers: 5.8.0
Pytorch: 2.11.0+cu129
Datasets: 4.3.0
Tokenizers: 0.22.2

Used archtechture

This model is an experimental tiny language model trained on TinyStories using a Multiscreen-style architecture inspired by the paper Screening Is Enough by Ken M. Nakanishi. This model implementation was developed as an experimental Hugging Face Transformers port, with reference to the unofficial PyTorch implementation dieOD/multiscreen-pytorch. This model is not an official implementation released by the author of the Multiscreen paper.