Instructions to use summerstars/summer-s1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use summerstars/summer-s1 with Transformers:

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

pipe = pipeline("text-generation", model="summerstars/summer-s1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("summerstars/summer-s1")
model = AutoModelForCausalLM.from_pretrained("summerstars/summer-s1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use summerstars/summer-s1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "summerstars/summer-s1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "summerstars/summer-s1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/summerstars/summer-s1

SGLang

How to use summerstars/summer-s1 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 "summerstars/summer-s1" \
    --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": "summerstars/summer-s1",
		"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 "summerstars/summer-s1" \
        --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": "summerstars/summer-s1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use summerstars/summer-s1 with Docker Model Runner:
```
docker model run hf.co/summerstars/summer-s1
```

license: apache-2.0

summer-S1

This repository contains a lightweight and fast model developed by Japanese high school students, based on MobileLLM-R1-950M-MLX.

Overview

This model improves specific computational layers to achieve:

Fast inference in low-resource environments
Memory-efficient sequential data processing
Efficient knowledge transfer from pretrained models

It is the result of a high school research project aiming to create a compact, pretrained language model.

Model Details

Base model: robbiemu/MobileLLM-R1-950M-MLX
Improved layers: Selected layers optimized for speed and memory efficiency
Model size: Hidden layer size × 2 (efficiency-focused)
Lightweight parameters: Knowledge transfer from pretrained models

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the high school project model
model_name = "summerstars/summer-S1"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Text generation
prompt = "This model was developed by high school students"
input_ids = tokenizer(prompt, return_tensors="pt").to(device)
output_ids = model.generate(input_ids.input_ids, max_length=100)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

Downloads last month: 1

Safetensors

Model size

0.9B params

Tensor type

F32