Instructions to use razor5050/Moonlit-SummaryStories-45M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use razor5050/Moonlit-SummaryStories-45M with Transformers:

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

pipe = pipeline("text-generation", model="razor5050/Moonlit-SummaryStories-45M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("razor5050/Moonlit-SummaryStories-45M")
model = AutoModelForCausalLM.from_pretrained("razor5050/Moonlit-SummaryStories-45M")

llama-cpp-python

How to use razor5050/Moonlit-SummaryStories-45M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="razor5050/Moonlit-SummaryStories-45M",
	filename="gguf/moonlit-summarystories-45m-Q5_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use razor5050/Moonlit-SummaryStories-45M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf razor5050/Moonlit-SummaryStories-45M:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf razor5050/Moonlit-SummaryStories-45M:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf razor5050/Moonlit-SummaryStories-45M:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf razor5050/Moonlit-SummaryStories-45M:Q5_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf razor5050/Moonlit-SummaryStories-45M:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf razor5050/Moonlit-SummaryStories-45M:Q5_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf razor5050/Moonlit-SummaryStories-45M:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf razor5050/Moonlit-SummaryStories-45M:Q5_K_M

Use Docker

docker model run hf.co/razor5050/Moonlit-SummaryStories-45M:Q5_K_M

LM Studio
Jan

vLLM

How to use razor5050/Moonlit-SummaryStories-45M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "razor5050/Moonlit-SummaryStories-45M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razor5050/Moonlit-SummaryStories-45M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/razor5050/Moonlit-SummaryStories-45M:Q5_K_M

SGLang

How to use razor5050/Moonlit-SummaryStories-45M 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 "razor5050/Moonlit-SummaryStories-45M" \
    --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": "razor5050/Moonlit-SummaryStories-45M",
		"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 "razor5050/Moonlit-SummaryStories-45M" \
        --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": "razor5050/Moonlit-SummaryStories-45M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use razor5050/Moonlit-SummaryStories-45M with Ollama:
```
ollama run hf.co/razor5050/Moonlit-SummaryStories-45M:Q5_K_M
```

Unsloth Studio

How to use razor5050/Moonlit-SummaryStories-45M with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for razor5050/Moonlit-SummaryStories-45M to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for razor5050/Moonlit-SummaryStories-45M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for razor5050/Moonlit-SummaryStories-45M to start chatting

Docker Model Runner
How to use razor5050/Moonlit-SummaryStories-45M with Docker Model Runner:
```
docker model run hf.co/razor5050/Moonlit-SummaryStories-45M:Q5_K_M
```

Lemonade

How to use razor5050/Moonlit-SummaryStories-45M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull razor5050/Moonlit-SummaryStories-45M:Q5_K_M

Run and chat with the model

lemonade run user.Moonlit-SummaryStories-45M-Q5_K_M

List all available models

lemonade list

Moonlit-SummaryStories-45M

Moonlit-SummaryStories-45M is a 45M-parameter TinyStories model specialized for Summary → Story generation. It starts from the pretrained checkpoint of razor5050/TinyStories-45M and is then supervised fine-tuned to take a short summary prompt and generate a complete TinyStories-style story.

What this model does

Input format:

Summary: A little fox is afraid of the dark until a glowing jar helps him find his way home.
Story:

The model continues with a full short story.

Model details

Architecture: LLaMA-style decoder-only transformer
Parameters: 45.46M
Hidden size: 512
Layers: 13
Attention heads: 8
KV heads (GQA): 4
Intermediate size: 1344
Vocabulary size: 16384
Context length: 512
Tokenizer: SentencePiece unigram

Training recipe

Pretraining base

Base model: razor5050/TinyStories-45M
Original pretraining dataset: roneneldan/TinyStories
Original pretraining epochs: 3

Fine-tuning task

Dataset source: roneneldan/TinyStoriesInstruct
Finetuning format: Summary: ... Story: → full story
Loss masking: prompt masked, loss only on story tokens
No truncation policy: only samples that fully fit 512 total tokens were kept
Usable SFT examples: 1702072

Exact usable dataset size under 512-token no-truncation rule

Train: 1685116
Validation: 16956
Total: 1702072

Fine-tuning hyperparameters

Epochs: 1
Effective batch size: 64
Micro-batch size: 8
Learning rate: 8e-5
Scheduler: cosine decay
Precision: FP16
Max sequence length: 512

Evaluation

Validation loss: 1.238696612096066
Perplexity: 3.4511122703552246
Example generations: see evaluation/40_prompts.json
Evaluation report: see evaluation/report.md

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "razor5050/Moonlit-SummaryStories-45M"
model = AutoModelForCausalLM.from_pretrained(repo_id)
tokenizer = AutoTokenizer.from_pretrained(repo_id)

prompt = "Summary: A shy rabbit learns to sing with the help of fireflies.
Story:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=220,
    do_sample=True,
    temperature=0.8,
    top_p=0.95,
    top_k=50,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Files in this repo

Root: final finetuned model
checkpoints/base_pretrain_final/: pretrained base checkpoint used for finetuning
checkpoints/sft/: intermediate SFT checkpoints and final SFT export
evaluation/: metrics, prompt generations, and report

Hardware

Training GPU: NVIDIA RTX 3060 12GB
Intended deployment class: small creative story model

Notes

This model is optimized for TinyStories-style English story generation from a short summary prompt. Because the model context window is 512 tokens total, longer prompts reduce the available generation budget.

Generated on 2026-05-23 11:04:08

Downloads last month: 1,106

Safetensors

Model size

45.5M params

Tensor type

F32

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