Instructions to use gatilin/sparklm-31m-tinystories with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gatilin/sparklm-31m-tinystories with Transformers:

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

pipe = pipeline("text-generation", model="gatilin/sparklm-31m-tinystories", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gatilin/sparklm-31m-tinystories", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gatilin/sparklm-31m-tinystories with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gatilin/sparklm-31m-tinystories"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gatilin/sparklm-31m-tinystories",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gatilin/sparklm-31m-tinystories

SGLang

How to use gatilin/sparklm-31m-tinystories 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 "gatilin/sparklm-31m-tinystories" \
    --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": "gatilin/sparklm-31m-tinystories",
		"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 "gatilin/sparklm-31m-tinystories" \
        --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": "gatilin/sparklm-31m-tinystories",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gatilin/sparklm-31m-tinystories with Docker Model Runner:
```
docker model run hf.co/gatilin/sparklm-31m-tinystories
```

SparkLM-31M-TinyStories

This is a SparkLM checkpoint exported into a HuggingFace-compatible directory. It is intended to load through the Transformers remote-code path.

Architecture

Model type: SparkLMForCausalLM
Parameters: 30,814,720
Hidden size: 512
Layers: 8
Attention heads: 8
KV heads: 2
MLA: True
MoE: False
MTP: False
Q/K Norm: False
Tokenizer source: results/mps_31m_tinystories_final

Checkpoint Metadata

Step: 199
Loss: 1.921442563533783

Training loss is not a benchmark score. Publish evaluation results separately before comparing this checkpoint with other public models.

Intended Use

This release is suitable for reproducibility checks, loading smoke tests, and lightweight text-generation experiments. It is not a safety-aligned assistant release unless a downstream alignment and evaluation report is attached.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "gatilin/sparklm-31m-tinystories"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
inputs = tokenizer("Hello SparkLM", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=32)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

The exported remote code wraps SparkLM's native PyTorch implementation in a transformers.PreTrainedModel interface.

Release Contract

A public release is considered loadable when the repository root includes:

config.json
generation_config.json
tokenizer_config.json
tokenizer vocabulary artifacts
model.safetensors or pytorch_model.bin
configuration_sparklm.py
modeling_sparklm.py
tokenization_sparklm.py
release_manifest.json

The config and tokenizer config should expose auto_map entries so AutoConfig, AutoTokenizer, and AutoModelForCausalLM can load with trust_remote_code=True.

Expert Parallel Training

This model supports end-to-end expert-parallel training via a differentiable all-to-all dispatch/combine primitive (_AllToAllSingle autograd.Function). The backward pass through the EP all-to-all is a reverse all-to-all with swapped split sizes, enabling gradient flow across EP ranks.

Files

The exporter writes model.safetensors when the safetensors package is available, falling back to pytorch_model.bin in minimal environments.

Release Validation

Run this before uploading or after cloning the repository:

python -m sparklm.cli.release.validate_hf_release gatilin/sparklm-31m-tinystories --load --generate

Launch a local inference UI against the same release:

python -m sparklm.cli.inference_webui --model gatilin/sparklm-31m-tinystories

Downloads last month: 167

Safetensors

Model size

34.1M params

Tensor type

F32