Instructions to use ifx-pse-sys-ml/spark-13m-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ifx-pse-sys-ml/spark-13m-instruct with Transformers:

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

pipe = pipeline("text-generation", model="ifx-pse-sys-ml/spark-13m-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("ifx-pse-sys-ml/spark-13m-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ifx-pse-sys-ml/spark-13m-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ifx-pse-sys-ml/spark-13m-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": "ifx-pse-sys-ml/spark-13m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ifx-pse-sys-ml/spark-13m-instruct

SGLang

How to use ifx-pse-sys-ml/spark-13m-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 "ifx-pse-sys-ml/spark-13m-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": "ifx-pse-sys-ml/spark-13m-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 "ifx-pse-sys-ml/spark-13m-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": "ifx-pse-sys-ml/spark-13m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ifx-pse-sys-ml/spark-13m-instruct with Docker Model Runner:
```
docker model run hf.co/ifx-pse-sys-ml/spark-13m-instruct
```

spark-13m-instruct

A 13.2M-parameter English instruction-tuned model: spark-13m-base (pretrained SmolLM-style on FineWeb-Edu-dedup + Cosmopedia-v2) fine-tuned on SmolTalk. ~10× smaller than SmolLM-135M-Instruct.

Architecture: Llama-style decoder — hidden 384, 6 layers, 6 heads / 2 KV heads (GQA), intermediate 1216, RoPE, context 1024, vocab 6400 (English BPE).
Training: base → SmolTalk SFT (200k conversations, 5 epochs).

Usage (chat)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ifx-pse-sys-ml/spark-13m-instruct", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("ifx-pse-sys-ml/spark-13m-instruct")

messages = [{"role": "user", "content": "Tell me about the moon in one sentence."}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(ids, max_new_tokens=64, do_sample=True, temperature=0.7, top_p=0.9)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

The decoder also accepts inputs_embeds (exactly one of input_ids / inputs_embeds), so a vision projector can splice visual tokens in — usable as a small VLM text backbone. A raw PyTorch checkpoint (pytorch_model.pth) is included alongside the safetensors weights.

Benchmarks

Accuracy (%) via lm-evaluation-harness 0.4, same harness and shots for every model, so columns are directly comparable.

Benchmark	chance	spark-13m-instruct	SmolLM-135M-Instruct
hellaswag	25	27.9	41.9
arc_easy	25	34.3	43.9
arc_challenge	25	24.3	27.4
piqa	50	57.7	67.0
winogrande	50	50.4	51.3
openbookqa	25	24.8	33.6
commonsense_qa	20	21.0	20.3
mmlu	25	24.6	24.4
average	—	33.1	38.7

Honest limitations

At 13M parameters this is near random chance on knowledge/reasoning benchmarks; the gap to SmolLM-135M is capacity, not data or tuning. Instruction-tuning adds response format, not facts (academic benchmarks are ~flat vs the base). A research/prototyping instruct model and a lightweight decoder, not a knowledge model. English only. Trained with the Nexus codebase.

Downloads last month: 653

Safetensors

Model size

13.2M params

Tensor type

F32