Maaza-SLM-360M-JSON-v1.2

Maaza SLM-360M-JSON v1.2

First SLM with complex schema wins on EdgeJSON v3.1 normalized benchmark.

A 360M parameter model fine-tuned for high-accuracy JSON extraction. v1.2 introduces extended context (2048 tokens) for improved complex schema handling.

Performance

EdgeJSON v3.1 Benchmark (Normalized Scoring)

By Complexity

Version Comparison

Note: v1.1+ use EdgeJSON v3.1 normalized scoring (case-insensitive keys). This is stricter on simple/medium schemas but fairer overall. v1.2 is the first SLM to solve complex schemas under the standardized benchmark.

vs Baselines

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load model
base = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceTB/SmolLM2-360M",
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base, "CycleCoreTechnologies/Maaza-SLM-360M-JSON-v1.2")
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-360M")

# Inference
prompt = """Extract the structured JSON data from the following text. Use snake_case for all keys.

Input: Order #12345 from Jane Smith ([email protected]). Items: Widget x2 ($19.99), Gadget ($49.99). Ship to 123 Main St, Springfield IL 62701. Total $89.97.

Output:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("Output:")[-1])

Expected Output

{
  "order_id": "12345",
  "customer": {"name": "Jane Smith", "email": "[email protected]"},
  "items": [
    {"name": "Widget", "quantity": 2, "price": 19.99},
    {"name": "Gadget", "quantity": 1, "price": 49.99}
  ],
  "shipping": {"street": "123 Main St", "city": "Springfield", "state": "IL", "zip": "62701"},
  "total": 89.97
}

Evaluation

Run the EdgeJSON benchmark:

git clone https://github.com/CycleCore-Technologies/slmbench
cd slmbench
pip install -r requirements.txt

python benchmarks/edge_json/scripts/eval.py \
  --model HuggingFaceTB/SmolLM2-360M \
  --adapter CycleCoreTechnologies/Maaza-SLM-360M-JSON-v1.2 \
  --dataset benchmarks/edge_json/data/edgejson_test_v3.jsonl \
  --device cuda

Model Details

• Base: SmolLM2-360M
• Method: LoRA fine-tuning
• Context: 2048 tokens (extended in v1.2)
• License: Apache 2.0

Use Cases

• Edge device JSON extraction
• API response parsing
• Document structure extraction
• IoT data normalization

Limitations

• Complex schemas (8+ fields, deep nesting) remain challenging
• Best suited for simple/medium complexity extraction
• v1.1 recommended if complex schemas not needed (higher overall accuracy)

Links

• EdgeJSON Benchmark
• SLMBench Leaderboard
• v1.1 Model
• Research Paper (forthcoming)

Citation

@misc{cyclecore2025maaza,
  title={Maaza SLM-360M-JSON: Small Language Model for JSON Extraction},
  author={CycleCore Technologies},
  year={2025},
  howpublished={\url{https://huggingface.co/CycleCoreTechnologies/Maaza-SLM-360M-JSON-v1.2}}
}

Contact

• [email protected]
• @CycleCoreTech

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Apache 2.0 | Copyright 2025 CycleCore Technologies