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README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-360M
+tags:
+- json
+- structured-output
+- edge-ai
+- iot
+- small-language-model
+- peft
+- lora
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Maaza SLM-360M-JSON v1.2
+
+**First sub-400M model with consistent complex schema wins on EdgeJSON 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 Benchmark (Normalized Scoring)
+
+| Metric | Score |
+|--------|-------|
+| **JSONExact** | 58.9% |
+| **Field F1** | 0.761 |
+| **Complex Schemas** | 4.0% |
+| **Avg Latency** | ~39ms/token |
+
+### By Complexity
+
+| Complexity | JSONExact | Field F1 |
+|------------|-----------|----------|
+| Simple (2-4 fields) | 88.2% | 0.961 |
+| Medium (4-8 fields) | 51.4% | 0.860 |
+| Complex (8+ fields) | 4.0% | 0.072 |
+
+### Version Comparison
+
+| Version | JSONExact | Complex | Notes |
+|---------|-----------|---------|-------|
+| v1.0 | 55.1% | 4.0% | Initial release |
+| v1.1 | 60.1% | 0.0% | Best simple/medium |
+| **v1.2** | **58.9%** | **4.0%** | Complex breakthrough |
+
+### vs Baselines
+
+| Model | Params | JSONExact | Complex |
+|-------|--------|-----------|---------|
+| SmolLM2-360M (base) | 360M | 11.4% | 0.0% |
+| Qwen2.5-3B | 3B | 6.0% | 0.0% |
+| **Maaza v1.2** | **360M** | **58.9%** | **4.0%** |
+
+## Quick Start
+
+```python
+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 (jane@example.com). 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
+
+```json
+{
+  "order_id": "12345",
+  "customer": {"name": "Jane Smith", "email": "jane@example.com"},
+  "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:
+
+```bash
+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](https://github.com/CycleCore-Technologies/slmbench)
+- [SLMBench Leaderboard](https://slmbench.com)
+- [v1.1 Model](https://huggingface.co/CycleCoreTechnologies/Maaza-SLM-360M-JSON-v1.1)
+- [Research Paper](https://github.com/CycleCore-Technologies/slmbench/tree/main/papers) (forthcoming)
+
+## Citation
+
+```bibtex
+@misc{cyclecore2025maaza,
+  title={Maaza SLM-360M-JSON: Sub-400M JSON Extraction},
+  author={CycleCore Technologies},
+  year={2025},
+  howpublished={\url{https://huggingface.co/CycleCoreTechnologies/Maaza-SLM-360M-JSON-v1.2}}
+}
+```
+
+## Contact
+
+- hi@cyclecore.ai
+- [@CycleCoreTech](https://x.com/CycleCoreTech)
+
+---
+
+Apache 2.0 | Copyright 2025 CycleCore Technologies

adapter_config.json

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+{
+  "alpha_pattern": {},
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+  "base_model_name_or_path": "HuggingFaceTB/SmolLM2-360M",
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+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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+}