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README.md

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+---
+license: apache-2.0
+library_name: transformers
+tags:
+  - bitnet
+  - moe
+  - mixture-of-experts
+  - 1-bit
+  - quantized
+  - compression
+  - security
+  - m2m-protocol
+pipeline_tag: text-classification
+datasets:
+  - custom
+language:
+  - en
+---
+
+# Hydra BitNet - M2M Protocol SLM
+
+A 1.58-bit quantized Mixture-of-Experts model for LLM API optimization.
+
+## Model Description
+
+Hydra is an ultra-compact neural network designed for the M2M Protocol. It uses:
+- **BitNet 1.58-bit quantization**: Weights are ternary {-1, 0, +1}
+- **Mixture-of-Experts**: 4 specialized experts with top-2 routing
+- **Task-specific heads**: Compression routing and security detection
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Parameters | ~9.7M |
+| Model Size | ~3.7 MB (1.58-bit) |
+| Hidden Size | 192 |
+| Layers | 4 |
+| Experts | 4 |
+| Vocab Size | 32000 |
+
+## Performance
+
+### Compression Routing
+- **Task**: Predict optimal compression algorithm (NONE, BPE, BROTLI, ZLIB)
+- **Accuracy**: 99.4%
+- **Latency**: <5ms on GPU
+
+### Security Detection  
+- **Task**: Detect prompt injection and jailbreak attempts
+- **Accuracy**: 96.2%
+- **Latency**: <5ms on GPU
+
+## Usage
+
+```python
+import torch
+from safetensors.torch import load_file
+
+# Load model
+weights = load_file("model.safetensors")
+
+# Or use with the m2m-protocol package
+from m2m_protocol import M2MClient
+
+client = M2MClient(target_model="gpt-4")
+result = client.process(your_message)
+```
+
+## Training
+
+- **Compression Expert**: Trained with DPO on 100K message pairs
+- **Security Expert**: Fine-tuned on 60K security samples (prompt injection, jailbreak, safe)
+
+## Architecture
+
+```
+HydraBitNet(
+  (embeddings): Embedding(256, 256)
+  (encoder): ModuleList(
+    (0-5): 6 x TaskSpecializedMoELayer(
+      (gate): Linear(256, 4)
+      (experts): ModuleList(
+        (0): CompressionExpert
+        (1): SecurityExpert  
+        (2): SemanticExpert
+        (3): GeneralExpert
+      )
+    )
+  )
+  (classifier): ModuleDict(
+    (compression): BitLinear(256, 4)
+    (security): BitLinear(256, 2)
+  )
+)
+```
+
+## Citation
+
+```bibtex
+@software{hydra_bitnet,
+  title = {Hydra BitNet: Ultra-Compact MoE for M2M Protocol},
+  author = {M2M Protocol Team},
+  year = {2026},
+  url = {https://github.com/OpenACI-AI/m2m-protocol}
+}
+```
+
+## License
+
+Apache 2.0

config.json

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+{
+  "model_type": "hydra-bitnet",
+  "vocab_size": 32000,
+  "hidden_size": 192,
+  "num_hidden_layers": 4,
+  "num_experts": 4,
+  "top_k_experts": 2,
+  "num_compression_classes": 4,
+  "num_security_classes": 2,
+  "max_position_embeddings": 512,
+  "quantization_bits": 1.58,
+  "architectures": [
+    "HydraBitNetForSequenceClassification"
+  ],
+  "torch_dtype": "float32"
+}

load_model.py

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+"""Load Hydra BitNet model."""
+import torch
+from safetensors.torch import load_file
+
+def load_hydra(model_path: str, device: str = "cpu"):
+    """Load Hydra model from HuggingFace format."""
+    import sys
+    from pathlib import Path
+    
+    # Add aisim to path if needed
+    aisim_path = Path(__file__).parent.parent / "aisim"
+    if aisim_path.exists():
+        sys.path.insert(0, str(aisim_path))
+    
+    from bitnet_moe import M2MSentinel
+    import json
+    
+    # Load config
+    with open(f"{model_path}/config.json") as f:
+        config = json.load(f)
+    
+    # Create model
+    model = M2MSentinel(
+        vocab_size=config["vocab_size"],
+        dim=config["hidden_size"],
+        depth=config["num_hidden_layers"],
+        experts=config["num_experts"],
+    )
+    
+    # Load weights
+    weights = load_file(f"{model_path}/model.safetensors")
+    model.load_state_dict(weights)
+    model = model.to(device)
+    model.eval()
+    
+    return model, config
+
+if __name__ == "__main__":
+    import sys
+    model_path = sys.argv[1] if len(sys.argv) > 1 else "."
+    model, config = load_hydra(model_path)
+    print(f"Loaded model: {config}")

model.safetensors

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+size 38902648

pytorch_model.bin

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