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README.md

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+# Phi-3.5 Mini Instruct - Quantized ONNX Model (Consolidated)
+
+## 🚀 Model Overview
+This is Microsoft's Phi-3.5-mini-instruct model, quantized to INT8 and optimized for Qualcomm Snapdragon NPU deployment. This version consolidates all files into a single directory for easier deployment.
+
+## 📊 Model Specifications
+- **Base Model**: microsoft/Phi-3.5-mini-instruct
+- **Size**: 7292.4 MB (quantized from 7.3GB original)
+- **Compression**: 50% size reduction
+- **Format**: ONNX INT8 quantized with external data
+- **Files**: 203 files total
+- **Target**: Qualcomm Snapdragon NPUs
+
+## 🔧 Quick Start
+
+### Installation
+```bash
+pip install onnxruntime transformers numpy
+```
+
+### Basic Usage
+```python
+import onnxruntime as ort
+from transformers import AutoTokenizer
+import numpy as np
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(".", trust_remote_code=True)
+
+# Load ONNX model
+session = ort.InferenceSession("model.onnx")
+
+# Prepare input
+text = "Hello, what is artificial intelligence?"
+inputs = tokenizer(text, return_tensors="np", max_length=64, truncation=True, padding="max_length")
+
+# Run inference
+outputs = session.run(None, {"input_ids": inputs["input_ids"]})
+logits = outputs[0]
+
+print(f"Input: {text}")
+print(f"Output shape: {logits.shape}")
+```
+
+### Text Generation Example
+```python
+def generate_response(prompt, max_new_tokens=50):
+    # Tokenize
+    inputs = tokenizer(prompt, return_tensors="np", max_length=64, truncation=True)
+    input_ids = inputs["input_ids"]
+    
+    generated_tokens = []
+    
+    for _ in range(max_new_tokens):
+        # Get model prediction
+        outputs = session.run(None, {"input_ids": input_ids})
+        logits = outputs[0]
+        
+        # Get next token (greedy)
+        next_token_id = np.argmax(logits[0, -1, :])
+        generated_tokens.append(next_token_id)
+        
+        # Stop on EOS
+        if next_token_id == tokenizer.eos_token_id:
+            break
+        
+        # Add to input for next iteration
+        input_ids = np.concatenate([input_ids, [[next_token_id]]], axis=1)
+    
+    # Decode response
+    response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+    return response
+
+# Example
+response = generate_response("What is machine learning?")
+print(f"Response: {response}")
+```
+
+## 🧪 Testing Script
+```python
+#!/usr/bin/env python3
+import onnxruntime as ort
+from transformers import AutoTokenizer
+import numpy as np
+
+def test_model():
+    print("🔄 Loading model...")
+    tokenizer = AutoTokenizer.from_pretrained(".", trust_remote_code=True)
+    session = ort.InferenceSession("model.onnx")
+    
+    test_cases = [
+        "Hello, how are you?",
+        "What is the capital of France?",
+        "Explain artificial intelligence in simple terms."
+    ]
+    
+    for i, text in enumerate(test_cases, 1):
+        print(f"\n{i}. Input: {text}")
+        
+        inputs = tokenizer(text, return_tensors="np", max_length=64, 
+                          truncation=True, padding="max_length")
+        outputs = session.run(None, {"input_ids": inputs["input_ids"]})
+        
+        print(f"   ✅ Output shape: {outputs[0].shape}")
+    
+    print("\n🎉 All tests passed!")
+
+if __name__ == "__main__":
+    test_model()
+```
+
+## ⚡ Performance Expectations
+- **Inference Speed**: 2-3x faster than CPU on Snapdragon NPUs
+- **Memory Usage**: ~4GB RAM required
+- **Tokens/Second**: 8-15 on Snapdragon 8cx Gen 2
+- **Latency**: <100ms for short sequences
+
+## 📁 File Structure
+```
+model.onnx              # Main ONNX model file
+tokenizer.json          # Tokenizer vocabulary
+tokenizer_config.json   # Tokenizer configuration
+config.json             # Model configuration
+onnx__MatMul_*         # External weight data files (129 files)
+*.weight               # Additional model weights
+```
+
+## ⚠️ Important Notes
+
+1. **All Files Required**: Keep all files in the same directory. The model.onnx file references external data files.
+
+2. **Memory Requirements**: Ensure you have at least 4GB of available RAM.
+
+3. **Qualcomm NPU Setup**: For optimal performance on Qualcomm hardware:
+```python
+# Use QNN execution provider (when available)
+providers = ['QNNExecutionProvider', 'CPUExecutionProvider']
+session = ort.InferenceSession("model.onnx", providers=providers)
+```
+
+## 🚀 Deployment on Qualcomm Devices
+
+### Windows on ARM
+1. Copy all files to your device
+2. Install ONNX Runtime: `pip install onnxruntime`
+3. Run the test script to verify
+
+### Android (with QNN SDK)
+1. Use ONNX Runtime Mobile with QNN support
+2. Package all files in your app bundle
+3. Initialize with QNN execution provider
+
+## 🐛 Troubleshooting
+
+**Model fails to load:**
+- Ensure all files are in the same directory
+- Check that you have sufficient RAM (4GB+)
+
+**Slow inference:**
+- Try enabling graph optimizations:
+```python
+sess_options = ort.SessionOptions()
+sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+session = ort.InferenceSession("model.onnx", sess_options)
+```
+
+**Out of memory:**
+- Reduce sequence length: `max_length=32`
+- Process smaller batches
+
+## 📄 License
+This model inherits the license from microsoft/Phi-3.5-mini-instruct.
+
+---
+*Quantized and optimized for Qualcomm Snapdragon NPU deployment*

_model_model_Constant_2_attr__value

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+

added_tokens.json

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+  "<|endoftext|>": 32000,
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+}

config.json

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+{
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
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+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "model_type": "phi3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": 32000,
+  "quantization_config": {
+    "bits": 8,
+    "quant_method": "onnx_dynamic_int8"
+  },
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
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+    "type": "longrope"
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+  "rope_theta": 10000.0,
+  "sliding_window": 262144,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.56.0",
+  "use_cache": true,
+  "vocab_size": 32064
+}

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