marcusmi4n/phi-3.5-mini-instruct-quantized-qnn-single-file

By marcusmi4n · ↓ 2 · ♥ 0 · Updated 2025-09-09

What's in this repo

This page lists the files in marcusmi4n/phi-3.5-mini-instruct-quantized-qnn-single-file, including model.onnx.

1 ONNX models (.onnx files)
207 files, including 66 .weight, 6 .json, 1 .md, 1 .onnx, 1 .py
Last updated 2025-09-09

ONNX files

File	Size	Download
model.onnx	—	Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

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

pip install onnxruntime transformers numpy

Basic Usage

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

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

#!/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

All Files Required: Keep all files in the same directory. The model.onnx file references external data files.
Memory Requirements: Ensure you have at least 4GB of available RAM.
Qualcomm NPU Setup: For optimal performance on Qualcomm hardware:

# Use QNN execution provider (when available)
providers = ['QNNExecutionProvider', 'CPUExecutionProvider']
session = ort.InferenceSession("model.onnx", providers=providers)

🚀 Deployment on Qualcomm Devices

Windows on ARM

Copy all files to your device
Install ONNX Runtime: pip install onnxruntime
Run the test script to verify

Android (with QNN SDK)

Use ONNX Runtime Mobile with QNN support
Package all files in your app bundle
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:

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 info

onnx phi3 custom_code 8-bit onnx_dynamic_int8 region:us

↓ 2 ♥ 0

Created: 2025-09-09

Updated: 2025-09-09

View on Hugging Face

Files (207)

.gitattributes

README.md

_model_model_Constant_2_attr__value

_model_model_Constant_attr__value

added_tokens.json

config.json

model.model.embed_tokens.weight

model.model.layers.0.input_layernorm.weight

model.model.layers.0.post_attention_layernorm.weight

model.model.layers.1.input_layernorm.weight

model.model.layers.1.post_attention_layernorm.weight

model.model.layers.10.input_layernorm.weight

model.model.layers.10.post_attention_layernorm.weight

model.model.layers.11.input_layernorm.weight

model.model.layers.11.post_attention_layernorm.weight

model.model.layers.12.input_layernorm.weight

model.model.layers.12.post_attention_layernorm.weight

model.model.layers.13.input_layernorm.weight

model.model.layers.13.post_attention_layernorm.weight

model.model.layers.14.input_layernorm.weight

model.model.layers.14.post_attention_layernorm.weight

model.model.layers.15.input_layernorm.weight

model.model.layers.15.post_attention_layernorm.weight

model.model.layers.16.input_layernorm.weight

model.model.layers.16.post_attention_layernorm.weight

model.model.layers.17.input_layernorm.weight

model.model.layers.17.post_attention_layernorm.weight

model.model.layers.18.input_layernorm.weight

model.model.layers.18.post_attention_layernorm.weight

model.model.layers.19.input_layernorm.weight

model.model.layers.19.post_attention_layernorm.weight

model.model.layers.2.input_layernorm.weight

model.model.layers.2.post_attention_layernorm.weight

model.model.layers.20.input_layernorm.weight

model.model.layers.20.post_attention_layernorm.weight

model.model.layers.21.input_layernorm.weight

model.model.layers.21.post_attention_layernorm.weight

model.model.layers.22.input_layernorm.weight

model.model.layers.22.post_attention_layernorm.weight

model.model.layers.23.input_layernorm.weight

model.model.layers.23.post_attention_layernorm.weight

model.model.layers.24.input_layernorm.weight

model.model.layers.24.post_attention_layernorm.weight

model.model.layers.25.input_layernorm.weight

model.model.layers.25.post_attention_layernorm.weight

model.model.layers.26.input_layernorm.weight

model.model.layers.26.post_attention_layernorm.weight

model.model.layers.27.input_layernorm.weight

model.model.layers.27.post_attention_layernorm.weight

model.model.layers.28.input_layernorm.weight

model.model.layers.28.post_attention_layernorm.weight

model.model.layers.29.input_layernorm.weight

model.model.layers.29.post_attention_layernorm.weight

model.model.layers.3.input_layernorm.weight

model.model.layers.3.post_attention_layernorm.weight

model.model.layers.30.input_layernorm.weight

model.model.layers.30.post_attention_layernorm.weight

model.model.layers.31.input_layernorm.weight

model.model.layers.31.post_attention_layernorm.weight

model.model.layers.4.input_layernorm.weight

model.model.layers.4.post_attention_layernorm.weight

model.model.layers.5.input_layernorm.weight

model.model.layers.5.post_attention_layernorm.weight

model.model.layers.6.input_layernorm.weight

model.model.layers.6.post_attention_layernorm.weight

model.model.layers.7.input_layernorm.weight

model.model.layers.7.post_attention_layernorm.weight

model.model.layers.8.input_layernorm.weight

model.model.layers.8.post_attention_layernorm.weight

model.model.layers.9.input_layernorm.weight

model.model.layers.9.post_attention_layernorm.weight

model.model.norm.weight

model.onnx ONNX

model_info.json

onnx__MatMul_6060

onnx__MatMul_6117

onnx__MatMul_6118

onnx__MatMul_6119

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onnx__MatMul_7598

onnx__MatMul_7599

onnx__MatMul_7647

onnx__MatMul_7648

onnx__MatMul_7649

onnx__MatMul_7650

onnx__MatMul_7698

onnx__MatMul_7699

onnx__MatMul_7700

onnx__MatMul_7701

special_tokens_map.json

test_model.py

tokenizer.json

tokenizer_config.json

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