Or maybe you were still paying attention to the Meta Llama 3 released last week, but today Microsoft did something different and released a new Phi-3 series of models. The first wave of releases on Hugging face is the Phi-3-mini version with a parameter size of 3.8B. Phi-3-mini can not only run on traditional computing devices, but also on edge devices such as mobile devices and IoT devices. The Phi-3-mini release covers the traditional Pytorch model format, the quantized version of the gguf format, and the onnx-based quantized version. This also brings convenience to developers in different application scenarios. The content of this blog hopes to allow different developers to explore different model formats released by Phi-3-mini combined with different technical frameworks, so that everyone can inference Phi-3-mini first.
Use Semantic Kernel to access Phi-3-mini
In the Copilot application, we create applications through Semantic Kernel / LangChain. This type of application framework is generally compatible with Azure OpenAI Service / OpenAI models, and can also support open source models on Hugging face and local models. What should we do if we want to use Semantic Kernel to access Phi-3-mini? Using .NET as an example, we can combine it with the Hugging face Connector in Semantic Kernel. By default, it can correspond to the model id on Hugging face (the first time you use it, the model will be downloaded from Hugging face, which takes a long time). You can also connect to the built local service. Compared with the two, I recommend using the latter because it has a higher degree of autonomy, especially in enterprise applications.
From the figure accessing local services through Semantic Kernel can easily connect to the self-built Phi-3-mini model server. Here is the running result.
More users prefer to use quantized models to run models locally. Through Ollama/LM Studio, individual users can call different quantized models at will. I will first show how to use Ollama to call the Phi-3-mini quantization model
You can directly run ollama run phi3 or configure it offline using the following.
Create a Modelfile
FROM {Add your gguf file path}
TEMPLATE """<|user|>
{{.Prompt}}<|end|>
<|assistant|>"""
PARAMETER stop <|end|>
PARAMETER num_ctx 4096
Running in terminal
ollama create phi3mini -f Modelfile
ollama run phi3mini
I have always been a supporter of cross-platform applications. If you want to use gguf in the cloud and edge devices at the same time, LlamaEdge can be your choice. LlamaEdge can be understood as WasmEdge (WasmEdge is a lightweight, high-performance, scalable WebAssembly runtime suitable for cloud native, edge and decentralized applications. It supports serverless applications, embedded functions, microservices, smart contracts and IoT devices. You can deploy gguf's quantitative model to edge devices and the cloud through LlamaEdge.
ONNX Runtime is an efficient runtime library for ONNX models. It supports multiple operating systems and hardware platforms, including CPU, GPU, etc. The key advantages of ONNX Runtime are its efficient performance and ease of deployment. Through ONNX Runtime, developers can easily deploy trained models to production environments without caring about the underlying inference framework. In the era of large models, ONNX Runtime has released an interface (Python/.NET/C/C++) based on generative AI. We can use this interface to call the Phi-3-mini model. Next we try to call the Phi-3-mini model through Python (when using ONNX Runtime Generative AI, you need to compile the environment first, please refer tohttps://github.com/microsoft/onnxruntime-genai/blob/main/examples/python/phi-3-tutorial.md)
The release of Phi-3-mini allows individuals and enterprises to deploy SLM on different hardware devices, especially mobile devices and industrial IoT devices that can complete simple intelligent tasks under limited computing power. Combining LLMs can open up a new era of Generative AI. Inference is only the first step. I hope you will continue to pay attention to the content of this series.
Where can I find the few lines of code in JavaScript or PHP to ask questions and recieve answers of PHI3 from a webpage. And PHI3 downloadable all setup and ready to integrate ( with API + KEY ) on my web hosting server.
I tried to install the requirements.txt to test the semantickernel but it didn't worked
This is the error that I get back:
ERROR: Cannot install -r requirements.txt (line 11), -r requirements.txt (line 30) and typing_extensions==4.6.1 because these package versions have conflicting dependencies.
The conflict is caused by: The user requested typing_extensions==4.6.1 huggingface-hub 0.14.1 depends on typing-extensions>=3.7.4.3 torch 2.2.1 depends on typing-extensions>=4.8.0
kinfey - thanks for this post. I am trying to implement on device running .net maui. It needs to be offline and run on Windows and Android. Can you point me in the right direction?
kinfey, your script above will start downloading 2 shards, like this: model.safetensors.index.json: 0%| | 0.00/16.3k [00:00<?, ?B/s] model.safetensors.index.json: 100%|██████████| 16.3k/16.3k [00:00<?, ?B/s] Downloading shards: 0%| | 0/2 [00:00<?, ?it/s] model-00001-of-00002.safetensors: 0%| | 0.00/4.97G [00:00<?, ?B/s][A model-00001-of-00002.safetensors: 0%| | 10.5M/4.97G [00:02<21:53, 3.78MB/s][A model-00001-of-00002.safetensors: 0%| | 21.0M/4.97G [00:05<20:04, 4.11MB/s][A model-00001-of-00002.safetensors: 1%| | 31.5M/4.97G [00:07<19:41, 4. But I already have downloaded both shards (model-00001-of-00002.safetensors, model-00002-of-00002.safetensors), as I described above. So, how to prompt the model locally?
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Getting started with Microsoft Phi-3-mini - Inference Phi-3-mini models, Discover how Phi-3-mini, a new series of models from Microsoft, enables deployment of Large Language Models (LLMs) on edge devices and IoT devices. Learn how to use Semantic Kernel, Ollama/LlamaEdge, and ONNX Runtime to access and infer phi3-mini models, and explore the possibilities of generative AI in various application scenarios
","body":"
\n
\n
Or maybe you were still paying attention to the Meta Llama 3 released last week, but today Microsoft did something different and released a new Phi-3 series of models. The first wave of releases on Hugging face is the Phi-3-mini version with a parameter size of 3.8B. Phi-3-mini can not only run on traditional computing devices, but also on edge devices such as mobile devices and IoT devices. The Phi-3-mini release covers the traditional Pytorch model format, the quantized version of the gguf format, and the onnx-based quantized version. This also brings convenience to developers in different application scenarios. The content of this blog hopes to allow different developers to explore different model formats released by Phi-3-mini combined with different technical frameworks, so that everyone can inference Phi-3-mini first.
\n
Use Semantic Kernel to access Phi-3-mini
\n
In the Copilot application, we create applications through Semantic Kernel / LangChain. This type of application framework is generally compatible with Azure OpenAI Service / OpenAI models, and can also support open source models on Hugging face and local models. What should we do if we want to use Semantic Kernel to access Phi-3-mini? Using .NET as an example, we can combine it with the Hugging face Connector in Semantic Kernel. By default, it can correspond to the model id on Hugging face (the first time you use it, the model will be downloaded from Hugging face, which takes a long time). You can also connect to the built local service. Compared with the two, I recommend using the latter because it has a higher degree of autonomy, especially in enterprise applications.
\n
\n
\n
\n
From the figure accessing local services through Semantic Kernel can easily connect to the self-built Phi-3-mini model server. Here is the running result.
More users prefer to use quantized models to run models locally. Through Ollama/LM Studio, individual users can call different quantized models at will. I will first show how to use Ollama to call the Phi-3-mini quantization model
\n
\n
You can directly run ollama run phi3 or configure it offline using the following.
I have always been a supporter of cross-platform applications. If you want to use gguf in the cloud and edge devices at the same time, LlamaEdge can be your choice. LlamaEdge can be understood as WasmEdge (WasmEdge is a lightweight, high-performance, scalable WebAssembly runtime suitable for cloud native, edge and decentralized applications. It supports serverless applications, embedded functions, microservices, smart contracts and IoT devices. You can deploy gguf's quantitative model to edge devices and the cloud through LlamaEdge.
ONNX Runtime is an efficient runtime library for ONNX models. It supports multiple operating systems and hardware platforms, including CPU, GPU, etc. The key advantages of ONNX Runtime are its efficient performance and ease of deployment. Through ONNX Runtime, developers can easily deploy trained models to production environments without caring about the underlying inference framework. In the era of large models, ONNX Runtime has released an interface (Python/.NET/C/C++) based on generative AI. We can use this interface to call the Phi-3-mini model. Next we try to call the Phi-3-mini model through Python (when using ONNX Runtime Generative AI, you need to compile the environment first, please refer tohttps://github.com/microsoft/onnxruntime-genai/blob/main/examples/python/phi-3-tutorial.md)
The release of Phi-3-mini allows individuals and enterprises to deploy SLM on different hardware devices, especially mobile devices and industrial IoT devices that can complete simple intelligent tasks under limited computing power. Combining LLMs can open up a new era of Generative AI. Inference is only the first step. I hope you will continue to pay attention to the content of this series.
Or maybe you were still paying attention to the Meta Llama 3 released last week, but today Microsoft did something different and released a new Phi-3 series of models. The first wave of releases on Hugging face is the Phi-3-mini version with a parameter size of 3.8B. Phi-3-mini can not only run on traditional computing devices, but also on edge devices such as mobile devices and IoT devices. The Phi-3-mini release covers the traditional Pytorch model format, the quantized version of the gguf format, and the onnx-based quantized version. This also brings convenience to developers in different application scenarios. The content of this blog hopes to allow different developers to explore different model formats released by Phi-3-mini combined with different technical frameworks, so that everyone can inference Phi-3-mini first.
\n
Use Semantic Kernel to access Phi-3-mini
\n
In the Copilot application, we create applications through Semantic Kernel / LangChain. This type of application framework is generally compatible with Azure OpenAI Service / OpenAI models, and can also support open source models on Hugging face and local models. What should we do if we want to use Semantic Kernel to access Phi-3-mini? Using .NET as an example, we can combine it with the Hugging face Connector in Semantic Kernel. By default, it can correspond to the model id on Hugging face (the first time you use it, the model will be downloaded from Hugging face, which takes a long time). You can also connect to the built local service. Compared with the two, I recommend using the latter because it has a higher degree of autonomy, especially in enterprise applications.
\n
\n
\n
\n
From the figure accessing local services through Semantic Kernel can easily connect to the self-built Phi-3-mini model server. Here is the running result.
More users prefer to use quantized models to run models locally. Through Ollama/LM Studio, individual users can call different quantized models at will. I will first show how to use Ollama to call the Phi-3-mini quantization model
\n
\n
You can directly run ollama run phi3 or configure it offline using the following.
I have always been a supporter of cross-platform applications. If you want to use gguf in the cloud and edge devices at the same time, LlamaEdge can be your choice. LlamaEdge can be understood as WasmEdge (WasmEdge is a lightweight, high-performance, scalable WebAssembly runtime suitable for cloud native, edge and decentralized applications. It supports serverless applications, embedded functions, microservices, smart contracts and IoT devices. You can deploy gguf's quantitative model to edge devices and the cloud through LlamaEdge.
ONNX Runtime is an efficient runtime library for ONNX models. It supports multiple operating systems and hardware platforms, including CPU, GPU, etc. The key advantages of ONNX Runtime are its efficient performance and ease of deployment. Through ONNX Runtime, developers can easily deploy trained models to production environments without caring about the underlying inference framework. In the era of large models, ONNX Runtime has released an interface (Python/.NET/C/C++) based on generative AI. We can use this interface to call the Phi-3-mini model. Next we try to call the Phi-3-mini model through Python (when using ONNX Runtime Generative AI, you need to compile the environment first, please refer tohttps://github.com/microsoft/onnxruntime-genai/blob/main/examples/python/phi-3-tutorial.md)
The release of Phi-3-mini allows individuals and enterprises to deploy SLM on different hardware devices, especially mobile devices and industrial IoT devices that can complete simple intelligent tasks under limited computing power. Combining LLMs can open up a new era of Generative AI. Inference is only the first step. I hope you will continue to pay attention to the content of this series.
Getting started with Microsoft Phi-3-mini - Inference Phi-3-mini models, Discover how Phi-3-mini, a new series of models from Microsoft, enables deployment of Large Language Models (LLMs) on edge devices and IoT devices. Learn how to use Semantic Kernel, Ollama/LlamaEdge, and ONNX Runtime to access and infer phi3-mini models, and explore the possibilities of generative AI in various application scenarios
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Getting Started - Generative AI with Phi-3-mini: A Guide to Inference and Deployment","moderationData":{"__ref":"ModerationData:moderation_data:4130883"},"body":"
Where can I find the few lines of code in JavaScript or PHP to ask questions and recieve answers of PHI3 from a webpage. And PHI3 downloadable all setup and ready to integrate ( with API + KEY ) on my web hosting server.
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kinfey I had met same issue on downloading shards and safetensors package.
Could you tell me how to git clone phi3 to resolve the download issue? I am new on git.
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I tried to install the requirements.txt to test the semantickernel but it didn't worked
This is the error that I get back:
ERROR: Cannot install -r requirements.txt (line 11), -r requirements.txt (line 30) and typing_extensions==4.6.1 because these package versions have conflicting dependencies.
The conflict is caused by: The user requested typing_extensions==4.6.1 huggingface-hub 0.14.1 depends on typing-extensions>=3.7.4.3 torch 2.2.1 depends on typing-extensions>=4.8.0
How I can fix it?
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but the final command say: unknown option nn-preload. What am I missing here?
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solved, thx
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TimB2024 this time, I use MLC framework to run phi 3 in iPhone only, if you want to run with .NET MAUI, I think you can choose onnxruntime to try
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please confirm to run bash above firstly. Gabriel_Giani
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kinfey - thanks for this post. I am trying to implement on device running .net maui. It needs to be offline and run on Windows and Android. Can you point me in the right direction?
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kinfey, your script above will start downloading 2 shards, like this: model.safetensors.index.json: 0%| | 0.00/16.3k [00:00<?, ?B/s] model.safetensors.index.json: 100%|██████████| 16.3k/16.3k [00:00<?, ?B/s] Downloading shards: 0%| | 0/2 [00:00<?, ?it/s] model-00001-of-00002.safetensors: 0%| | 0.00/4.97G [00:00<?, ?B/s][A model-00001-of-00002.safetensors: 0%| | 10.5M/4.97G [00:02<21:53, 3.78MB/s][A model-00001-of-00002.safetensors: 0%| | 21.0M/4.97G [00:05<20:04, 4.11MB/s][A model-00001-of-00002.safetensors: 1%| | 31.5M/4.97G [00:07<19:41, 4. But I already have downloaded both shards (model-00001-of-00002.safetensors, model-00002-of-00002.safetensors), as I described above. So, how to prompt the model locally?
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