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feat(aio): add intel profile (#1901)
* feat(aio): add intel profile * docs: clarify AIO images features
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16 changed files with 178 additions and 23 deletions
12
aio/intel/embeddings.yaml
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12
aio/intel/embeddings.yaml
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name: text-embedding-ada-002
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backend: sentencetransformers
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parameters:
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model: all-MiniLM-L6-v2
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usage: |
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You can test this model with curl like this:
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curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
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"input": "Your text string goes here",
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"model": "text-embedding-ada-002"
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}'
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20
aio/intel/image-gen.yaml
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aio/intel/image-gen.yaml
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name: stablediffusion
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parameters:
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model: runwayml/stable-diffusion-v1-5
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backend: diffusers
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step: 25
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f16: true
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diffusers:
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pipeline_type: StableDiffusionPipeline
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cuda: true
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enable_parameters: "negative_prompt,num_inference_steps"
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scheduler_type: "k_dpmpp_2m"
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usage: |
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curl http://localhost:8080/v1/images/generations \
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-H "Content-Type: application/json" \
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-d '{
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"prompt": "<positive prompt>|<negative prompt>",
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"step": 25,
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"size": "512x512"
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}'
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18
aio/intel/speech-to-text.yaml
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aio/intel/speech-to-text.yaml
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name: whisper-1
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backend: whisper
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parameters:
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model: ggml-whisper-base.bin
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usage: |
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## example audio file
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wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
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## Send the example audio file to the transcriptions endpoint
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curl http://localhost:8080/v1/audio/transcriptions \
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-H "Content-Type: multipart/form-data" \
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-F file="@$PWD/gb1.ogg" -F model="whisper-1"
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download_files:
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- filename: "ggml-whisper-base.bin"
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sha256: "60ed5bc3dd14eea856493d334349b405782ddcaf0028d4b5df4088345fba2efe"
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uri: "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.bin"
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15
aio/intel/text-to-speech.yaml
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aio/intel/text-to-speech.yaml
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name: tts-1
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download_files:
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- filename: voice-en-us-amy-low.tar.gz
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uri: https://github.com/rhasspy/piper/releases/download/v0.0.2/voice-en-us-amy-low.tar.gz
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parameters:
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model: en-us-amy-low.onnx
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usage: |
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To test if this model works as expected, you can use the following curl command:
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curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
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"model":"tts-1",
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"input": "Hi, this is a test."
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}'
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51
aio/intel/text-to-text.yaml
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aio/intel/text-to-text.yaml
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name: gpt-4
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mmap: false
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f16: false
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parameters:
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model: huggingface://NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/Hermes-2-Pro-Mistral-7B.Q6_K.gguf
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roles:
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assistant_function_call: assistant
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function: tool
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template:
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chat_message: |
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<|im_start|>{{if eq .RoleName "assistant"}}assistant{{else if eq .RoleName "system"}}system{{else if eq .RoleName "function"}}{{.Role}}{{else if eq .RoleName "user"}}user{{end}}
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{{ if eq .RoleName "assistant_function_call" }}<tool_call>{{end}}
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{{ if eq .RoleName "function" }}<tool_result>{{end}}
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{{if .Content}}{{.Content}}{{end}}
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{{if .FunctionCall}}{{toJson .FunctionCall}}{{end}}
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{{ if eq .RoleName "assistant_function_call" }}</tool_call>{{end}}
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{{ if eq .RoleName "function" }}</tool_result>{{end}}
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<|im_end|>
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# https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF#prompt-format-for-function-calling
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function: |
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<|im_start|>system
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You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:
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<tools>
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{{range .Functions}}
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{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
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{{end}}
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</tools>
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Use the following pydantic model json schema for each tool call you will make:
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{'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
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For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
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<tool_call>
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{'arguments': <args-dict>, 'name': <function-name>}
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</tool_call><|im_end|>
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{{.Input}}
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<|im_start|>assistant
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<tool_call>
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chat: |
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{{.Input}}
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<|im_start|>assistant
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completion: |
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{{.Input}}
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context_size: 4096
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stopwords:
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- <|im_end|>
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- <dummy32000>
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usage: |
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curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "gpt-4",
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"messages": [{"role": "user", "content": "How are you doing?", "temperature": 0.1}]
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}'
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35
aio/intel/vision.yaml
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aio/intel/vision.yaml
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backend: llama-cpp
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context_size: 4096
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mmap: false
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f16: false
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name: gpt-4-vision-preview
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roles:
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user: "USER:"
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assistant: "ASSISTANT:"
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system: "SYSTEM:"
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mmproj: llava-v1.6-7b-mmproj-f16.gguf
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parameters:
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model: llava-v1.6-mistral-7b.Q5_K_M.gguf
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temperature: 0.2
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top_k: 40
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top_p: 0.95
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seed: -1
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template:
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chat: |
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A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
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{{.Input}}
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ASSISTANT:
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download_files:
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- filename: llava-v1.6-mistral-7b.Q5_K_M.gguf
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uri: huggingface://cjpais/llava-1.6-mistral-7b-gguf/llava-v1.6-mistral-7b.Q5_K_M.gguf
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- filename: llava-v1.6-7b-mmproj-f16.gguf
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uri: huggingface://cjpais/llava-1.6-mistral-7b-gguf/mmproj-model-f16.gguf
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usage: |
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curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "gpt-4-vision-preview",
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"messages": [{"role": "user", "content": [{"type":"text", "text": "What is in the image?"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" }}], "temperature": 0.9}]}'
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