feat(aio): add intel profile (#1901)

* feat(aio): add intel profile

* docs: clarify AIO images features
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Ettore Di Giacinto 2024-03-26 18:45:25 +01:00 committed by GitHub
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16 changed files with 178 additions and 23 deletions

12
aio/intel/embeddings.yaml Normal file
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name: text-embedding-ada-002
backend: sentencetransformers
parameters:
model: all-MiniLM-L6-v2
usage: |
You can test this model with curl like this:
curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
"input": "Your text string goes here",
"model": "text-embedding-ada-002"
}'

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aio/intel/image-gen.yaml Normal file
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name: stablediffusion
parameters:
model: runwayml/stable-diffusion-v1-5
backend: diffusers
step: 25
f16: true
diffusers:
pipeline_type: StableDiffusionPipeline
cuda: true
enable_parameters: "negative_prompt,num_inference_steps"
scheduler_type: "k_dpmpp_2m"
usage: |
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"prompt": "<positive prompt>|<negative prompt>",
"step": 25,
"size": "512x512"
}'

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name: whisper-1
backend: whisper
parameters:
model: ggml-whisper-base.bin
usage: |
## example audio file
wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
## Send the example audio file to the transcriptions endpoint
curl http://localhost:8080/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@$PWD/gb1.ogg" -F model="whisper-1"
download_files:
- filename: "ggml-whisper-base.bin"
sha256: "60ed5bc3dd14eea856493d334349b405782ddcaf0028d4b5df4088345fba2efe"
uri: "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.bin"

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name: tts-1
download_files:
- filename: voice-en-us-amy-low.tar.gz
uri: https://github.com/rhasspy/piper/releases/download/v0.0.2/voice-en-us-amy-low.tar.gz
parameters:
model: en-us-amy-low.onnx
usage: |
To test if this model works as expected, you can use the following curl command:
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
"model":"tts-1",
"input": "Hi, this is a test."
}'

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name: gpt-4
mmap: false
f16: false
parameters:
model: huggingface://NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/Hermes-2-Pro-Mistral-7B.Q6_K.gguf
roles:
assistant_function_call: assistant
function: tool
template:
chat_message: |
<|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}}
{{ if eq .RoleName "assistant_function_call" }}<tool_call>{{end}}
{{ if eq .RoleName "function" }}<tool_result>{{end}}
{{if .Content}}{{.Content}}{{end}}
{{if .FunctionCall}}{{toJson .FunctionCall}}{{end}}
{{ if eq .RoleName "assistant_function_call" }}</tool_call>{{end}}
{{ if eq .RoleName "function" }}</tool_result>{{end}}
<|im_end|>
# https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF#prompt-format-for-function-calling
function: |
<|im_start|>system
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:
<tools>
{{range .Functions}}
{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
{{end}}
</tools>
Use the following pydantic model json schema for each tool call you will make:
{'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{'arguments': <args-dict>, 'name': <function-name>}
</tool_call><|im_end|>
{{.Input}}
<|im_start|>assistant
<tool_call>
chat: |
{{.Input}}
<|im_start|>assistant
completion: |
{{.Input}}
context_size: 4096
stopwords:
- <|im_end|>
- <dummy32000>
usage: |
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "gpt-4",
"messages": [{"role": "user", "content": "How are you doing?", "temperature": 0.1}]
}'

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aio/intel/vision.yaml Normal file
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backend: llama-cpp
context_size: 4096
mmap: false
f16: false
name: gpt-4-vision-preview
roles:
user: "USER:"
assistant: "ASSISTANT:"
system: "SYSTEM:"
mmproj: llava-v1.6-7b-mmproj-f16.gguf
parameters:
model: llava-v1.6-mistral-7b.Q5_K_M.gguf
temperature: 0.2
top_k: 40
top_p: 0.95
seed: -1
template:
chat: |
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
{{.Input}}
ASSISTANT:
download_files:
- filename: llava-v1.6-mistral-7b.Q5_K_M.gguf
uri: huggingface://cjpais/llava-1.6-mistral-7b-gguf/llava-v1.6-mistral-7b.Q5_K_M.gguf
- filename: llava-v1.6-7b-mmproj-f16.gguf
uri: huggingface://cjpais/llava-1.6-mistral-7b-gguf/mmproj-model-f16.gguf
usage: |
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "gpt-4-vision-preview",
"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}]}'