Create repo card with prompt-templates library

README.md

@@ -1,90 +1,9 @@
 ---
-license: mit
 library_name: prompt-templates
 tags:
 - prompts
-- model-prompts
+- prompt-templates
 ---
-
-## Sharing special prompts of open-weight models
-
-This repo illustrates how you can use the `prompt_templates` library to load prompts from YAML files in open-weight model repositories.
-Several open-weight models have been tuned on specific tasks with specific prompts. 
-For example, the InternVL2 vision language models are one of the very few VLMs that have been trained for zeroshot bounding box prediction for any object.
-To elicit this capability, users need to use this special prompt: `Please provide the bounding box coordinate of the region this sentence describes: <ref>{region_to_detect}</ref>'`
-
-These these kinds of task-specific special prompts are currently unsystematically reported in model cards, github repos, .txt files etc. 
-
-The prompt_templates library standardises the sharing of prompts in YAML files.
-I recommend sharing these special prompts directly in the model repository of the respective model. 
-
-Below is an example for the InternVL2 model. 
-
-Note that this model card is not actively maintained and the latest documentation for `prompt_templates` is available at https://github.com/MoritzLaurer/prompt_templates 
-
-#### Prompt for extracting bounding boxes of specific objects of interest with InternVL2
-```py
-#!pip install prompt_templates
-from prompt_templates import PromptTemplateLoader
-
-# download image prompt template
-prompt_template = PromptTemplateLoader.from_hub(repo_id="MoritzLaurer/open_models_special_prompts", filename="internvl2-bbox-prompt.yaml")
-
-# populate prompt
-image_url = "https://unsplash.com/photos/ZVw3HmHRhv0/download?ixid=M3wxMjA3fDB8MXxhbGx8NHx8fHx8fDJ8fDE3MjQ1NjAzNjl8&force=true&w=1920"
-region_to_detect = "the bird"
-messages = prompt_template.populate_template(image_url=image_url, region_to_detect=region_to_detect)
-
-print(messages)
-# out: [{'role': 'user'
-#        'content': [{'type': 'image_url',
-#                      'image_url': {'url': 'https://unsplash.com/photos/ZVw3HmHRhv0/download?ixid=M3wxMjA3fDB8MXxhbGx8NHx8fHx8fDJ8fDE3MjQ1NjAzNjl8&force=true&w=1920'}},
-#                    {'type': 'text',
-#                     'text': 'Please provide the bounding box coordinate of the region this sentence describes: <ref>the bird</ref>'}]
-#      }]
-```
-
-#### Prompt for extracting bounding boxes of any object in an image with InternVL2
-```py
-# download image prompt template
-prompt_template = PromptTemplateLoader.from_hub(repo_id="MoritzLaurer/open_models_special_prompts", filename="internvl2-objectdetection-prompt.yaml")
-
-# populate prompt
-image_url = "https://unsplash.com/photos/ZVw3HmHRhv0/download?ixid=M3wxMjA3fDB8MXxhbGx8NHx8fHx8fDJ8fDE3MjQ1NjAzNjl8&force=true&w=1920"
-messages = prompt_template.populate_template(image_url=image_url)
-
-print(messages)
-# [{'role': 'user',
-#  'content': [{'type': 'image_url',
-#    'image_url': {'url': 'https://unsplash.com/photos/ZVw3HmHRhv0/download?ixid=M3wxMjA3fDB8MXxhbGx8NHx8fHx8fDJ8fDE3MjQ1NjAzNjl8&force=true&w=1920'}},
-#   {'type': 'text',
-#    'text': 'Please detect and label all objects in the following image and mark their positions.'}]}]
-```
-
-#### Using the prompt with an open inference container like vLLM or TGI
-
-These populated prompts in the OpenAI messages format are then directly compatible with vLLM or TGI containers. 
-When you host one of these containers on a HF Endpoint, for example, you can call on the model with the OpenAI client or with the HF Interence Client. 
-
-```py
-from openai import OpenAI
-import os
-
-ENDPOINT_URL = "https://tkuaxiztuv9pl4po.us-east-1.aws.endpoints.huggingface.cloud" + "/v1/" 
-
-# initialize the OpenAI client but point it to an endpoint running vLLM or TGI
-client = OpenAI(
-    base_url=ENDPOINT_URL, 
-    api_key=os.getenv("HF_TOKEN")
-)
-
-response = client.chat.completions.create(
-    model="/repository", # with vLLM deployed on HF endpoint, this needs to be /repository since there are the model artifacts stored
-    messages=messages,
-)
-
-response.choices[0].message.content
-# out: 'the bird[[54, 402, 515, 933]]'
-```
-
-
+This repository was created with the `prompt-templates` library and contains
+prompt templates in the `Files` tab.
+For easily reusing these templates, see the [prompt-templates documentation](https://github.com/MoritzLaurer/prompt-templates).