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Tim Dolan

macadeliccc

AI & ML interests

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Recent Activity

updated a dataset 17 days ago

macadeliccc/US-SupremeCourt-RAG

liked a model 17 days ago

microsoft/OmniParser

liked a model about 1 month ago

bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF

Articles

Deploy hundreds of open source models on one GPU using LoRAX

Jul 18

• 3

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Posts 10

Post

947

My tool calling playgrounds repo has been updated again to include the use of flux1-schnell or dev image generation. This functionality is similar to using Dall-E 3 via the @ decorator in ChatGPT. Once the function is selected, the model will either extract or improve your prompt (depending on how you ask).

I have also included 2 notebooks that cover different ways to access Flux for your specific use case. The first method covers how to access flux via LitServe from Lightning AI. LitServe is a bare-bones inference engine with a focus on modularity rather than raw performance. LitServe supports text generation models as well as image generation, which is great for some use cases, but does not provide the caching mechanisms from a dedicated image generation solution.

Since dedicated caching mechanisms are so crucial to performance, I also included an example for how to integrate SwarmUI/ComfyUI to utilize a more dedicated infrastructure that may already be running as part of your tech stack. Resulting in a Llama-3.1 capable of utilizing specific ComfyUI JSON configs, and many different settings.

Lastly, I tested the response times for each over a small batch request to simulate a speed test.

It becomes clear quickly how efficient caching mechanisms can greatly reduce the generation time, even in a scenario where another model is called. An average 4.5 second response time is not bad at all when you consider that an 8B model is calling a 12B parameter model for a secondary generation.

Repo: https://github.com/tdolan21/tool-calling-playground
LitServe: https://github.com/Lightning-AI/LitServe
SwarmUI: https://github.com/mcmonkeyprojects/SwarmUI

Post

1589

Automated web scraping with playwright is becoming easier by the day. Now, using ollama tool calling, its possible to perform very high accuracy web scraping (in some cases 100% accurate) through just asking an LLM to scrape the content for you.

This can be completed in a multistep process similar to cohere's platform. If you have tried the cohere playground with web scraping, this will feel very similar. In my experience, the Llama 3.1 version is much better due to the larger context window. Both tools are great, but the difference is the ollama + playwright version is completely controlled by you.

All you need to do is wrap your scraper in a function:

async def query_web_scraper(url: str) -> dict:
    scraper = WebScraper(headless=False)
    return await scraper.query_page_content(url)

and then make your request:

# First API call: Send the query and function description to the model
response = ollama.chat(
    model=model,
    messages=messages,
    tools=[
        {
            'type': 'function',
            'function': {
                'name': 'query_web_scraper',
                'description': 'Scrapes the content of a web page and returns the structured JSON object with titles, articles, and associated links.',
                'parameters': {
                    'type': 'object',
                    'properties': {
                        'url': {
                            'type': 'string',
                            'description': 'The URL of the web page to scrape.',
                        },
                    },
                    'required': ['url'],
                },
            },
        },
    ]
)