microsoft/phi-4
Text Generation
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Alxy Savin
xsa-dev
AI & ML interests
npl, news, sentiments, rl, io
Recent Activity
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8 days ago
microsoft/phi-4
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macadeliccc's
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5 months ago
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'],
},
},
},
]
)
```
To learn more:
Github w/ Playground: https://github.com/tdolan21/tool-calling-playground/blob/main/notebooks/ollama-playwright-web-scraping.ipynb
Complete Guide: https://medium.com/@tdolan21/building-an-llm-powered-web-scraper-with-ollama-and-playwright-6274d5d938b5
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6 months ago
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xsa-dev's activity
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5 months ago
Post
1748
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:
and then make your request:
To learn more:
Github w/ Playground: https://github.com/tdolan21/tool-calling-playground/blob/main/notebooks/ollama-playwright-web-scraping.ipynb
Complete Guide: https://medium.com/@tdolan21/building-an-llm-powered-web-scraper-with-ollama-and-playwright-6274d5d938b5
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'],
},
},
},
]
)To learn more:
Github w/ Playground: https://github.com/tdolan21/tool-calling-playground/blob/main/notebooks/ollama-playwright-web-scraping.ipynb
Complete Guide: https://medium.com/@tdolan21/building-an-llm-powered-web-scraper-with-ollama-and-playwright-6274d5d938b5
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#1 opened 9 months ago
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myx0
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radames's
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10 months ago
Post
3724
Testing new pix2pix-Turbo in real-time, very interesting GAN architecture that leverages SD-Turbo model. Here I'm using edge2image LoRA single-step inference 🤯
It's very interesting how ControlNet Canny quality is comparable, but in a single step. Looking forward to when they release the code: https://github.com/GaParmar/img2img-turbo/issues/1
I've been keeping a list of fast diffusion model pipelines together with this real-time websocket app. Have a look if you want to test it locally, or check out the demo here on Spaces.
radames/real-time-pix2pix-turbo
Github app:
https://github.com/radames/Real-Time-Latent-Consistency-Model/
You can also check the authors img2img sketch model here
gparmar/img2img-turbo-sketch
Refs:
One-Step Image Translation with Text-to-Image Models (2403.12036)
cc @gparmar @junyanz
It's very interesting how ControlNet Canny quality is comparable, but in a single step. Looking forward to when they release the code: https://github.com/GaParmar/img2img-turbo/issues/1
I've been keeping a list of fast diffusion model pipelines together with this real-time websocket app. Have a look if you want to test it locally, or check out the demo here on Spaces.
radames/real-time-pix2pix-turbo
Github app:
https://github.com/radames/Real-Time-Latent-Consistency-Model/
You can also check the authors img2img sketch model here
gparmar/img2img-turbo-sketch
Refs:
One-Step Image Translation with Text-to-Image Models (2403.12036)
cc @gparmar @junyanz
upvoted
a
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11 months ago
reacted to
victor's
post with 👍
12 months ago
Post
🔥 New on HuggingChat: Assistants!
Today we are releasing Assistants on HuggingChat!
Assistants are a fun way to package your prompts and share them with the world - powered by Open source Models of course!
Learn more about Assistants here: huggingchat/chat-ui#357
Browse Assistants here: https://huggingface.co/chat/assistants
Today we are releasing Assistants on HuggingChat!
Assistants are a fun way to package your prompts and share them with the world - powered by Open source Models of course!
Learn more about Assistants here: huggingchat/chat-ui#357
Browse Assistants here: https://huggingface.co/chat/assistants
- 11 replies
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euclaise's
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12 months ago
Post
Memphis: Advancing language model reasoning without relying on proprietary model outputs
Memphis is a series of models which advance human-data models, offering good performance without relying on proprietary model outputs (e.g. GPT-generated datasets). I've developed a new iterative finetuning procedure to improve the reasoning ability of these models beyond what is possible using only SFT on the same data.
Currently, I've released two models: Memphis-CoT-3B, and Memphis-scribe-3B.
To create these models, I've created new datasets:
- euclaise/reddit-instruct : A dataset of instruction/QA-like data scraped from Reddit. A curated version, filtered using Lilac and neural embedding models, is available at euclaise/reddit-instruct-curated
- euclaise/TinyCoT : TinyCoT is a mtea-dataset that aggregates a variety of different human-sourced reasoning data. It is a curated version of my previous MegaCoT dataset euclaise/MegaCoT, which contains 629k responses which get cut down to 28k for TinyCoT. There's also an intermediate version euclaise/MiniCoT, which has 129k responses.
Memphis-CoT is trained on reddit-instruct, a filtered version of oasst2 sablo/oasst2_curated, and TinyCoT. Multiple iterations were performed on TinyCoT, while reddit-instruct and oasst2 were only used for the initial model.
Memphis-scribe further finetunes Memphis-CoT on more creative tasks. It was finetuned from Memphis-CoT on 18 different datasets, including datasets like euclaise/WritingPrompts_curated, lemonilia/LimaRP, and more.
To prevent catastrophic forgetting, I used weight averaging between iterations.
- euclaise/Memphis-CoT-3B
- euclaise/Memphis-scribe-3B
Memphis is a series of models which advance human-data models, offering good performance without relying on proprietary model outputs (e.g. GPT-generated datasets). I've developed a new iterative finetuning procedure to improve the reasoning ability of these models beyond what is possible using only SFT on the same data.
Currently, I've released two models: Memphis-CoT-3B, and Memphis-scribe-3B.
To create these models, I've created new datasets:
- euclaise/reddit-instruct : A dataset of instruction/QA-like data scraped from Reddit. A curated version, filtered using Lilac and neural embedding models, is available at euclaise/reddit-instruct-curated
- euclaise/TinyCoT : TinyCoT is a mtea-dataset that aggregates a variety of different human-sourced reasoning data. It is a curated version of my previous MegaCoT dataset euclaise/MegaCoT, which contains 629k responses which get cut down to 28k for TinyCoT. There's also an intermediate version euclaise/MiniCoT, which has 129k responses.
Memphis-CoT is trained on reddit-instruct, a filtered version of oasst2 sablo/oasst2_curated, and TinyCoT. Multiple iterations were performed on TinyCoT, while reddit-instruct and oasst2 were only used for the initial model.
Memphis-scribe further finetunes Memphis-CoT on more creative tasks. It was finetuned from Memphis-CoT on 18 different datasets, including datasets like euclaise/WritingPrompts_curated, lemonilia/LimaRP, and more.
To prevent catastrophic forgetting, I used weight averaging between iterations.
- euclaise/Memphis-CoT-3B
- euclaise/Memphis-scribe-3B
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