risedangel (risedangel)

upvoted 2 collections 29 minutes ago

Turkish Subwords Research

Collection

34 items • Updated about 1 hour ago • 1

Turkish Benchmarking Sets

Collection

A collection of benchmarking sets for Turkish NLP. • 4 items • Updated about 1 hour ago • 1

liked 3 models about 2 months ago

liked a model 2 months ago

Phr00t/Qwen-Image-Edit-Rapid-AIO

Text-to-Image • Updated 4 days ago • 1.19k

liked a dataset 2 months ago

AlicanKiraz0/Turkish-SFT-Dataset-v1.0

Viewer • Updated Oct 26 • 5.58k • 376 • 47

liked a dataset 3 months ago

selimfirat/bilkent-turkish-writings-dataset

Viewer • Updated May 24 • 25.1k • 169 • 8

reacted to ZennyKenny's post with ❤️ 3 months ago

Post

8915

🖤 Probably one of my favorite projects that I've worked on so far, introducing Новояз (Novoyaz).

🛠 One of the first acts of the Bolshevik government after the Russian Revolution was the reform and standardization of the Russian language, which at the time had a non-standard and challenging orthography.

📚 Upon its reform the government launched a nationwide campaign called Ликбез (Likbez), which sought to improve literacy in the country (by the way, it worked, bringing the national literacy rate from <20% in the 1920s to >80% by the 1930s).

‼ While this is a remarkable result that should absolutely be celebrated, it's one that has left behind literally hundreds of thousands if not millions of artifacts using pre-reform Russian orthography.

😓 Researchers and historians are working tirelessly to translate these artifacts to modern Russian so that they may be archived and studied but many have told me that. they are doing this BY HAND (!).

💡 I thought, well this is a perfect use case for OCR and a fine-tuned LLM to step in and help to aid in this important work!

🌏 Introducing НОВОЯЗ (NOVOYAZ)! Powered by ChatDOC/OCRFlux-3B and https://huggingface.co/ZennyKenny/oss-20b-prereform-to-modern-ru-merged, researchers can now convert images of their pre-reform documents to modern Russian orthography using the power of open-source AI!

Check it out and drop a like to support more real-world use cases for open source AI outside of traditional tech-centric domains!

ZennyKenny/Novoyaz

reacted to hesamation's post with 👍❤️ 4 months ago

Post

10860

a senior engineer at google just dropped a 400-page free book on docs for review: agentic design patterns.

the table of contents looks like everything you need to know about agents + code:
> advanced prompt techniques
> multi-agent patterns
> tool use and MCP
> you name it

read it here: https://docs.google.com/document/d/1rsaK53T3Lg5KoGwvf8ukOUvbELRtH-V0LnOIFDxBryE/edit?tab=t.0#heading=h.pxcur8v2qagu

you can also pre-order on Amazon (published by Springer) and the royalties goes to Save the Children: https://www.amazon.com/Agentic-Design-Patterns-Hands-Intelligent/dp/3032014018/

reacted to codelion's post with 👍 4 months ago

Post

6186

I recently worked on a LoRA that improves tool use in LLM. Thought the approach might interest folks here.

The issue I have had when trying to use some of the local LLMs with coding agents is this:

Me: "Find all API endpoints with authentication in this codebase"
LLM: "You should look for @app .route decorators and check if they have auth middleware..."

But I often want it to search the files and show me but the LLM doesn't trigger a tool use call.

To fine-tune it for tool use I combined two data sources:

1. Magpie scenarios - 5000+ diverse tasks (bug hunting, refactoring, security audits)
2. Real execution - Ran these on actual repos (FastAPI, Django, React) to get authentic tool responses

This ensures the model learns both breadth (many scenarios) and depth (real tool behavior).

Tools We Taught:
- read_file - Actually read file contents
- search_files - Regex/pattern search across codebases
- find_definition - Locate classes/functions
- analyze_imports - Dependency tracking
- list_directory - Explore structure
- run_tests - Execute test suites

Improvements:
- Tool calling accuracy: 12% → 80%
- Correct parameters: 8% → 87%
- Multi-step tasks: 3% → 78%
- End-to-end completion: 5% → 80%
- Tools per task: 0.2 → 3.8

The LoRA really improves on intential tool call as an example consider the query: "Find ValueError in payment module"

The response proceeds as follows:

1. Calls search_files with pattern "ValueError"
2. Gets 4 matches across 3 files
3. Calls read_file on each match
4. Analyzes context
5. Reports: "Found 3 ValueError instances: payment/processor.py:47 for invalid amount, payment/validator.py:23 for unsupported currency..."

Resources:
- Colab notebook https://colab.research.google.com/github/codelion/ellora/blob/main/Ellora_Recipe_3_Enhanced_Tool_Calling_and_Code_Understanding.ipynb
- Model - codelion/Llama-3.2-1B-Instruct-tool-calling-lora
- GitHub - https://github.com/codelion/ellora