Paraskevi Kivroglou's picture

Paraskevi Kivroglou

KvrParaskevi

AI & ML interests

I am looking forward into a world full of AI innovation. By having small ideas in new projects, I want to take the next step and give them life.

Recent Activity

liked a dataset 4 days ago
evalplus/mbppplus
liked a dataset 10 days ago
BAAI/TACO
upvoted a paper about 1 month ago
Qwen2.5-Coder Technical Report
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GEM benchmark's profile picture lora concepts library's profile picture Blog-explorers's profile picture ZeroGPU Explorers's profile picture INNOVA AI's profile picture Cognitive Computations's profile picture

KvrParaskevi's activity

reacted to reach-vb's post with ๐Ÿš€ about 2 months ago
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2973
Smol models ftw! AMD released AMD OLMo 1B - beats OpenELM, tiny llama on MT Bench, Alpaca Eval - Apache 2.0 licensed ๐Ÿ”ฅ

> Trained with 1.3 trillion (dolma 1.7) tokens on 16 nodes, each with 4 MI250 GPUs

> Three checkpoints:

- AMD OLMo 1B: Pre-trained model
- AMD OLMo 1B SFT: Supervised fine-tuned on Tulu V2, OpenHermes-2.5, WebInstructSub, and Code-Feedback datasets
- AMD OLMo 1B SFT DPO: Aligned with human preferences using Direct Preference Optimization (DPO) on UltraFeedback dataset

Key Insights:
> Pre-trained with less than half the tokens of OLMo-1B
> Post-training steps include two-phase SFT and DPO alignment
> Data for SFT:
- Phase 1: Tulu V2
- Phase 2: OpenHermes-2.5, WebInstructSub, and Code-Feedback

> Model checkpoints on the Hub & Integrated with Transformers โšก๏ธ

Congratulations & kudos to AMD on a brilliant smol model release! ๐Ÿค—

amd/amd-olmo-6723e7d04a49116d8ec95070
replied to qq8933's post about 2 months ago
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Awesome work. Can we finetune further this reasoning model?

reacted to qq8933's post with ๐Ÿ‘ about 2 months ago
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6258
LLaMA-O1: Open Large Reasoning Model Frameworks For Training, Inference and Evaluation With PyTorch and HuggingFace
Large Reasoning Models powered by Monte Carlo Tree Search (MCTS), Self-Play Reinforcement Learning, PPO, AlphaGo Zero's dua policy paradigm and Large Language Models!
https://github.com/SimpleBerry/LLaMA-O1/

What will happen when you compound MCTS โค LLM โค Self-Play โคRLHF?
Just a little bite of strawberry!๐Ÿ“

Past related works:
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning (2410.02884)
Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B (2406.07394)
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reacted to nroggendorff's post with ๐Ÿ‘€ about 2 months ago
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2648
When huggingface patches this, I'm going to be really sad, but in the meantime, here you go:

When AutoTrain creates a new space to train your model, it does so via the huggingface API. If you modify the code so that it includes a premade README.md file, you can add these two lines:

---
app_port: 8080 # or any integer besides 7860 that's greater than 2 ** 10
startup_duration_timeout: 350m
---


This will tell huggingface to listen for the iframe on your port, instead of the one autotrain is actually hosting on, and because startup time isn't charged, you get the product for free. (you can take this even further by switching compute type to A100 or something)
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reacted to ajibawa-2023's post with โค๏ธ๐Ÿš€ about 2 months ago
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2988
New Dataset: Software-Architecture
Link: ajibawa-2023/Software-Architecture

I am releasing a Large Dataset covering topics related to Software-Architecture. This dataset consists of around 450,000 lines of data in jsonl.

I have included following topics:

Architectural Frameworks

Architectural Patterns for Reliability

Architectural Patterns for Scalability

Architectural Patterns

Architectural Quality Attributes

Architectural Testing

Architectural Views

Architectural Decision-Making

Advanced Research

Cloud-Based Architectures

Component-Based Architecture

Data Architecture

Emerging Trends

Event-Driven Architecture

Evolvability and Maintainability

Microservices and Monolithic

Microservices Architecture

Security Architecture

Service-Oriented Architecture

Software Design Principles

and Many More!

This dataset is useful in LLM development. Also those who are working on developing Software development related LLMs then this dataset can be useful.

This dataset is very useful to Researchers as well.
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