Raymond Oniel's 🇺🇬🇬🇧🇪🇹

cloud security engineer (intern) @Virtully Testing Foundation
Real men are SLICK!
They commit to:
Service
Loyalty
Integrity
Courage
Knowledge

[PUSSY LICKING KING 🇺🇬 @ugpussylicker]

That feeling 😍😋💦
https://t.co/rTbEgwSRHN

[Cyber_Racheal @CyberRacheal]

How a computer boots

[Nyaf fx @kichwa_]

Kumbee kuna venye you can run a Website through github without paying for dormain,techies how do i go about it ...?

[Kiki @Njoki_Njanja]

Which shower gel do you swear by?

[𝐉!𝐌𝐌𝐘 🇫🇷 🇸🇳 🇵🇹 @kafangi]

Ukienda kulala dem mambo huwa like this

[Khalil Afridi @khalilApriday]

FREE Microsoft Cyber Security 101
Kick-start Your Cybersecurity Career

https://t.co/aZibT6WT6G

[Oluwatobi Mustapha @Tobi_Msp]

Kali Linux Tools Explanation in 5 minutes

[Elorm Daniel @elormkdaniel]

Kali Linux Installation in 4 Minutes

[Maryam Miradi, PhD @MaryamMiradi]

🎁⏳These 6 steps make every future post on LLMs instantly clear and meaningful.

Learn exactly where Web Scraping, Tokenization, RLHF, Transformer Architectures, ONNX Optimization, Causal Language Modeling, Gradient Clipping, Adaptive Learning, Supervised Fine-Tuning, RLAIF, TensorRT Inference, and more fit into the LLM pipeline.

𝗜 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗲𝗱 𝟭𝟬𝟬𝟬+ 𝗽𝗮𝗴𝗲𝘀 ▼

﹌﹌﹌﹌﹌﹌﹌﹌﹌

》 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝟲 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝘁𝗲𝗽𝘀

✸ 1️⃣ Data Collection (Web Scraping & Curation)

☆ Web Scraping: Gather data from books, research papers, Wikipedia, GitHub, Reddit, and more using Scrapy, BeautifulSoup, Selenium, and APIs.

☆ Filtering & Cleaning: Remove duplicates, spam, broken HTML, and filter biased, copyrighted, or inappropriate content.

☆ Dataset Structuring: Tokenize text using BPE, SentencePiece, or Unigram; add metadata like source, timestamp, and quality rating.

✸ 2️⃣ Preprocessing & Tokenization

☆ Tokenization: Convert text into numerical tokens using SentencePiece or GPT’s BPE tokenizer.

☆ Data Formatting: Structure datasets into JSON, TFRecord, or Hugging Face formats; use Sharding for parallel processing.

✸ 3️⃣ Model Architecture & Pretraining

☆ Architecture Selection: Choose a Transformer-based model (GPT, T5, LLaMA, Falcon) and define parameter size (7B–175B).

☆ Compute & Infrastructure: Train on GPUs/TPUs (A100, H100, TPU v4/v5) with PyTorch, JAX, DeepSpeed, and Megatron-LM.

☆ Pretraining: Use Causal Language Modeling (CLM) with Cross-Entropy Loss, Gradient Checkpointing, and Parallelization (FSDP, ZeRO).

☆ Optimizations: Apply Mixed Precision (FP16/BF16), Gradient Clipping, and Adaptive Learning Rate Schedulers for efficiency.

✸ 4️⃣ Model Alignment (Fine-Tuning & RLHF)

☆ Supervised Fine-Tuning (SFT): Train on high-quality human-annotated datasets (InstructGPT, Alpaca, Dolly).

☆ Reinforcement Learning from Human Feedback (RLHF): Generate responses, rank outputs, train a Reward Model (PPO), and refine using Proximal Policy Optimization (PPO).

☆ Safety & Constitutional AI: Apply RLAIF, adversarial training, and bias filtering.

✸ 5️⃣ Deployment & Optimization

☆ Compression & Quantization: Reduce model size with GPTQ, AWQ, LLM.int8(), and Knowledge Distillation.

☆ API Serving & Scaling: Deploy with vLLM, Triton Inference Server, TensorRT, ONNX, and Ray Serve for efficient inference.

☆ Monitoring & Continuous Learning: Track performance, latency, and hallucinations;

✸ 6️⃣Evaluation & Benchmarking

☆ Performance Testing: Validate using HumanEval, HELM, OpenAI Eval, MMLU, ARC, and MT-Bench.

≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣

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[Winston Ighodaro @Officialwhyte22]

Kali Linux CheatSheet
Thread 🧵

[Hon.Moses Kuria,HSC @HonMoses_Kuria]

Thank you Mr President. You are a good man. Thank you for
Kindiki
Kagwe
Kabogo
Kinyanjui
The 4K Club of Mt Kenya

[Julians Amboko @AmbokoJH]

Finally, catch the full Draft Budget Policy Statement 2025 via link below.

Read & send comments by Jan 21st.

Ends!

https://t.co/6ca21vKToa

[Mohamed Wehliye, MBS @WehliyeMohamed]

Starehe School Band at the Namanga Border welcoming Ian Gichohi back to Kenya. Ian rode from Cairo to Cape to raise 100m towards the Griffin Endowment Fund. Well done Ian!

[Nelson Amenya @amenya_nelson]

Namna gani hapa Uhuru advising his daughter to join the movement? Haha interesting times. https://t.co/ylY1aP93PG

[Elvis @Elvis_Localman]

Earning Kes 30,000, this budget fits you to be fair. Don’t live above this if you can.

1. Rent - Kes 8,000

2. Food & home supplies - Kes 5,000

3. Transport - Kes 3,000

4. Utilities (power/water) - Kes 1,000

5. Airtime/Data/Internet - Kes 1,000

7. Toiletries/cleaning - Kes 500

8. Entertainment - Kes 3,000

9. Kids - Take them to public school, you have no business paying school fees.

10. Black Tax - avoid if you can, here it’s a matter of survival. Just remember you cannot help others whilst yourself you are in a hole 🕳️.

Total spent Kes 21,500

Saved Kes 8,500

At this stage in life, you need to properly network, have that confidence of get crushing any social joint you think is above you, as that is your only currency 💴 that time, sell yourself properly to potential clients or people who could possibly link you to better paying jobs etc.

I hope you are finding me up to there.

[Raymond Oniel's 🇺🇬🇬🇧🇪🇹 @Garang06]

@karigoh Hahaha

[Raymond Oniel's 🇺🇬🇬🇧🇪🇹 @Garang06]

@W_Mellannie @ombachi13 @ombachi13 alisema hajui hii kitu 😂 😆

[Spry Voice @SpryVoice]

If MP. Kibagendi has acquired three hospitals in three years, then he must be a miracle worker! Those who know about his route to MP will tell you the insults he was subjected to. He was insulted cause of his family background. His campaign relied on fundraising by friend.

[Nelson Havi SC @NelsonHavi]

Larry Madowo, give the young lawyer the brief. Do not listen to naysayers. You got a good case. Kshs 20,000,000.00 pap!

[Dif Madara @dif_madara]

@kanyi_254 Never disrespect the person who knows how and when you're asleep - Kabuoch Proverb
A person who cooks your food is your God - Kaksingri Proverb