Tag: #huggingface
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 17 posts
17 posts are tagged #huggingface; the most recent was published on 2026-08-12.
Often tagged together: #llm 14 #ai 9 #open-source-llm 8 #2026-03 5 #fine-tuning 3
Most read in the last 90 days:
Choosing Speech Models: Practical Criteria for STT and TTS
Unlike text models, speech models are chosen after the language coverage, audio length constraints, real-time requirement, and diarization need are already fixed. This post lays out the parameters, licenses, language cov
2026-08-12 · 7 min read #ai#huggingface#open-source-llm#speech-to-text#text-to-speechChoosing Open Text Generation Models by Size Class
When picking an open text generation model, size is not a quality tier — it is a deployment constraint. This post splits the field into on-device small, single-GPU mid, and server-class large, listing the parameters, con
2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#text-generationOpen Models That Support Korean, and the Cost of Tokenization
A line saying a model supports Korean means it processes the language, not that it is good at it. This post lays out the card values read on 2026-08-12 for Korean-focused, bilingual, and multilingual open models, explain
2026-08-12 · 6 min read #ai#llm#huggingface#open-source-llm#korean-nlpLicenses and Deployment: Reading the Types and Verifying Quantized Builds
The short identifier in an open model license field is a table of contents; the actual terms live in the full text. This post organizes the license types actually encountered on 2026-08-12, what community licenses and no
2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#licenseChoosing Code Models: Completion vs Chat, FIM, and Licenses
Code models come in two different products: the completion model that fills the cursor position inside an editor, and the conversational model that answers questions. This post organizes the parameters, context, fill-in-
2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#code-llmVision and Multimodal: Image Understanding, OCR, and VLMs
Vision models are not one category. Conversational VLMs, dedicated OCR, document structuring, and image retrieval embeddings are different objects and do not substitute for one another. This post organizes the card value
2026-08-12 · 7 min read #ai#huggingface#open-source-llm#vision-language-model#ocrHow to Read a Hugging Face Model Card: What to Trust and What to Doubt
Six fields on a Hugging Face model card actually carry decision weight: license, parameter count, context length, intended use, limitations, and training data. This post explains what each field means and what it hides,
2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#model-cardEmbeddings and Rerankers: What Actually Matters in RAG
What decides your embedding model in a RAG build is not a leaderboard rank but dimensionality, maximum input length, prefix conventions, multilingual coverage, and the division of labor with a reranker. This post lays ou
2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#embeddingHow to Actually Read a Model Card — Pulling Out What You Need in 5 Minutes ♪ Listenable
A model card is written in a way that, read top to bottom, keeps you from finding what you need. The benchmark table takes up half the screen while the license and chat template pass by in a single line. This post flips
2026-08-02 · 19 min read #llm#huggingface#model-card#license#tokenizerWhat's Trending on Hugging Face Right Now — A Map of August 2026
As of August 2, 2026, I went through the Hugging Face trending list directly and organized models that are actually usable by purpose. In order — general LLMs, coding, embeddings and rerankers, vision, speech, image/vide
2026-08-02 · 18 min read #llm#huggingface#open-weights#model-selection#quantizationMLOps Platforms 2026 Deep Dive — MLflow, Kubeflow, W&B, Vertex AI, SageMaker, Databricks, BentoML, Ray, Modal, Hugging Face ♪ Listenable
A side-by-side look at 30+ MLOps platforms in May 2026. MLflow 3, Kubeflow 1.10, Weights & Biases, Comet, Neptune.ai, ClearML, Vertex AI, SageMaker, Azure ML, Databricks ML + Mosaic AI, Hugging Face Inference Endpoints,
2026-05-16 · 17 min read #mlops#mlflow#kubeflow#weights-and-biases#vertex-aiComplete Guide to LLM Fine-tuning with Unsloth 2025: QLoRA, 4-bit Quantization, 2x Faster Training ♪ Listenable
Everything about LLM fine-tuning with Unsloth! QLoRA/LoRA theory, 4-bit quantization (bitsandbytes), Unsloth 2x speed secret, Llama 3/Mistral/Qwen fine-tuning, data preparation, training config, VRAM optimization, GGUF/G
2026-03-25 · 17 min read #unsloth#llm#fine-tuning#qlora#loraComplete Guide to Korean LLM Training Data: Hugging Face Datasets, Preprocessing, and Quality Control ♪ Listenable
Everything about LLM training data! Hugging Face datasets (types/loading/conversion), Korean data collection (crawling/synthetic/translation), preprocessing (tokenization/cleaning/dedup), Instruction Tuning formats (Alpa
2026-03-25 · 23 min read #llm#training-data#huggingface#dataset#korean-nlp2025 AI Research Trends: Top HuggingFace Papers and 10 Defining Research Directions
A developer-focused review of HuggingFace trending papers and the 10 defining AI research trends of 2025. DeepSeek-R1 pure RL reasoning, Nemotron-Cascade 30B/3B MoE, GRPO, PagedAttention, million-token context limitation
2026-03-21 · 15 min read #ai-research#papers#huggingface#reasoning#moeOpen-Source LLM Landscape Guide: Models, Tools, and Deployment in 2026 ♪ Listenable
A comprehensive guide to the open-source LLM ecosystem in 2026. Covers the leading model families (Llama, Mistral, Gemma, Qwen, DeepSeek), local inference tools (Ollama, llama.cpp, vLLM), fine-tuning techniques (LoRA, QL
2026-03-17 · 20 min read #open-source#llm#llama#mistral#gemmaHuggingFace Ecosystem Complete Guide: Master Transformers, Datasets, PEFT, and Accelerate
A complete guide to mastering the entire HuggingFace ecosystem. Learn Transformers, Datasets, Tokenizers, PEFT, Accelerate, Diffusers, and Hub API with hands-on examples.
2026-03-17 · 18 min read #huggingface#transformers#peft#accelerate#nlpPractical LLM Fine-Tuning — Building Your Own Model with LoRA, QLoRA, and PEFT
A practical guide to LLM fine-tuning. From the mathematical principles of LoRA to training large models on consumer GPUs with QLoRA, and leveraging the PEFT library — all with detailed code examples.
2026-03-02 · 7 min read #llm#fine-tuning#lora#qlora#peft