Top Tweets for #fineTuning
Some earned rest after Indiana the last 10 days was nice, but back to the field & working on all the little things (that add up to the BIG things) felt great too! #srszn #catcher #finetuning #2027catcher
3+ years of AI engineering and advancing to the latest 2026 Agentic AI frameworks and enterprise AI implementation. #OpenAI #Claude #Gemini #AIEngineering #GenAI #LLM #RAG #AgenticAI #MCP #AutoGen #LangChain #LangGraph #CrewAI #VectorDB #FineTuning #AIEvals #ServiceNow #Oracle
🧠 Crusoe’s serverless fine-tuning is GA: upload data, skip GPU-cluster babysitting, and adapt open models—so long as your training set fits under 3 GB. “Serverless,” with a size limit.
https://t.co/79fT1CxTgA
#Lora #OpenaiApi #CrusoeCloud #FineTuning
【About "#FineTuning of the ＃universe.＆ my #life】
💫✨🌏✨🪐✨🌙✨
総集編 「眠れなくなるほどおもしろい宇宙の謎８０ 」
：
10の4万乗分の一・・・ふむ🤨
【end】https://t.co/TMNdF4mS3r @YouTubeより
Kimi K3 Explained: Inside Moonshot AI’s 2.8T Open-Weight Frontier Model
(Vietnamese version at the end)
...
Read full article here: https://t.co/4GHn8ocJuv
#satmis #quanskill #quansolution #quanverse #aibuilders #finetuning
Limitation: doesn't work well for small models (<1B). Under that size, use LoRA or full fine-tuning.
Bottom line: Prompt Tuning is a bet on scale. If your base model is big enough, this is the cheapest fine-tune that exists.
#AI #PromptTuning #FineTuning #LLMs
Is the universe fine-tuned - and does that mean there’s a cosmic designer? Peter S. Williams and John Richards debate evidence, God, and the limits of explanation. #UnbelievablePodcast #FineTuning #CosmologyDebate
https://t.co/VFlUFXFVoW
"If you run agents in prod, this matters because tuning API settings like reasoning retention and compaction can massively boost performance metrics. This highlights the importance of fine-tuning in AI systems for efficiency. #FineTuning #AIengineering"
Bottom line: P-Tuning is the "engineered" version of Prompt Tuning. More complex, but bridges the gap for smaller models.
#AI #PTuning #FineTuning #LLMs
Bottom line: Instruction Tuning is why modern LLMs feel "helpful." It's not the fanciest technique in this series — it's the most important.
#AI #InstructionTuning #FineTuning #LLMs
Bottom line: Prefix Tuning proved you can steer a model with just its input. That insight reshaped how we think about fine-tuning entirely.
#AI #PrefixTuning #FineTuning #LLMs
Bottom line: adapters are the "microservices" of fine-tuning. Less popular than LoRA, but architecturally cleaner for complex systems.#AI #Adapters #FineTuning #LLMs
Bottom line: adapters are the "microservices" of fine-tuning. Less popular than LoRA, but architecturally cleaner for complex systems.#AI #Adapters #FineTuning #LLMs
Bottom line: LoRA democratized fine-tuning. QLoRA democratized it for models that used to require a data center.
#AI #QLoRA #FineTuning #LLMs
Bottom line: if you're fine-tuning in 2026 and not using LoRA (or a variant), you're burning money. It's the default for a reason.
#AI #LoRA #FineTuning #LLMs
Moonshot AI just released Kimi K3, a massive 2.8T MoE model. NeMo-Automodel supports finetuning it day1 with CP! Checkout our EP32PP8 full parameter finetuning recipe: https://t.co/3l078KpwhF
And our optimization roadmap: https://t.co/D4U5KHIYBa
#SFT #finetuning #pytorch #NeMo
Train a custom #AI model from your own documents in about a minute.
Just name your model, upload your data, choose a base model, and start training.
🎥 Watch the full demo:
https://t.co/vpXE3842GL
#AI #LLM #GenAI #FineTuning #MachineLearning
Reserved GPU clusters bill you 24/7—even when idle.
BiOS bills by the second: spin up, fine-tune, deploy, and shut down. Access 600+ models with no reserved capacity, at 70–90% less than building your own infrastructure.
Start free → https://t.co/OVIwbtE16y
#MLOps #FineTuning
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