๐ง A 26B model that runs like a 4B model? That's the magic of Gemma 4's MoE architecture.
๐ค Dive into thinking mode, multimodal reasoning, audio support, long-context attention, and benchmark performance.
โก From phones to H100s, learn which Gemma 4 model is right for workload.
๐ Turn your RAG pipeline from a black box into a glass box.
๐ค Powered by @langfuse, @vllm_project, FAISS, and SentenceTransformers.
๐ Trace retrieval, prompts, token usage, hallucinations, and quality scores with a fully observable production-grade RAG stack.
๐ Build a production-grade document ingestion pipeline for RAG systems.
โ๏ธ Using @ApacheAirflow + @FastAPI + @PostgreSQL
๐ Learn DAG orchestration, idempotency, deduplication, status tracking, and reliable PDF processing workflows.
Learn how to manually trace pipelines, score outputs, measure latency, and monitor quality with @langfuse + @vllm_project.
โก Full manual tracing API walkthrough
๐ Custom evaluation metrics + quality scoring
๐งฉ Built for RAG, agents, and complex AI systems
๐ง Self-host @langfuse locally
๐ Track prompts, traces, latency & token usage
โก Connect everything to @vllm_project with @OpenAI compatible APIs
๐ณ Run the full observability stack with @Docker Compose
New tutorial! ๐
LLM Observability with Self-Hosted Langfuse and vLLM
Because โit works on my machineโ is not an observability strategy ๐
https://t.co/SEUH7KpEd3
Author: Vikram Singh
#LLM#Langfuse#vLLM#MLOps#GenerativeAI#Docker#AI#Python#Tutorial
@puneet2k ๐ง Learn how Kimi-K2 stabilizes trillion-parameter LLM training with MuonClip + QK-Clip
โก Built using DeepSeek-V3-style MoE + MLA components
๐ ๏ธ Full PyTorch implementation included