Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or…
vLLM tiered KV cache configuration for production H100/H200 clusters. Native CPU offload, LMCache (CPU+NVMe+GDS), NixlConnector (disaggregated prefill), MooncakeConnector (RDMA),…
Configure vLLM completely — YAML config file format, CLI arg precedence, full VLLM_*/HF_*/TRANSFORMERS_* env-var catalog, end-to-end recipe for air-gapped environments (internal…
vLLM is a fast and memory-efficient inference and serving engine for large language models. It uses PagedAttention for efficient memory management, supports continuous batching,…
Set up CI/CD pipelines for vLLM-Omni model deployments including Docker builds, automated testing, rolling updates, and deployment validation.
On HK - SSH, Slurm, non-interactive docker exec (bash -lc): **`source /rebase/.venv/bin/activate`** inside the container before repo commands, then run…
Use when working on vLLM-Omni quantization for autoregressive, diffusion, or multi-stage omni models, choosing methods such as `awq`, `gptq`, `fp8`, `int8`, `gguf`, or ModelOpt…
Use when adding a recipe for omnimodal models (text-to-image, text-to-video, text-to-audio, image-to-video, any-to-any, diffusion transformers) to the vLLM recipes repository, or…
Two report kinds; **default output is always HTML** unless the user explicitly asks for Markdown (.md).
Despliegue de vLLM para inferencia LLM/VLM en GPUs AMD ROCm o NVIDIA CUDA con detección automática de backend.
This SOP provides a systematic workflow for training and deploying neural networks using Flow Nexus platform with distributed E2B sandboxes.
This skill optimizes prompts for Large Language Models (LLMs) to reduce token usage, lower costs, and improve performance.
Use when when you have MS/MS spectra from GNPS or other libraries and need to apply a pre-trained FIDDLE model (TCN formula predictor or Siamese rescore architecture) without…
This skill optimizes deep learning models using various techniques. It is triggered when the user requests improvements to model performance, such as increasing accuracy, reducing…
Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud.
Use this skill when building production LLM applications, implementing guardrails, evaluating model outputs, or deciding between prompting and fine-tuning.
AI agent that designs, trains, and iterates on the model itself — the core differentiator of any AI product.
This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis,…
This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin.
Comprehensive RAG evaluation with retrieval metrics, generation quality, and end-to-end testing. Use this skill when measuring and improving RAG system performance.
Create and work with token embeddings for LLMs. Use this skill whenever you need to understand token embeddings, create embedding layers in PyTorch, add positional embeddings…
Retell AI architecture variants \u2014 AI voice agent and phone call\
TensorFlow best practices for tf.function, GPU memory, and deployment