Packs
7 packs@thedotmack
Claude Mem
Memory, search and workflow skills from thedotmack/claude-mem.
19 skills · pack
@micsapp
Plugin
Persistent memory system for Claude Code - seamlessly preserve context across sessions
5 skills · pack
curated
C/C++ Debugging
For C/C++ developers needing debugging tools, memory analysis, and GDB integration.
8 skills · pack
curated
Build RAG Pipeline with Pinecone
Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.
6 skills · pack
@juliusbrussee
Caveman
Token-compression suite: compressed chat mode plus commit, review, help, stats, memory-compress and subagent-crew skills by Julius Brussee.
7 skills · pack
@micsapp
Arscontexta
Conversational derivation engine — generate agent-native memory architecture from natural conversation. 15 kernel primitives, 26 commands, 17 feature blocks, 3 presets.
10 skills · pack
@pwdev-solucoes
Pwdev Code
Spec-driven development framework v2.3 — 8 real subagents (incl. advisor), per-task model routing, curated memory graph, opt-in parallel waves, external CLI delegation (Codex/OpenCode/Kimi/Gemini/Kiro), simplification pass, strict verify, audit hooks, 22 commands
2 skills · pack
Results for “memory”
19 skills
nvidia nemo-mbridge-perf-memory-tuning
Reduces peak GPU memory in Megatron Bridge training by applying expandable segments, parallelism resizing, activation recompute, and CPU offloading constraints.
2.2k · bundle
nvidia jetson-inference-mem-tune
Recommends an inference runtime and memory-related launch flags for LLM/VLM workloads on NVIDIA Jetson devices, based on a live memory audit snapshot.
2.2k · bundle
lingxling hf-mem
Estimates memory requirements for running Hugging Face models, including optional KV cache, using HTTP range requests without downloading weights.
253
orchestra-research unsloth
Provides expert guidance for fast fine-tuning with Unsloth, including LoRA/QLoRA optimization, with 2-5x faster training and 50-80% less memory usage.
10.4k · bundle
huggingface hf-mem
Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.
10.8k
antigravity hf-mem
Estimates GPU memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub using HTTP Range requests, without downloading weights locally.
42.4k
More results
samyakjhaveri cuda-omp-translator
Reference guide for evaluating LLM-generated translations between CUDA and OpenMP, covering memory model mapping, kernel launch patterns, shared memory, atomics, and common failure modes.
0
orchestra-research optimizing-attention-flash
Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
10.4k · bundle
orchestra-research quantizing-models-bitsandbytes
Quantize LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss using bitsandbytes. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers.
10.4k · bundle
orchestra-research rwkv-architecture
Use RWKV, a linear-time RNN-Transformer hybrid, for efficient long-context inference and training with constant memory usage.
10.4k · bundle
majiayu000 awq-quantization
Quantize large language models to 4-bit precision using activation-aware weight quantization, reducing memory footprint and speeding up inference with minimal accuracy loss.
567 · bundle
orchestra-research awq-quantization
Quantize large language models to 4-bit using activation-aware weight quantization, achieving ~3x speedup with minimal accuracy loss for deployment on limited GPU memory.
10.4k · bundle
lord1egypt peft-fine-tuning
Fine-tune large language models by training less than 1% of parameters using LoRA, QLoRA, and 25+ adapter methods, enabling efficient adaptation on limited GPU memory.
2
affaan-m pytorch-patterns
Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.
226k
orchestra-research gptq
Quantize large language models to 4-bit with minimal accuracy loss using GPTQ, enabling deployment of 70B+ models on consumer GPUs with 4× memory reduction and 3-4× faster inference.
10.4k · bundle
orchestra-research ml-training-recipes
Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.
10.4k · bundle
qhjqhj00 bss-eval
Evaluates speech language models on beyond-semantic speech attributes such as dialect comprehension, multi-turn context memory, emotion perception, age-aware response generation, and non-verbal cue handling, reporting accuracy and judge-based scores.
3
orchestra-research pytorch-fsdp2
Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.
10.4k · bundle
orchestra-research llama-cpp
Run LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
10.4k · bundle