lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
huggingface-tokenizers
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
nemo-curator
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16ร faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
obliteratus
Remove refusal behaviors from open-weight LLMs using OBLITERATUS โ mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
clip
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
segment-anything-model
Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
peft-fine-tuning
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
pytorch-fsdp
Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
simpo-training
Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.
slime-rl-training
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
fine-tuning-with-trl
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
bug-hunter
ๅๅธๅผๅคๆบ่ฝไฝ็ผบ้ทๆฃๆตๆปๆงๆ่ฝใๅบไบ่พๅ ฅ้ๆบๅใ่ง่ฒๅๅนถ่ก่ฏๅฎกใ่ฏญไนๆกถๅใๅ ๆๅ ฑ่ฏไธ่ฃๅณๅคๆ ธ่พๅบ้ซไฟกๅชๆฏไปฃ็ ่ฏๅฎกๆฅๅใ็จไบๅคง่งๆจก PRใๅคๆ้ป่พๅๆดใๅฎๅ จๆๆๆนๅจๆๅๆบ่ฝไฝ่ฏๅฎกๅฌๅ็ไธ่ถณ็ๅบๆฏใ
bug-hunter-stage2-parallel-review
bug-hunter ้ถๆฎต 2 ๆ่ฝใ่ด่ดฃๅฐ้ๆบๅๅ็ diff ๆ persona ็ฉ้ตๅๅ็ป 8 ไธชๅญๆบ่ฝไฝๅนถ่ก่ฏๅฎก๏ผๅนถๆถ้็ปไธ JSON ็ปๆใ
bug-hunter-stage3-evidence-fusion
bug-hunter ้ถๆฎต 3 ๆ่ฝใ่ด่ดฃๅฏนๅคๆบ่ฝไฝๅๅงๅ็ฐๅ่ฏญไนๅป้ใๆกถๅ่็ฑปไธๅฒ็ช่ฏๅซ๏ผๅฝขๆๅฏๆ็ฅจ็็ผบ้ทๅ้ๆฑ ใ
bug-hunter-stage4-consensus-judge
bug-hunter ้ถๆฎต 4 ๆ่ฝใ่ด่ดฃๅฏน็ผบ้ทๆกถๆง่กๅ ๆๅ ฑ่ฏๆ็ฅจ๏ผ็ญ้่ฟ้ๅผ้ฎ้ข๏ผๅนถ่พๅบ่ฃๅณ็บง็ปๆๅ่ฏๅฎกๆฅๅใ
generate-release-notes
Generate release notes for the new NNCF release.
numerai-experiment-design
Design and manage Numerai experiments in this repo for any model idea.
numerai-model-implementation
Add a new Numerai model type to the agents training pipeline. Use when you need to register a model in `agents/code/modeling/utils/model_factory.py`, handle fit/predict quirks in `agents/code/modeling/utils/numerai_cv.py`, and update configs so the model can run via `python -m agents.code.modeling`.