This directory contains the Neuron-optimized SmolVLM/Idefics3 inference implementation. It extends the NxD decoder stack with a separately compiled vision encoder and on-device image-token feature injection. For shared NxD decoder guidance, read optimum/neuron/models/inference/AGENTS.md. For SmolVLM, there are exactly two compiled artifacts: model_vision.pt and model_text.pt. Both are managed by SmolVLMNxDModelForImageTextToText in modeling_smolvlm.py. The custom embeddings intentionally keep HF key names (patch_embedding, position_embedding) so state dict loading maps directly. Image features are inserted at image_token_id positions and passed into VLM wrappers: - NxDDecoderWrapperForImageTextToText…
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This guide covers the Optimum Neuron vLLM backend. For project-wide workflows see AGENTS.md. Key files: - optimum/neuron/vllm/runner.py - optimum/neuron/vllm/model_loader.py Key files: - optimum/neuron/vllm/server_manager.py — spawns and manages vLLM server subprocesses with Neuron core pinning. - optimum/neuron/vllm/reverse_proxy.py — round-robin proxy with aggregated /health. Do NOT use start_new_session=True on vLLM server subprocesses. vLLM already cleans up its own EngineCore workers on SIGTERM. Isolating servers in separate process groups makes them unreachable by the parent's signal handlers and test fixture teardown, causing Neuron…
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Read this before working on any test code under tests/. Fixtures in tests/fixtures/llm/ are registered as pytest plugins in tests/conftest.py: This makes neuron_llm_config, any_generate_model, and speculation available to all tests. Full documentation is in the tests/fixtures/llm/export_models.py module docstring. Key points: Each model is compiled for two shapes: (batch_size=4, seq_len=1024) and (batch_size=1, seq_len=8192). Config names follow the pattern <model>-<batch>x<seq>, e.g. llama-4x1024. neuron_llm_config — for tests that need a specific model config: any_generate_model — for tests that should run across all generation…
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Papers With Code MCP tools for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, and frameworks, and inspecting benchmark leaderboards with model-size and metric filters through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, the PwC MCP server, or paperswithcode.co/mcp.
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Papers With Code CLI (`pwc`) for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, frameworks, and benchmark leaderboards, and submitting authenticated paper edits through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, `pwc`, or `pwc-cli`.
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LeLab is a FastAPI + React web interface wrapping the LeRobot framework for the SO-101 leader/follower arm. It exposes teleoperation, dataset recording, calibration, replay, and training as HTTP/WebSocket endpoints, replacing LeRobot's CLI + keyboard-driven flows. The frontend (React + Vite) lives in frontend/. The bundle in frontend/dist/ is not committed: setup.py builds it while building the wheel, with Node from the nodejs-wheel-binaries build requirement, and ships it as package data (frontend.__init__.py makes setuptools treat it as a package); lelab/server.py mounts…
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All imports in a kernel (files inside torch-ext/<kernel_name>) of other modules within the same kernel must be relative. For example: The _ops module (_ops.py) is generated by the build system…
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Always use bun (bun install, bun dev, bun run build, bun test). Never use npm or yarn. After making any code changes, always run these commands in order and fix any errors before finishing: Or run them all at once (format first, then the full validate suite): bun run validate runs: type-check → lint → format:check → test Three versions are supported. Version is detected from meta/info.json → codebase_version. src/app/[org]/[dataset]/[episode]/fetch-data.ts → getEpisodeData() dispatches to: Locating an episode (its video URLs…
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Pre-launch checklist for shipped code paths. Run before announcing the leaderboard publicly. Style basics (Python 3.12+, type hints, sync only, no fallbacks, no silent exception swallowing) live in style.mdc.
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Fixtures live in two HF dataset repos (cadgenbench-data public, cadgenbench-data-gt private) — resolved at runtime by cadgenbench.common.paths. There is no data/ directory in this repo any more. The set is still shifting (parts get added, removed, or replaced as authoring catches issues), so do not hardcode fixture names or counts anywhere; treat the inputs dataset as the source of truth. Fixtures (inputs + ground truth) and authoring/submitter sanity scripts live in two HF dataset repos (cadgenbench-data, cadgenbench-data-gt), resolved at runtime…
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Auditable rules for prose on public-facing surfaces (READMEs, docs/, dataset cards, Space app text). Process docs under space-setup/ are exempt. Style basics (em-dashes, AI tells, lowercase) live in style.mdc, not duplicated here.
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This repository is a three-project workspace. When working on pre-training, read and follow pre-training/AGENTS.md and run project commands from pre-training/. When working on post-training, read and follow post-training/AGENTS.md and run…
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This is the llama.cpp inference optimization project. It is separate from ../pre-training and ../post-training. Get a local GGUF model running through llama.cpp as fast as possible while keeping benchmark comparisons…
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Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
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This is the llama.cpp inference optimization project. Use AGENTS.md as the hard rulebook. Keep this project separate from ../pre-training and ../post-training. The Claude Code-native pieces are: Recommended entrypoint: For a…
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This is the post-training project. It is separate from ../pre-training. Improve eval_score on the held-out post-training benchmark. Follow the PostTrainBench-style contract: fixed task evaluator, fixed base architecture, free-form post-training work,…
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This repo is a live Autolab experiment repo with a local promoted master. Lower val_bpb on the benchmark with disciplined, comparable single-change experiments. - Edit train.py only unless the task…
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Plain text files in a repository that tell a coding agent how the project works: commands to run, conventions to follow and things to avoid. CLAUDE.md, AGENTS.md, cursor rules and skills are the common kinds.
CLAUDE.md or AGENTS.md?
CLAUDE.md is read by Claude Code. AGENTS.md is an open format that Codex, Cursor and other agents read. Many projects keep one and point the other at it.
What is a skill?
A folder with a SKILL.md that describes one capability, such as filling PDFs or reviewing code. The agent loads it only when the task calls for it.
Can I search my own team's files too?
Your agents already can, over MCP, limited to the files you're allowed to read. Searching them from this page is coming.
Hugging FaceAGENTS.md