{"id":"facebookresearch-lingua","name":"lingua","homepage":null,"repo_url":"https://github.com/facebookresearch/lingua","category":"ai-ml","subcategories":[],"tags":["ai-ml","pytorch","distributed-training","research","llm-training","slurm","checkpointing","profiling"],"what_it_does":"Meta Lingua (lingua) is a minimal, research-focused LLM training and inference codebase built on PyTorch, providing reusable components (models, data loading, distributed training, checkpointing, profiling) and example “apps” and configuration templates for end-to-end training/evaluation on SLURM or locally (e.g., via torchrun).","use_cases":["Researching and prototyping LLM pretraining architectures (e.g., Transformer variants, minGRU/minLSTM, Mamba-like blocks)","End-to-end training and evaluation pipelines for pretraining runs","Distributed training on multi-GPU clusters (FSDP/data/model parallel options)","Benchmarking training/inference speed and stability (profiling traces, MFU/HFU)","Experimentation with custom losses, data sources, and training recipes via easily modified PyTorch components"],"not_for":["Production API serving of LLMs as a hosted service","Turnkey fine-tuning/serving workflows with minimal ML engineering overhead","Compliance-heavy turnkey deployments that require strong packaging, documented operational guarantees, and hardened interfaces"],"best_when":"You have GPU/cluster access and want a modifiable research codebase to implement new training ideas with control over distributed strategy, data pipelines, and checkpoint formats.","avoid_when":"You need a simple public HTTP API/SDK for calling the model, or you require strongly documented operational semantics (SLA, error codes, stable backward-compatible APIs) rather than a research framework.","alternatives":["PyTorch TorchTitan","fairseq2","Hugging Face Transformers + Accelerate/DeepSpeed","Torchtune (fine-tuning-focused)","Lightning / Megatron-LM ecosystems (depending on scale and desired parallelism)"],"af_score":37.8,"security_score":45.5,"reliability_score":33.8,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T14:59:18.344774+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["Hugging Face access token for downloading tokenizer/data (via --api_key <HUGGINGFACE_TOKEN> in setup/download_tokenizer.py)"],"oauth":false,"scopes":false,"notes":"Authentication is limited to external tooling for dataset/tokenizer downloads (e.g., Hugging Face token). The training/eval interfaces shown are CLI/config driven rather than a remote service with first-class auth."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No service pricing described; it is an open-source training library where compute costs come from your infrastructure."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":37.8,"security_score":45.5,"reliability_score":33.8,"mcp_server_quality":0.0,"documentation_accuracy":55.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":90.0,"rate_limit_clarity":0.0,"tls_enforcement":100.0,"auth_strength":30.0,"scope_granularity":0.0,"dependency_hygiene":40.0,"secret_handling":60.0,"security_notes":"No network service is exposed by the library interface described; TLS is assumed for any HTTPS downloads. Authentication is only for third-party assets (Hugging Face token) and there is no documented fine-grained scope management. Secret handling quality is not directly verifiable from the provided README; since tokens are passed via CLI flags in setup scripts, care is needed to avoid leaking them in shell history/logs. Dependency hygiene (CVEs) and secure coding practices are not assessed from the provided content.","uptime_documented":0.0,"version_stability":50.0,"breaking_changes_history":30.0,"error_recovery":55.0,"idempotency_support":"false","idempotency_notes":"Some operational resilience is implied: jobs can be relaunched (sbatch submit.slurm) and checkpoints/train state are periodically saved, but no explicit idempotency guarantees for actions are documented in the README.","pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["This is not an API-based product; interactions are via CLI/Python entrypoints and SLURM workflows, which may require environment setup and GPU/distributed configuration.","Configuration templates require user adaptation (paths, dump_dir, tokenizer path, etc.), so automated agents must edit configs rather than rely on fully turnkey defaults.","Distributed training failures are likely; while relaunching via SLURM is mentioned, there is no structured, machine-readable error protocol described."]}}