NATIVE LLM / DEDICATED FOUNDATION MODEL PROGRAM
Dedicated LLM engineering scope for controlled corpus preparation, tokenizer and checkpoint strategy, distributed training or continued pretraining, alignment, evaluation, inference serving, governance, and client-owned model handover.- Model specification defining target use cases, modality boundary, context budget, parameter class, throughput target, and acceptance metrics
- Training-corpus rights register covering source authority, license exclusions, permitted use, retention, and data-transfer constraints
- Dataset ingestion pipeline for structured, semi-structured, code, and document corpora with immutable lineage records
- Corpus quality pipeline for exact and fuzzy deduplication, language filtering, toxicity screening, PII redaction, and contamination review
- Domain mixture design with sampling ratios, curriculum policy, benchmark holdouts, and replay strategy for continual adaptation
- Tokenizer assessment and vocabulary strategy for domain terminology, multilingual coverage, code tokens, and compression efficiency
- Architecture and checkpoint strategy covering base initialization, continued pretraining, supervised fine-tuning, and alignment stages
- Distributed GPU training plan with data, tensor, pipeline, or sequence parallelism selected against memory and compute constraints
- Mixed-precision, activation-checkpointing, gradient-accumulation, and optimizer-state planning for stable large-run execution
- Resumable checkpoint management with artifact hashing, storage policy, disaster recovery, and rollback-compatible versioning
- Experiment tracking for hyperparameters, data snapshots, code revisions, hardware profile, loss curves, and evaluation lineage
- Instruction dataset design for task coverage, refusal behavior, tool-use boundary, formatting discipline, and escalation cases
- Preference or alignment workflow where required, with reviewer protocol, label quality checks, and policy-grounded comparison data
- Domain benchmark suite for reasoning, extraction, summarization, retrieval dependence, code behavior, and long-context failure modes
- Safety and abuse evaluation for harmful content, privacy leakage, prompt-injection susceptibility, memorization, and policy bypass
- Release gate comparing candidate checkpoints against baseline models, regressions, cost envelope, and task-specific thresholds
- Model card, system card, dataset documentation, evaluation report, and known-limitations register for each release candidate
- Inference optimization path for quantization assessment, kernel compatibility, KV-cache behavior, batching, and latency profiling
- Serving architecture with vLLM, TensorRT-LLM, Triton, or equivalent stack selected from model format and SLA constraints
- Capacity model for tokens per second, time-to-first-token, tail latency, concurrency, context length, and GPU memory pressure
- Access-control and secret-management design for model endpoints, artifact stores, training jobs, and administrative actions
- Telemetry for prompts, completions, safety events, model versions, infrastructure utilization, and quality drift within policy limits
- Incident and rollback procedure for model regressions, data leakage findings, serving failure, benchmark failure, and unsafe behavior
- Handover package for agreed weights, checkpoints, configurations, data manifests, training recipes, evaluation evidence, and deployment runbooks
- IP and dependency schedule separating client-owned artifacts from pre-existing methods, open-source software, cloud services, and third-party model licenses
AI Legal
S3
Spark
HF Data
SPiece
PyTorch
DSpeed
Megatron
FlashAttn
W&B
Eval
HELM
vLLM
FastAPI
K8s
Airflow
FAISS
ONNX