Facingsouth

Quick Start Generate LoRA Adapters From metadata (JSON string or file):

tessera generate \ --from-metadata '{"task": "classification", "domain": "medical"}' \ --base-model mistralai/Mistral-7B-Instruct-v0.2 \ --rank 16 \ --save ./adapter.safetensors

From text description:

tessera generate \ --from-text "Medical diagnosis assistant" \ --base-model mistralai/Mistral-7B-Instruct-v0.2 \ --rank 16 \ --save ./adapter.safetensors

From document:

tessera generate \ --from-doc ./document.txt \ --base-model mistralai/Mistral-7B-Instruct-v0.2 \ --rank 16 \ --save ./adapter.safetensors

Base Model Management

Download a base model from HuggingFace Hub:

tessera model pull mistralai/Mistral-7B-Instruct-v0.2 tessera model pull meta-llama/Llama-3.1-8B-Instruct tessera model pull deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Start vLLM with a base model:

tessera model serve-model mistralai/Mistral-7B-Instruct-v0.2 --port 8000 tessera model serve-model mistralai/Mistral-7B-Instruct-v0.2 --gpu-memory-utilization 0.9 tessera model serve-model mistralai/Mistral-7B-Instruct-v0.2 --quantization awq

List cached base models:

tessera model list-models

Remove a cached model:

tessera model remove mistralai/Mistral-7B-Instruct-v0.2

Start Tessera Server

Start the hypernetwork server (with auto vLLM):

tessera serve --port 8080 --base-model mistralai/Mistral-7B-Instruct-v0.2

Start the hypernetwork server (standalone):

tessera serve --port 8080 --host 0.0.0.0 Check Server Health tessera health --url http://localhost:8080

List Available Models

tessera list

Performance

2-8x throughput improvements with vLLM optimization

30-50% bandwidth penalty eliminated with NUMA topology

2-5x CUDA Graph speedup with optimal topology

Up to 90% cost savings with automatic provider switching

<2 minute spot recovery with KV cache checkpointing

Up to 3x faster cold starts with weight streaming

Up to 50% cost savings with MLA-aware VRAM estimation

Float Protocols is a primitive that bridges existing dead zone communication systems (Iridium, Inmarsat, VSAT, HF/VHF, RockBLOCK) to future satellite networks. Currently supports traditional satellite protocols with future-proofing for AST SpaceMobile's enterprise IoT APIs when released. The system integrates with telemetry for accurate ping monitoring.

1. The "Invisible Blocking" Problem

* The Problem: In Python's asyncio, the entire loop runs on a single thread. If one function performs a heavy calculation (like a complex spatial hash or a large HMAC verification), it "blocks" the loop. Every other truck event in the system has to wait. * Our Solution (Rust Acceleration): We offload those "CPU-heavy" tasks to Rust. By using maturin and pyo3, we execute the heavy lifting in Rust code that releases the Global Interpreter Lock (GIL). The Python loop stays snappy, only handling the "IO orchestration" while Rust does the "math."

2. The "Backpressure" & Memory Leak Problem

* The Problem: If 50,000 trucks send webhooks at once, a naive async bridge will try to open 50,000 coroutines. This leads to unbounded memory growth and eventually a crash. * Our Solution (Redis Streams & Adaptive Backpressure): * Redis Streams: Acts as our "Buffer Tank." The API accepts the webhook and immediately pushes it to Redis. It doesn't care if the worker is ready yet. * Adaptive Backpressure: Your core/adaptive_backpressure.py monitors Redis latency. If the system is getting overwhelmed, it throttles the ingest layer. It's better to tell a vendor "Try again in 5 seconds" than to crash and lose everyone's data.

3. The "Race Condition" & Determinism Problem

* The Problem: Async doesn't mean "Parallel," but it does mean "Out of Order." If Truck Update #2 arrives before Truck Update #1 (common in dead zones), a standard system might overwrite the new location with the old one. * Our Solution (Stator's Latch & Vectorization): * Stator's Latch: We use a Redis-backed Event-Time Latch. If an event arrives with a timestamp older than the one we've already "committed" to the state, we flag it as "historical" and don't trigger real-time alerts. * Vectors: By describing events as vectors (as you suggested), we aren't just comparing points. We are looking at the trajectory. If a point is geographically or temporally impossible, the Rust-powered spatial checks catch it before it corrupts the state.

4. The "Zombie Task" Problem

* The Problem: In async, tasks can fail silently or run forever in the background without anyone knowing (unawaited coroutines). * Our Solution (OTel Tracing): Because every Mandala event is a trace, we can see exactly where an event "died." If a webhook comes in but never hits the worker, the trace in Jaeger will show a broken span. We aren't guessing; we're observing the Lifecycle of the Event.

COMMANDS:

# Install & setup pip install terradev-cli terradev configure --provider runpod

# Price discovery (19 clouds) terradev quote -g H100 terradev quote -g A100 --max-price 2.50

# Provision with auto topology optimization terradev provision -g H100 -n 4 --parallel 6 terradev provision -g A100 --dry-run terradev run --gpu H100 --image pytorch/pytorch:latest

# Instance management terradev status --live terradev manage -i <id> -a stop terradev analytics --days 30 terradev optimize Training Pipeline bash # Pre-flight validation terradev preflight

# Launch training terradev train --script train.py --from-provision latest terradev train-status terradev monitor --job my-job terradev checkpoint list --job my-job Inference Optimization bash # vLLM auto-tuning (6 critical knobs) terradev vllm auto-optimize -s workload.json -m meta-llama/Llama-2-7b-hf -g 4 terradev vllm analyze -e http://localhost:8000 terradev vllm benchmark -e http://localhost:8000 -c 10

# MoE deployment with auto-optimizations terradev provision --task clusters/moe-template/task.yaml \ --set model_id=Qwen/Qwen3.5-397B-A17B

# Disaggregated prefill/decode terradev ml ray --deploy-pd --model zai-org/GLM-5-FP8 \ --prefill-tp 8 --decode-tp 1 --decode-dp 24

# LoRA adapters (hot-load on running endpoint) terradev lora add -e http://endpoint:8000 -n customer-a -p /adapters/a terradev lora list -e http://endpoint:8000 terradev lora remove -e http://endpoint:8000 -n customer-a Kubernetes bash # Topology-optimized clusters terradev k8s create my-cluster --gpu H100 --count 8 --prefer-spot terradev k8s list terradev k8s info my-cluster terradev k8s destroy my-cluster Secondary Features bash # HF Spaces (one-click deployment) terradev hf-space my-llama --model-id meta-llama/Llama-2-7b-hf --template llm

# InferX serverless (<2s cold starts) terradev inferx deploy --endpoint my-api --model-id meta-llama/Llama-2-7b-hf terradev inferx status --endpoint my-api

# Observability & Safety terradev phoenix deploy --project my-inference terradev phoenix spans --project my-inference --limit 100 terradev qdrant create-collection --name docs --vector-size 1536 terradev guardrails generate-config --enable-topical --enable-pii

# GitOps automation terradev gitops init --provider github --repo my-org/infra --tool argocd terradev gitops sync --cluster production

# Integrations (BYOAPI) terradev configure --provider wandb --api-key $WANDB_KEY terradev configure --provider prometheus --api-key $PROMETHEUS_URL Quick Workflows bash # 5-minute GPU setup pip install terradev-cli && terradev setup runpod --quick && \ terradev quote -g H100 && terradev run --gpu H100 --image pytorch/pytorch:latest

# Production RAG pipeline terradev qdrant k8s --namespace rag && \ terradev qdrant create-collection --name kb --vector-size 1536 && \ terradev provision --task clusters/moe-template/task.yaml \ --set model_id=Qwen/Qwen3.5-397B-A17B && \ terradev phoenix deploy --project rag-pipeline

# Multi-cloud cost optimization terradev analytics --days 30 && terradev optimize

Key Features: Auto NUMA/RDMA topology optimization, 19-cloud price comparison, vLLM auto-tuning, disaggregated P/D, LoRA hot-loading, BYOAPI security model.