Editing Openai/691c1dba-9228-800f-8463-13b3a9006306 (section)

=== Assistant: Hell yes — you can make real, ethical, local AI work with what you’ve got. ===
Hell yes — you can make real, ethical, local AI work with what you’ve got. You don’t need $10K to prove a point or run y.AI.y at first. You need a plan that respects the hardware, uses the right models, and protects your independence. Below I’ll give you a practical, no-nonsense playbook: what to run now on your i9-11900K + 2×2080S + 64GB RAM rig, how to get useful local LLM behavior (without waiting for cloud credits), and how to wire your y.AI.y stack (HPL, ingots, ZCP, persistence) into a robust, ethical local lab.

I’ll be blunt where it matters and specific where it helps. Let’s go.

==== - i9-11900K — great CPU for feeding data, running corpus preprocessing, and smaller models on CPU; strong single-thread performance. ====
* 2 × RTX 2080 Super — decent GPUs, each with ~8GB VRAM. Important: consumer GPUs do not combine VRAM for a single model (SLI is irrelevant for modern ML); you’ll generally use one GPU per model unless you run advanced model-parallel software (complex and often fragile).
* 64 GB RAM — very good; lets you run multiple processes and hold embeddings in memory comfortably.
* Storage: Prefer an NVMe SSD; if you don’t have one, get a 1TB NVMe when you can (cheap and massively helpful).

Bottom line: you can run 7B-class models comfortably (with quantization), and some 13B models if you use heavy quantization and GPU-CPU combination. You’ll be limited on 30B+ native runs unless you use CPU-only ggml with swapping (slow) or rent GPU for heavy lifts.

==== Tier 1 — Get working, cheap, fast (today) ====
* Run a 7B quantized model locally (LLaMA 2 7B, Mistral 7B, or GGML-converted Vicuna).
* Use llama.cpp / ggml builds (very low friction) or text-generation-webui for a web UI.

Tier 2 — More capability, still local
* Run 13B quantized (Q4_0/Q3) in GPU/CPU hybrid with llama.cpp CUDA build or GGML CUDA if available.
* Use vLLM / text-generation-webui with bitsandbytes or GPTQ (note hardware compatibility).

Tier 3 — Heavy experiments
* Rent GPU time for 30B+ experiments (spot instances) for specific UCF runs or training phases.

==== 1. OS: Ubuntu 22.04 (or WSL2/Ubuntu if you’re on Windows). ====
# Drivers: Latest NVIDIA drivers compatible with your 2080 Supers + CUDA 11.x/12.x. (Install through apt or NVIDIA installer).
# Python env: Miniconda.
# Core tools: - llama.cpp (for ggml CPU/GPU usage) — very lightweight, great for quantized models. - text-generation-webui — friendly UI + supports many backends (llama.cpp, GPTQ, bitsandbytes). - vLLM (optional later) — high-performance serving for GPU but needs CUDA+PyTorch setup. - QPTuning/GPTQ and gptq-for-llama — for quantizing models locally. - FAISS or Weaviate for embeddings/memory store. - sentence-transformers for embeddings (local CPU accelerated by Intel MKL).
# Optional wrappers: Ollama or LM Studio for UX, if you prefer GUIs — but these may be proprietary/paid.

==== 1. Install system basics ====

<syntaxhighlight lang="bash"># On Ubuntu
sudo apt update && sudo apt install -y build-essential git wget python3 python3-venv python3-pip
=== NVidia drivers (if not installed): follow distro-specific instructions ===

</syntaxhighlight>
# Create Python environment

<syntaxhighlight lang="bash">python3 -m venv ~/venvs/yaiy
source ~/venvs/yaiy/bin/activate
pip install --upgrade pip setuptools wheel

</syntaxhighlight>
# Clone text-generation-webui (fast multi-backend UI)

<syntaxhighlight lang="bash">git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt

</syntaxhighlight>
# Get a quantized 7B model
* Option A: If you have Hugging Face access: download a GGML/Q4_K_M or GPTQ quantized Llama 2 7B or Mistral 7B model. Place in models/your_model_name/ inside webui folder.
* Option B: Use a pre-quantized GGML file with llama.cpp.
# Run with llama.cpp backend (CPU/GPU)
* If using llama.cpp backed webui: start python server.py --model your_model_name --model_type llama and add flags like --load-in-8bit or --use-cuda depending on build.
# If using llama.cpp directly (no webui)

<syntaxhighlight lang="bash">git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
=== Build CPU + CUDA if you want ===
make
=== Put ggml-model-q4_0.bin in models/ ===
./main -m models/ggml-model-q4_0.bin -p "Hello"

</syntaxhighlight>

Result: A responsive local chat model, constrained but usable, runs on CPU or GPU with low VRAM.

==== - You can’t easily combine 8GB + 8GB VRAM for a single large model. Model parallelism exists (DeepSpeed ZeRO, Megatron-LM) but is complex, fragile, and often requires NVLink or enterprise hardware. ====
* Practical rule: Use one GPU for inference at a time. Use the other GPU as a worker for another process (embedding calc, caching, or a second smaller model).
* If you want to run larger models, you can: - Quantize aggressively (GPTQ/Q4_0/Q4_K) to fit 13B into ~8–10GB VRAM. - Use ggml CPU mode (slower) and let your NVMe paging help. - Rent a single A100/4090 hour from a spot provider when needed.

==== - Llama 2 7B (quantized) — best balance: fast + coherent. ====
* Mistral 7B — good quality; may be heavier.
* Vicuna 7B (fine-tuned) — chatty and follows instruction well.
* 13B Q4-level — possible with careful quantization & CPU+GPU fallback.
* Avoid: 70B+ locally—unless you rent cloud GPUs.

==== - Use GPTQ to quantize float16 -> 4-bit or 8-bit for big quality/size wins. ====
* Use llama.cpp / ggml CUDA builds to accelerate with your 2080 if compiled with CUDA.
* Use CPU for embeddings (sentence-transformers) if one GPU is needed for the model.
* Keep a fast NVMe scratch for model IO; this greatly speeds loads and temp memory.

==== 1. Memory store — use SQLite + FAISS (local). Example simple pattern: - Keep memory table with timestamp, ingot_hash, synopsis, embedding_vector. - Compute embedding (sentence-transformers) for any session log you want to save. - Store embedding in FAISS and metadata in SQLite. ====
# Carousel ingot generator (Python snippet)

<syntaxhighlight lang="python">import hashlib, json
def carousel_ingot(lines, title="session"):
    joined = "\n".join(lines).encode('utf-8')
    digest = hashlib.sha256(joined).hexdigest()[:32]
    synopsis = lines[-3:]
    return {"title": title, "hash": digest, "synopsis": synopsis}

</syntaxhighlight>
# ZCP guard (policies.py snippet)

<syntaxhighlight lang="python">def zcp_guard(fn):
    def wrapped(*args, **kwargs):
        result = fn(*args, **kwargs)
        assert isinstance(result, dict)
        # add checks – e.g. never claim actions, never return PII
        return result
    return wrapped

</syntaxhighlight>
# HPL Compiler / Runtime
* Keep your hpl/ files as human-readable manifests. Use your compiler.py to compile to JSON. Have runtime.py read those manifests and run steps on local models.
* Example: compiler.py → manifests/bridge_manifest.json → runtime.py executes steps via Python glue code calling the local LLM through the webui HTTP API or a direct python binding.

==== - Run everything locally on a machine with full-disk encryption (LUKS) if you can. ====
* Offline backups: export your ingots and manifests to encrypted USBs.
* Use local ACLs so you don’t accidentally expose your data.
* If you must use cloud for heavy runs, use short-lived instances and delete volumes after use. Don’t store keys in the cloud.

==== If you need to run 30B+ for a single test: ====
* Rent cheap spot GPU compute (Vast.ai, RunPod, CoreWeave, Lambda Labs) for an hour or two.
* Use that time to run your test, store ingot hash, and download transcripts locally.
* Don’t leave a long-lived cloud model running.

(I won’t quote prices here — they change — but spot pricing is often minutes-to-hours of cost vs $10K capital outlay.)

==== 1. Install prerequisites (drivers, Python). ====
# Clone and run text-generation-webui or llama.cpp.
# Download and place a quantized 7B model in models/.
# Start webui: python server.py --model <model_dir> --load-in-8bit (or appropriate flags).
# Hook runtime: expose a small Python script that calls local web UI API to send HPL-compiled steps and receives responses.
# Implement carousel_ingot() to store ingots with SHA256 into memory/ingots.json and FAISS index.
# Use ZCP guard wrappers for runtime functions.

==== <syntaxhighlight lang="python"># hpl_runner.py ====
import requests, json, sqlite3, hashlib, time
API = "http://127.0.0.1:5000/api/v1/generate"  # webui endpoint

def call_local_llm(prompt):
    resp = requests.post(API, json={"prompt": prompt, "max_new_tokens": 512})
    return resp.json()["text"]

def run_protocol(manifest, ctx):
    # minimal step runner
    log = []
    for step in manifest["steps"]:
        prompt = f"Step: {step['do']}\nContext: {ctx}\n"
        out = call_local_llm(prompt)
        log.append(out)
    ingot = carousel_ingot(log, title=manifest["title"])
    save_ingot_db(ingot)
    return ingot

</syntaxhighlight>

==== - Use text-generation-webui for quick experimentation: it supports role-play, API calls to scripts, and lets you save sessions. ====
* Keep a standardized HPL → JSON manifest workflow so you can replay experiments reproducibly.
* Version everything (Git), export ingots as immutable hashes.

==== 1. Produce an exact list of shell commands to get llama.cpp + text-generation-webui running on your rig, tuned for RTX 2080 Supers. ====
# Create a requirements.txt, runner script, and manifest template for your HPL → runtime glue.
# Generate a one-file Python ucf_local_lab.py that: - boots a small local model via llama.cpp (if present), - runs your Rainbow/Mirror protocols, - writes ingots to ingots.db (SQLite), - enforces ZCP guards.

Say “Do it” and I’ll drop the full runnable scripts and commands in the next reply (no fluff, copy/paste-ready).