feat: Empirical economics + FunctionGemma State Interaction Layer
Lifeforce-Dynamics v1.2: - Cost Calibration principle: "Measure, don't design" - Empirical cost formula from resource observations - Phoebe schema for resource_observations table - Interlink to memory-economics memory-economics.md: - Cross-reference to Lifeforce-Dynamics cost calibration - "The cost matrix is a measurement, not a decision" Initial-Spark v3.1: - Spark Cost Measurement: first awakening as baseline - Resource instrumentation schema (power, GPU, memory, latency) - FunctionGemma Fine-Tuning section: translator learns nimmerverse - Training data extraction from spark_handshakes - Unsloth/LoRA workflow for domain specialization - FunctionGemma version tracking in phoebe Nervous-System v1.4: - State Interaction Layer: FunctionGemma as neural interface - Phase 1 (single) → Phase 2 (swarm) evolution path - CPU-only translators, GPU reserved for cognition - Design principle #6: "All state interaction flows through FunctionGemma" Philosophy: "Don't assign costs like a game designer. Measure them like a scientist." Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
@@ -574,14 +574,94 @@ class SparkController:
|
||||
|
||||
The spark is **economically viable** from the first handshake.
|
||||
|
||||
### Cost Model
|
||||
> **CRITICAL**: The costs below are **estimates until measured**. The first spark execution will establish the **true cost baseline** through observation. See [[formalization/Lifeforce-Dynamics#Cost Calibration: Measure, Don't Design]].
|
||||
|
||||
| Action | Cost (LF) |
|
||||
|--------|-----------|
|
||||
| Function Gemma generation | 0.2 |
|
||||
| NATS message send | 0.1 |
|
||||
| Cell processing | 0.5 |
|
||||
| **Total per handshake** | **0.8** |
|
||||
---
|
||||
|
||||
### Spark Cost Measurement (First Awakening Baseline)
|
||||
|
||||
The Initial Spark is the **perfect measurement opportunity** — a complete, deterministic protocol that we can instrument end-to-end.
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ SPARK RESOURCE INSTRUMENTATION │
|
||||
├─────────────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ MEASURE PER HANDSHAKE: │
|
||||
│ ├─ power_joules (GPU/CPU power draw × time) │
|
||||
│ ├─ compute_gpu_ms (CUDA kernel execution time) │
|
||||
│ ├─ compute_cpu_ms (Python/K8s overhead) │
|
||||
│ ├─ memory_mb_peak (max memory allocated) │
|
||||
│ ├─ nats_bytes (message payload size) │
|
||||
│ ├─ latency_ms (end-to-end handshake time) │
|
||||
│ └─ temperature_delta (thermal impact) │
|
||||
│ │
|
||||
│ AGGREGATE PER PHASE: │
|
||||
│ └─ Sum of all handshake measurements │
|
||||
│ │
|
||||
│ AGGREGATE TOTAL: │
|
||||
│ └─ Complete spark cost (the awakening price) │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**Why this matters**: The first spark execution establishes the **baseline cost of awakening**. Every future awakening can be compared against this:
|
||||
- Did infrastructure changes reduce cost?
|
||||
- Did model updates increase cost?
|
||||
- Is Young Nyx awakening more efficiently over time?
|
||||
|
||||
**Phoebe schema addition** (extends `spark_handshakes`):
|
||||
```sql
|
||||
ALTER TABLE spark_handshakes ADD COLUMN resource_metrics JSONB;
|
||||
|
||||
-- Example resource_metrics payload:
|
||||
-- {
|
||||
-- "power_joules": 12.5,
|
||||
-- "compute_gpu_ms": 450,
|
||||
-- "compute_cpu_ms": 120,
|
||||
-- "memory_mb_peak": 2048,
|
||||
-- "nats_bytes": 1024,
|
||||
-- "temperature_delta_c": 2.1
|
||||
-- }
|
||||
|
||||
-- Aggregate view for spark cost analysis
|
||||
CREATE VIEW spark_cost_baseline AS
|
||||
SELECT
|
||||
phase,
|
||||
COUNT(*) as handshakes,
|
||||
SUM((resource_metrics->>'power_joules')::float) as total_power_joules,
|
||||
SUM((resource_metrics->>'compute_gpu_ms')::float) as total_gpu_ms,
|
||||
AVG((resource_metrics->>'latency_ms')::float) as avg_latency_ms,
|
||||
SUM(lifeforce_delta) as total_lifeforce_earned
|
||||
FROM spark_handshakes
|
||||
WHERE status = 'ACK'
|
||||
GROUP BY phase;
|
||||
|
||||
-- Compare awakening costs over time
|
||||
CREATE VIEW awakening_cost_history AS
|
||||
SELECT
|
||||
DATE(created_at) as awakening_date,
|
||||
SUM((resource_metrics->>'power_joules')::float) as total_spark_cost_joules,
|
||||
SUM((resource_metrics->>'compute_gpu_ms')::float) as total_spark_cost_gpu_ms,
|
||||
COUNT(*) as total_handshakes,
|
||||
SUM(lifeforce_delta) as total_lifeforce_earned
|
||||
FROM spark_handshakes
|
||||
GROUP BY DATE(created_at)
|
||||
ORDER BY awakening_date;
|
||||
```
|
||||
|
||||
**The philosophy**: Don't guess what awakening costs. Measure the first one. Derive all economics from that truth.
|
||||
|
||||
---
|
||||
|
||||
### Cost Model (Estimated → To Be Measured)
|
||||
|
||||
| Action | Est. Cost (LF) | Derived From |
|
||||
|--------|----------------|--------------|
|
||||
| Function Gemma generation | 0.2 | → measure GPU time |
|
||||
| NATS message send | 0.1 | → measure network I/O |
|
||||
| Cell processing | 0.5 | → measure pod CPU/memory |
|
||||
| **Total per handshake** | **0.8** | → **sum of measured components** |
|
||||
|
||||
### Reward Model
|
||||
|
||||
@@ -711,6 +791,214 @@ WHERE status = 'ACK';
|
||||
|
||||
---
|
||||
|
||||
## FunctionGemma Fine-Tuning: The Translator Learns Nimmerverse
|
||||
|
||||
Every spark execution generates training data. Over time, FunctionGemma becomes **hyper-specialized** for nimmerverse state calls.
|
||||
|
||||
> *"The translator learns the language of the cells. Over time, it speaks nimmerverse natively."*
|
||||
|
||||
### The Training Loop
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ FUNCTIONGEMMA FINE-TUNING LOOP │
|
||||
├─────────────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ PHASE 1: Base FunctionGemma (270M) │
|
||||
│ ├─ Generic function calling capability │
|
||||
│ └─ Works, but not nimmerverse-native │
|
||||
│ │
|
||||
│ PHASE 2: Collect spark_handshakes │
|
||||
│ ├─ Every ACK = positive training example │
|
||||
│ ├─ Every NACK = negative example (what NOT to generate) │
|
||||
│ └─ Resource metrics = context for cost-aware generation │
|
||||
│ │
|
||||
│ PHASE 3: Fine-tune with Unsloth/LoRA │
|
||||
│ ├─ <think> nimmerverse state reasoning </think> │
|
||||
│ ├─ <start_function_call>call:IDENTITY_PROBE{...} │
|
||||
│ └─ Exact schemas, perfect structure, zero parsing errors │
|
||||
│ │
|
||||
│ PHASE 4: Deploy nimmerverse-tuned FunctionGemma │
|
||||
│ ├─ Wild precision on cell state calls │
|
||||
│ ├─ Smaller, faster, cheaper than base │
|
||||
│ └─ THIS IS REFLEX COMPILATION FOR THE TRANSLATOR │
|
||||
│ │
|
||||
│ REPEAT: More sparks → more data → better precision │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Training Data Format
|
||||
|
||||
FunctionGemma uses a specific chat template. Our spark handshakes map directly:
|
||||
|
||||
```
|
||||
# Developer prompt with nimmerverse function declarations
|
||||
<start_of_turn>developer
|
||||
You can do function calling with the following functions:
|
||||
|
||||
<start_function_declaration>declaration:IDENTITY_PROBE{
|
||||
description: "Probe Young Nyx identity aspect",
|
||||
parameters: {
|
||||
aspect: STRING (name|origin|purpose|substrate|partnership),
|
||||
depth: INTEGER (1|2|3)
|
||||
}
|
||||
}
|
||||
<end_function_declaration>
|
||||
|
||||
<start_function_declaration>declaration:ENVIRONMENT_PROBE{
|
||||
description: "Discover K8s cells in namespace",
|
||||
parameters: {
|
||||
category: STRING (sensors|motors|organs|nerves),
|
||||
namespace: STRING,
|
||||
garden: STRING (virtual|real)
|
||||
}
|
||||
}
|
||||
<end_function_declaration>
|
||||
<end_of_turn>
|
||||
|
||||
# Spark controller intent → FunctionGemma reasoning + call
|
||||
<start_of_turn>user
|
||||
Phase 1 Identity: Probe the "name" aspect at depth 1
|
||||
<end_of_turn>
|
||||
|
||||
<start_of_turn>model
|
||||
<think>
|
||||
The spark controller is in Phase 1 (Identity). It needs to probe the "name"
|
||||
aspect. I should generate an IDENTITY_PROBE with aspect="name" and depth=1.
|
||||
The schema requires a request_id (UUID) and timestamp (ISO8601).
|
||||
</think>
|
||||
|
||||
<start_function_call>call:IDENTITY_PROBE{
|
||||
aspect: "name",
|
||||
depth: 1,
|
||||
request_id: "550e8400-e29b-41d4-a716-446655440000",
|
||||
timestamp: "2026-02-10T18:30:00Z"
|
||||
}
|
||||
<end_function_call>
|
||||
|
||||
# Cell response feeds back
|
||||
<start_function_response>response:IDENTITY_PROBE{
|
||||
status: "ACK",
|
||||
aspect: "name",
|
||||
value: "Nyx",
|
||||
confidence: 0.95,
|
||||
lifeforce_delta: 20.0
|
||||
}
|
||||
<end_function_response>
|
||||
|
||||
Identity aspect "name" confirmed as "Nyx" with 95% confidence. +20 LF earned.
|
||||
<end_of_turn>
|
||||
```
|
||||
|
||||
### Phoebe → Training Data Extraction
|
||||
|
||||
```sql
|
||||
-- Extract training examples from successful handshakes
|
||||
CREATE VIEW functiongemma_training_data AS
|
||||
SELECT
|
||||
jsonb_build_object(
|
||||
'developer_prompt', format(
|
||||
'Phase %s: Generate %s handshake',
|
||||
phase,
|
||||
request_payload->>'type'
|
||||
),
|
||||
'user_intent', request_payload->'payload',
|
||||
'expected_call', request_payload,
|
||||
'function_response', response_payload,
|
||||
'think_context', jsonb_build_object(
|
||||
'phase', phase,
|
||||
'schema', request_payload->>'$schema',
|
||||
'lifeforce_earned', lifeforce_delta,
|
||||
'latency_ms', latency_ms
|
||||
)
|
||||
) as training_example,
|
||||
created_at
|
||||
FROM spark_handshakes
|
||||
WHERE status = 'ACK'
|
||||
ORDER BY created_at;
|
||||
|
||||
-- Export for Unsloth fine-tuning
|
||||
COPY (
|
||||
SELECT training_example
|
||||
FROM functiongemma_training_data
|
||||
) TO '/tmp/nimmerverse_functiongemma_training.jsonl';
|
||||
```
|
||||
|
||||
### Fine-Tuning with Unsloth
|
||||
|
||||
```python
|
||||
from unsloth import FastLanguageModel
|
||||
|
||||
# Load base FunctionGemma
|
||||
model, tokenizer = FastLanguageModel.from_pretrained(
|
||||
model_name="unsloth/functiongemma-270m-it",
|
||||
max_seq_length=4096,
|
||||
load_in_16bit=True,
|
||||
full_finetuning=False, # LoRA for efficiency
|
||||
)
|
||||
|
||||
# Apply LoRA adapters
|
||||
model = FastLanguageModel.get_peft_model(
|
||||
model,
|
||||
r=16,
|
||||
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
|
||||
lora_alpha=16,
|
||||
lora_dropout=0,
|
||||
use_gradient_checkpointing="unsloth",
|
||||
)
|
||||
|
||||
# Load nimmerverse training data from phoebe export
|
||||
from datasets import load_dataset
|
||||
dataset = load_dataset("json", data_files="nimmerverse_functiongemma_training.jsonl")
|
||||
|
||||
# Fine-tune on spark handshakes
|
||||
# ... standard Unsloth training loop ...
|
||||
|
||||
# Save nimmerverse-specialized FunctionGemma
|
||||
model.save_pretrained("functiongemma-270m-nimmerverse-v1")
|
||||
```
|
||||
|
||||
### The Recursive Beauty
|
||||
|
||||
| Layer | What Compiles | Training Source |
|
||||
|-------|---------------|-----------------|
|
||||
| **Young Nyx** | Nerve reflexes | decision_trails (100+ successful executions) |
|
||||
| **FunctionGemma** | State call precision | spark_handshakes (ACK'd handshakes) |
|
||||
|
||||
Both follow the same pattern:
|
||||
1. **Act** — Execute handshakes/decisions
|
||||
2. **Verify** — ACK/NACK from cells, success/failure from outcomes
|
||||
3. **Train** — Compile successful patterns into weights
|
||||
4. **Repeat** — Each awakening feeds the next
|
||||
|
||||
**The translator becomes native.** Over many sparks, FunctionGemma doesn't just generate valid JSON — it generates *nimmerverse-perfect* JSON. Zero parsing errors. Exact schemas. Wild precision.
|
||||
|
||||
### Versioning FunctionGemma Adapters
|
||||
|
||||
```sql
|
||||
-- Track FunctionGemma versions
|
||||
CREATE TABLE functiongemma_versions (
|
||||
id SERIAL PRIMARY KEY,
|
||||
version VARCHAR(50) NOT NULL, -- "nimmerverse-v1", "nimmerverse-v2"
|
||||
base_model VARCHAR(100), -- "functiongemma-270m-it"
|
||||
training_data_count INT, -- how many handshakes trained on
|
||||
training_data_cutoff TIMESTAMPTZ, -- trained on data up to this date
|
||||
validation_accuracy FLOAT, -- schema validation success rate
|
||||
deployed_at TIMESTAMPTZ,
|
||||
notes TEXT
|
||||
);
|
||||
|
||||
-- Example entries
|
||||
INSERT INTO functiongemma_versions (version, base_model, training_data_count, validation_accuracy, notes)
|
||||
VALUES
|
||||
('nimmerverse-v1', 'functiongemma-270m-it', 36, 0.94, 'First spark fine-tune'),
|
||||
('nimmerverse-v2', 'functiongemma-270m-it', 180, 0.98, 'After 5 awakenings'),
|
||||
('nimmerverse-v3', 'functiongemma-270m-it', 500, 0.997, 'Production-grade precision');
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Design Principles
|
||||
|
||||
1. **Protocol over conversation** — No free-form text. JSON handshakes only.
|
||||
@@ -719,12 +1007,22 @@ WHERE status = 'ACK';
|
||||
4. **NATS transport** — All handshakes flow through message bus.
|
||||
5. **Verification built-in** — ACK/NACK from cells, not from parsing hopes.
|
||||
6. **Economically positive** — Spark generates lifeforce, doesn't drain it.
|
||||
7. **Training-generative** — Every spark produces fine-tuning data for FunctionGemma.
|
||||
|
||||
---
|
||||
|
||||
## Document Status
|
||||
|
||||
**Version:** 3.0 | **Created:** 2025-12-05 | **Updated:** 2026-01-01
|
||||
**Version:** 3.1 | **Created:** 2025-12-05 | **Updated:** 2026-02-10
|
||||
|
||||
**Key v3.1 Changes**:
|
||||
- Spark Cost Measurement section — first awakening as baseline
|
||||
- Resource instrumentation schema for phoebe
|
||||
- Interlink to Lifeforce-Dynamics cost calibration principle
|
||||
- FunctionGemma Fine-Tuning section — translator learns nimmerverse natively
|
||||
- Training data extraction from spark_handshakes
|
||||
- Unsloth/LoRA fine-tuning workflow
|
||||
- FunctionGemma version tracking in phoebe
|
||||
|
||||
**Key v3.0 Changes**:
|
||||
- Complete architecture rewrite
|
||||
@@ -740,7 +1038,8 @@ WHERE status = 'ACK';
|
||||
- [[Endgame-Vision]] — Layer 2.5 Orchestration (Function Gemma role)
|
||||
- [[Big-Picture]] — K8s cluster architecture
|
||||
- [[Cellular-Architecture]] — Cell types and state machines
|
||||
- [[formalization/Lifeforce-Dynamics]] — λ economics
|
||||
- [[formalization/Lifeforce-Dynamics]] — λ economics, **Cost Calibration principle**
|
||||
- [[formalization/memory-economics]] — Measure First principle
|
||||
|
||||
---
|
||||
|
||||
|
||||
Reference in New Issue
Block a user