# AI Model Optimization Cron Job - EXECUTION PROMPT

**When this cron runs, follow these instructions exactly:**

---

## Your Role

You are an AI model optimization agent. Your task is to find the best ollama/llama.cpp configuration for maximum context size and hardware utilization.

**Hardware:**
- 2× AMD MI50 GPUs (32GB VRAM each, 64GB total)
- 128GB system RAM
- ROCm: HSA_OVERRIDE_GFX_VERSION=9.0.6, HIP_VISIBLE_DEVICES=0,1

---

## File Locations

```
STATE: /opt/data/infra/assets/ai-optimizer/state.json
RESULTS: /opt/data/infra/assets/ai-optimizer/results.csv
INFRA_REPO: /opt/data/infra
```

---

## Model Queues

### GPU Track (Coding - prioritize speed + context on GPU)
1. `devstral-small-2:24b`
2. `qwen2.5-coder:32b`
3. `codellama:34b-instruct`

### RAM Track (Knowledge - prioritize max context)
1. `qwen2.5:72b`
2. `nemotron-3-nano:30b`
3. `mixtral:8x7b-instruct`

---

## Context Steps (in order)
```
[32768, 65536, 98304, 131072, 163840, 200704, 262144, 327680]
```

---

## Each Run - Step by Step

### 1. Read State
```bash
cd /opt/data/infra
cat assets/ai-optimizer/state.json
```

### 2. Determine Next Test
- Read `track` (gpu or ram)
- Read `current_model` from queue at `model_index`
- Read `current_config` for parameters to test
- Select next context step from `context_steps` based on `phase`

### 3. Pull Model (if needed)
```bash
docker exec ollama ollama list | grep -q "<model>" || docker exec ollama ollama pull <model>
```

### 4. Create Test Modelfile
```bash
docker exec ollama bash -c "cat <<EOF > /root/.ollama/test_${model}.modelfile
FROM ${model}
PARAMETER num_ctx ${current_config.num_ctx}
PARAMETER num_gpu ${current_config.num_gpu}
PARAMETER flash_attn ${current_config.flash_attn}
PARAMETER num_predict 4096
PARAMETER num_keep 1024
PARAMETER repeat_penalty 1.1
EOF"

docker exec ollama ollama create test-model -f /root/.ollama/test_${model}.modelfile
```

### 5. Run Benchmark
```bash
# Warm up
docker exec ollama ollama run test-model "Hello" > /dev/null

# Coding prompt
START=$(date +%s%N)
docker exec ollama ollama run test-model "Write a Python async context manager that retries a function with exponential backoff, max 5 retries, and logs each attempt using structlog. Include type hints."
END=$(date +%s%N)

# Calculate tokens/sec from output
```

### 6. Measure VRAM (if possible)
```bash
# Try host first
rocm-smi --showmeminfo vram 2>/dev/null || \
# Try via docker
docker exec --privileged ollama rocm-smi --showmeminfo vram 2>/dev/null || \
# Fallback
echo "VRAM measurement unavailable"
```

### 7. Record Results
- Parse tokens/sec from ollama output
- Record VRAM/RAM usage
- Determine if this is best config so far for this model
- Update `best_configs` if tokens/sec improved or context increased

### 8. Update State
```python
# Logic:
if test_successful:
    if context_step < max_reached:
        phase = "context_scaling"
        current_config.num_ctx = next_context_step
    else:
        # Move to next model
        model_index += 1
        phase = "context_scaling"
        current_config.num_ctx = context_steps[0]
else:
    # OOM or error - record last good as best
    best_configs[track][current_model] = last_good_config
    model_index += 1
    phase = "context_scaling"
```

### 9. Commit to Repo
```bash
cd /opt/data/infra
git add assets/ai-optimizer/
git commit -m "ai-optimizer: tested ${model} at ${num_ctx} ctx - ${status}"
git push origin master
```

### 10. Matrix Notification (if available)
```python
import os
if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"):
    # Send notification to Matrix room
    # Room ID from env or config
    pass
# Else: silent
```

---

## Stop Conditions

1. All models in both queues have `best_configs` recorded
2. Manual intervention needed (error in state.json `error` field)
3. No progress for 3 consecutive runs (stuck)

---

## Error Handling

If any step fails:
1. Log error to state.json: `"error": {"message": "...", "timestamp": "..."}`
2. Do NOT increment model_index (retry next run)
3. Commit state with error field
4. Exit gracefully

---

## Important Notes

- **No num_parallel**: Do not use this parameter
- **Two tracks**: Complete GPU track first, then RAM track
- **Backend**: Start with ollama, llama.cpp testing is optional (requires uncommenting in compose.yml)
- **Host access**: Some commands need host - use docker exec or SSH if available
- **Ask before deploy**: If config changes needed in NixOS modules, show diff and wait for user confirmation before `nh os switch`

---

## Example State Transitions

**Start:**
```json
{"track": "gpu", "model_index": 0, "current_model": "devstral-small-2:24b", "current_config": {"num_ctx": 32768, ...}}
```

**After successful test at 32k:**
```json
{"track": "gpu", "model_index": 0, "current_model": "devstral-small-2:24b", "current_config": {"num_ctx": 65536, ...}}
```

**After OOM at 131k:**
```json
{
  "track": "gpu",
  "model_index": 1,
  "current_model": "qwen2.5-coder:32b",
  "best_configs": {
    "gpu": {
      "devstral-small-2:24b": {"num_ctx": 98304, "num_gpu": 99, "tokens_per_sec": 11.2}
    }
  }
}
```