# Hermes Profiles for TurboQuant

This directory contains Hermes configuration profiles for running models with TurboQuant KV cache compression.

## Available Profiles

### gemma4-turboquant.yaml

**Profile for Gemma 4 model with TurboQuant KV cache compression.**

- **Primary Provider:** Local llama.cpp server with TurboQuant enabled
- **Endpoint:** http://localhost:8081
- **KV Compression:** turbo4 (4-bit PolarQuant)
- **Context Length:** 128K tokens
- **Memory Savings:** ~73% KV cache reduction
- **Fallback Providers:** Ollama, OpenAI-compatible API

## Quick Start

### 1. Build TurboQuant-enabled llama.cpp

```bash
git clone https://github.com/TheTom/llama-cpp-turboquant.git
cd llama-cpp-turboquant
git checkout feature/turboquant-kv-cache
cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(sysctl -n hw.ncpu)
```

### 2. Download Gemma 4 Model

```bash
# Download Gemma 4 Q4_K_M quantized model
huggingface-cli download <model-repo> gemma-4-q4_k_m.gguf
```

### 3. Start llama-server with TurboQuant

```bash
export TURBO_LAYER_ADAPTIVE=7
./build/bin/llama-server \
  -m /path/to/gemma-4-q4_k_m.gguf \
  --port 8081 \
  -ctk turbo4 -ctv turbo4 \
  -c 131072 \
  --host 0.0.0.0
```

### 4. Install Profile

```bash
# Copy profile to Hermes directory
cp gemma4-turboquant.yaml ~/.hermes/profiles/

# Or create symlink
ln -sf $(pwd)/gemma4-turboquant.yaml ~/.hermes/profiles/
```

### 5. Use with Hermes

```bash
# Start Hermes with the profile
hermes --profile gemma4-turboquant

# Or specify profile in Hermes config
echo "default_profile: gemma4-turboquant" >> ~/.hermes/config.yaml
```

## Profile Configuration

The profile includes:

- **Primary Provider:** Local llama.cpp server with TurboQuant
- **Fallback Providers:** Ollama (local), OpenAI (cloud)
- **TurboQuant Settings:**
  - `kv_type`: turbo4 (4-bit compression)
  - `layer_adaptive_mode`: 7 (best quality/compression ratio)
  - `max_context`: 128K tokens

## Performance Expectations

| Metric | Value | Notes |
|--------|-------|-------|
| KV Memory Savings | 73% | Measured on M3 Max |
| Prompt Processing | ~1% overhead | vs FP16 baseline |
| Generation Speed | ~11% overhead | vs FP16 baseline |
| Max Context (36GB) | 128K | Comfortable with 7.6GB headroom |

## Customization

### Adjust Compression Level

```yaml
turboquant:
  kv_type: "turbo3"  # Lower compression, faster
  # or
  kv_type: "turbo2"  # Minimal compression, fastest
```

### Disable Per-Layer Adaptive

```yaml
turboquant:
  layer_adaptive_mode: 0  # Uniform quantization
```

### Use Asymmetric K/V

For better quality on sensitive models:

```bash
# Start server with asymmetric K/V
llama-server -m model.gguf --port 8081 -ctk q8_0 -ctv turbo4 -c 131072
```

## Troubleshooting

### Server Won't Start

1. Check if port 8081 is available: `lsof -i :8081`
2. Verify model path is correct
3. Ensure TurboQuant branch is checked out

### Poor Generation Quality

1. Try `turbo3` instead of `turbo4`
2. Disable per-layer adaptive (mode 0)
3. Use asymmetric K/V: `-ctk q8_0 -ctv turbo4`

### High Memory Usage

1. Reduce context length: `-c 65536` (64K)
2. Check `TURBO_LAYER_ADAPTIVE` is set
3. Monitor with: `vmmap --summary $(pgrep llama-server)`

## References

- [TurboQuant Build Spec](../BUILD-SPEC.md)
- [Phase 1 Report](../PHASE1-REPORT.md)
- [Full Knowledge Transfer](../FULL-REPORT.md)
- [llama.cpp TurboQuant Fork](https://github.com/TheTom/llama-cpp-turboquant)