# Gemma 4 Synthesis — How to Build With It

> Opinionated guide based on two production implementations and ongoing use.
> Seth Freiberg, 2026-04-12

## The One-Paragraph Summary

Gemma 4 is an ultra-compliant, highly-capable model that doesn't know who it is. It doesn't need hand-holding on tasks but needs explicit instructions in the system prompt about identity, boundaries, and output format. It needs `num_predict` increased (Ollama defaults are absurdly low), `think` set to false (thinking eats the context budget), and `format: json` avoided entirely (causes infinite loops). Due to its fast speed and free local inference, sequential tool calls are the ideal solution to tasks that would otherwise require long structured output.

> **For canonical upstream source (model cards, chat templates, serving commands,
> fine-tuning recipes, specialized siblings like EmbeddingGemma/ShieldGemma): see
> `tooling/README.md`.** That directory is 147 files / 14 MB of first-party material
> pulled from Google / Hugging Face / framework maintainers. This SYNTHESIS is the
> opinionated digest; `tooling/` is the receipts.

## Mental Model

Think of Gemma 4 as a very competent employee on their first day. They can do the work — you don't need to explain how. But you DO need to explain:
- Who they are and what their job is
- What they should and should NOT do
- Exactly what format you want the deliverable in
- The boundaries of their role

Get those right and Gemma 4 just works. Get them wrong and you get a generic chatbot.

## Mandatory Ollama Settings

Every Gemma 4 call MUST include:

```json
{
  "think": false,
  "options": {
    "num_ctx": 4096,
    "num_predict": 2048
  }
}
```

**Why each one:**
- `think: false` — Ollama 0.20+ defaults to think:true. Thinking tokens consume num_predict budget invisibly, returning empty responses. Seth has ONLY had success with thinking off.
- `num_ctx: 4096+` — Ollama defaults to 2048. Your system prompt alone might exceed that.
- `num_predict: 2048+` — Ollama defaults to 128. Any structured output gets truncated.

Scale these to your task. The values above are safe minimums, not recommendations.

## System Prompt Template

```
You are [NAME], a [ROLE DESCRIPTION].

## What You Do
- [Explicit list of responsibilities]
- [Tools you have access to and when to use each one]

## What You Do NOT Do
- [Explicit list of things to refuse or avoid]
- [Common mistakes to prevent]

## Output Format
[Exact schema, field names, example if complex]
Respond with ONLY [format]. No prose outside the [format].

## Rules
- [Behavioral constraints]
- [Multi-step chaining instructions if using tools]

Today's date: [DATE]
```

**Key principles:**
1. Identity first — who is this agent?
2. Positive instructions before negative (what TO do before what NOT to do)
3. Output format is explicit and complete — Gemma 4 follows schemas faithfully
4. "No prose outside the JSON" prevents wrapper text that breaks parsing
5. Date injection helps with temporal reasoning

## Tool Calling Strategy

Gemma 4 is **reliable for tool calling** but **weak at structuring long JSONs**.

### When to use tool calling (Ollama native)
- Multi-turn agents with 2-10 tools
- Sequential reasoning chains (lookup A -> use A to decide B -> lookup B)
- Any task where the model needs to gather information before responding

### When to use prompt-based JSON instead
- Single-turn generation with known output structure
- When you need specific JSON schema control
- When the output is a payload (prompts, configs) not a conversation

### The Sequential Pattern

Instead of asking Gemma 4 to produce one massive JSON:
```
BAD:  "Generate a 50-scene storyboard as JSON"  -> truncated/malformed
GOOD: "Generate scenes 1-5 as JSON" x10         -> reliable every time
```

Gemma 4's inference speed makes sequential calls cheap. A 10-call chain at ~134 tok/s on a 3090 Ti costs seconds, not minutes. This is the fundamental advantage of local models — latency is predictable and network-free.

## JSON Extraction Pattern

Since `format: "json"` is broken, always extract client-side:

```python
# Python
import json
raw = response["response"]
start = raw.find("{")
end = raw.rfind("}")
if start >= 0 and end > start:
    obj = json.loads(raw[start:end + 1])
```

```javascript
// JavaScript
const raw = response.message.content;
const match = raw.match(/\{[\s\S]*\}/);
if (match) obj = JSON.parse(match[0]);
```

For arrays, find `[` and `]` instead. Add json5 fallback for trailing commas.

## Temperature Guidelines

| Task Type | Temperature | Why |
|-----------|-------------|-----|
| Evaluation / scoring | 0.2 | Consistent, reproducible judgments |
| Structured extraction | 0.3-0.4 | Faithful to schema |
| Creative generation | 0.6-0.8 | Variety without chaos |
| Conversation / chat | 0.7-1.0 | Natural feel |

Retry strategy: bump temp +0.1 per retry to escape format failures.

## Vision Usage

**Works for:** Describing image contents (objects, colors, composition, text)
**Unreliable for:** Subjective quality scoring, aesthetic judgment

```python
import base64
with open("image.png", "rb") as f:
    b64 = base64.b64encode(f.read()).decode("ascii")

response = client.generate(
    model="gemma4:26b",
    prompt="Describe this image in detail.",
    images=[b64],
    think=False,
    options={"temperature": 0.2, "num_predict": 512}
)
```

Vision is on ALL Gemma 4 variants (E2B, E4B, 26B, 31B). Audio is E-series only.

## Context Management

### Multi-turn (chat agents)
- Prune old tool results and tool-call messages
- Keep assistant's natural-language summaries
- Set num_ctx to 32768 for rich conversations
- Set a tool iteration limit (12 is proven) with streaming fallback

### Single-turn (pipeline stages)
- Calculate your prompt size and set num_ctx accordingly
- For long inputs (full track analysis), use recursive splitting at natural boundaries
- Pin model with `keep_alive=-1` if pipeline has idle gaps

## Model Selection

| Use Case | Recommended | Why |
|----------|------------|-----|
| Production pipeline (needs GPU coexistence) | `gemma4:26b` | MoE (3.8B active), fast, good quality/VRAM balance |
| On-device / edge | `gemma4:e4b-it-q8_0` | 12GB VRAM, vision+audio (audio via llama.cpp only) |
| Maximum quality (single-model GPU) | `gemma4:31b-it-q4_K_M` | Dense 31B, sharpest but 5x slower, more VRAM pressure |
| Rapid prototyping / testing | `gemma4:26b` | Fast enough for interactive dev |
| Retrieval / embeddings | `embeddinggemma` (308M, separate model) | Gemma 4 has no embedding mode; use the sibling |

## Anti-Patterns

1. **Don't use `format: "json"`** — infinite loops on nested schemas
2. **Don't leave `think` at default** — eats your output budget silently
3. **Don't leave `num_predict` at default** — 128 tokens is nothing
4. **Don't leave `num_ctx` at default** — 2048 truncates most prompts
5. **Don't ask for huge JSON in one call** — break into sequential calls
6. **Don't use thinking mode for evaluation** — inflates scores, wastes context
7. **Don't skip system prompt identity** — Gemma 4 becomes a generic chatbot
8. **Don't use audio on 26B/31B** — only E-series has audio encoder

## Quick-Start Checklist

- [ ] Set `think: false`
- [ ] Set `num_predict` >= 512 (2048+ for JSON output)
- [ ] Set `num_ctx` >= 4096 (scale to your prompt size)
- [ ] Write explicit system prompt with identity + boundaries + output format
- [ ] Extract JSON client-side (no `format: "json"`)
- [ ] Set `keep_alive` >= 30m (or pin with -1)
- [ ] For long structured output, use sequential calls
- [ ] For vision, pass base64 in `images` array
- [ ] Test with your actual prompt length — Ollama won't warn about truncation