---
license: gemma
base_model: google/gemma-4-E2B-it
pipeline_tag: text-generation
tags:
  - gemma4
  - hybrid
  - custom_code
  - custom_generate
---

# Cactus Hybrid — Gemma 4 E2B

A small, on-device model is fast and private, but sometimes wrong. At Cactus we
post-train models to *know when they are wrong*: we ship probes inside the
checkpoint that score every answer with a **confidence** between 0 and 1,
returned as structured data (never parsed out of the answer text). Answer
on-device when confidence is high; re-route to a bigger model when it's low:

```python
if confidence < 0.85:
    answer = ask_a_bigger_model(prompt)
```

This repo is **google/gemma-4-E2B-it plus the handoff probe**: a small head
(weight prefix `handoff_probe.*`) that scores every generation with
`confidence = 1 - p_wrong`. The base weights are byte-identical to the stock
checkpoint (same keys); the repo adds eleven probe tensors, a remote-code model
class and a `custom_generate` recipe. **Stock engine commands work unchanged** —
you only add `--trust-remote-code` / `trust_remote_code=True`.

## Benchmarks

Gemma 4 E2B Hybrid, the smallest Gemma model, matches Gemini 3.1 Flash-Lite on
most benchmarks by routing only 15–35% of queries to Flash-Lite and running the
rest itself:

| Benchmark | Handoff to match Flash-Lite (FP16) | At 4-bit | At 3-bit |
|---|---|---|---|
| ChartQA | 15–20% | 25–30% | 40–50% |
| MMBench | 30–35% | 40–45% | 50–55% |
| LibriSpeech | 25–30% | 35–40% | 55–65% |
| GigaSpeech | 30–35% | 40–45% | 50–55% |
| MMAU | 30–35% | 35–40% | 50–55% |
| MMLU-Pro | 45–55% | ~90% | n/a |

Quantisation quality is measured on
[Cactus Quants](https://github.com/cactus-compute/cactus/blob/main/docs/cactus_quants.md),
which performs well at uniform quantization; developers are encouraged to
benchmark Unsloth, GGUF, and MLX quantization independently.

## Quickstart

```python
# pip install "transformers>=5.5.4,<5.6" torch   (5.14+ segfaults on this checkpoint)
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Cactus-Compute/gemma-4-e2b-it-hybrid"
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, dtype="auto").to(device)

messages = [{"role": "user", "content": "What is the capital of France?"}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
).to(device)
out = model.generate(**inputs, return_confidence=True, max_new_tokens=512)

print(tokenizer.decode(out.sequences[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
print("confidence:", out.confidence)
```

Load the model with an explicit `.to(device)`, not `device_map="auto"`: the
probe scores generations outside the module `forward()` path, so weights that
accelerate offloads (left on the `meta` device) crash the confidence read.

## Serving with stock transformers

```bash
transformers serve --trust-remote-code
# then request model "Cactus-Compute/gemma-4-e2b-it-hybrid" via the OpenAI-compatible API
```

or interactively:

```bash
transformers chat Cactus-Compute/gemma-4-e2b-it-hybrid --trust-remote-code
```

### How the confidence reaches you (in-band trailer)

`transformers serve` cannot add response fields, so the score travels **in-band**:
the assistant content's final line is

```
\n[[hybrid:confidence=0.7812]]
```

always exactly 4 decimals, ASCII, `confidence` in `[0, 1]`. Strip the final
`[[hybrid:...]]` line before display and parse the float for routing. The
trailer is emitted in both streaming and non-streaming modes.

### Limitations

- `--continuous-batching`: not supported — the CB scheduler bypasses
  `generate()`, so no probe runs and **no trailer is emitted**. Serve without
  `--continuous-batching` to get confidence scores.
- The trailer (and confidence) is only produced for single-sequence decoding:
  batch size 1, no beam search, no assisted/speculative decoding. Unsupported
  modes fall back to stock behavior (no trailer).
- The probe scores at most the first 1024 generated tokens.
- The trailer's token ids are appended to the returned sequences, so reported
  completion token counts include the trailer (a handful of tokens).

## More Python APIs

```python
# Structured API: clean sequences + raw float (no in-band trailer).
sequences, confidence = model.generate_with_confidence(inputs, max_new_tokens=512)
print(confidence)                     # e.g. 0.7812
print(model.last_confidence)          # same value

# Stock generate (custom_generate recipe): plain tensor + in-band trailer.
sequences = model.generate(**inputs, max_new_tokens=512)

# Suppress the trailer while keeping stock behavior:
sequences = model.generate(**inputs, max_new_tokens=512, emit_trailer=False)
```

## Probe contract

- Input: float32 `[T, 1536]` — output of decoder layer index 28
  (`config.probe_layer`), captured at the position that predicts each generated
  token: row 0 = last prompt position at prefill, row t = position captured at
  generation step t. Only the first 1024 rows are scored.
- Math (float32): `x = LayerNorm(x, eps=1e-5) * norm.weight + norm.bias`;
  `p = relu(x @ proj.weight.T + proj.bias)`;
  `s = p @ attn_query / sqrt(32)`; `w = softmax_T(s - max)`; `pooled = w @ p`;
  `h = relu(head.0 @ pooled + b)`; `h = relu(head.2 @ h + b)`;
  `logit = head.4 @ h + b`; `p_wrong = sigmoid(logit)`;
  `confidence = 1 - p_wrong`.
- Capture uses a forward hook that keeps only one `[1, 1536]` row per decode
  step — full hidden-state stacks are never materialized.

## Repo contents

| File | Purpose |
|---|---|
| `configuration_gemma_4_e2b_it_hybrid.py` | `Gemma4E2BItHybridConfig` (stock Gemma-4 text config + probe hyperparams) |
| `modeling_gemma_4_e2b_it_hybrid.py` | `Gemma4E2BItHybridForCausalLM` (stock `Gemma4ForCausalLM` + `handoff_probe.*`) |
| `custom_generate/generate.py` | stock decode loop + confidence + in-band trailer |
| `model*.safetensors` | base weights (identical keys) + `handoff_probe.*` tensors |
| `gemma_4_e2b_it_hybrid.py` | single-file `mlx-lm` model, wired via config.json's `model_file` |

## Routing quality (AUROC)

AUROC measures how well the probe separates wrong answers from right ones
(higher = better, 0.5 is random, 1.0 is perfect):

| Hold-out | Modality | Cactus Hybrid | Token Entropy |
|---|---|---|---|
| MMLU | text MCQ | **0.770** | 0.697 |
| MMLU-Pro | text MCQ | **0.771** | 0.692 |
| ARC-Easy | text MCQ | **0.888** | 0.655 |
| ARC-Challenge | text MCQ | **0.834** | 0.646 |
| GSM8K (3-shot) | text gen | **0.782** | 0.731 |
| MMBench-EN-Dev | vision MCQ | **0.840** | 0.435 |
| ChartQA | vision QA | **0.779** | 0.615 |
| DocVQA | vision QA | **0.781** | 0.512 |
| MMAU | audio MCQ | **0.789** | 0.517 |
| GigaSpeech | audio | **0.876** | 0.343 |
| Earnings-22 | audio | **0.839** | 0.323 |
| LibriSpeech | audio | **0.822** | 0.427 |
| **Mean** | | **0.814** | **0.549** |

The strongest result: the probe was trained on **zero audio data**, yet achieves
0.79–0.88 AUROC on four audio benchmarks (two transcription, one audio MCQ, one
out-of-domain transcription). This rules out surface-level explanations: the
probe is reading a modality-independent correctness signal from the hidden
state, not memorizing patterns from training data.

## All formats

All Cactus Hybrid builds live in the
[Cactus Hybrid collection](https://huggingface.co/collections/Cactus-Compute/cactus-hybrid-6a60da4551074db058e8bb64):
[Transformers](https://huggingface.co/Cactus-Compute/gemma-4-e2b-it-hybrid) ·
[GGUF / llama.cpp](https://huggingface.co/Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF) ·
[MLX](https://huggingface.co/Cactus-Compute/gemma-4-e2b-it-hybrid-mlx) ·
[Cactus engine](https://huggingface.co/Cactus-Compute/gemma-4-E2B-it).
Copy-paste quickstarts for every engine:
[github.com/cactus-compute/cactus-hybrid](https://github.com/cactus-compute/cactus-hybrid).

## License

Gemma is provided under and subject to the Gemma Terms of Use. This derivative
includes the Cactus handoff probe head.