README.md

---
license: gemma
library_name: transformers
base_model:
- google/gemma-4-E4B-it
tags:
- gemma
- text-generation
- instruction-tuned
- tool-calling
- structured-output
- vllm
pipeline_tag: text-generation
---

# SuperGemma4 E4B Abliterated

`supergemma4-e4b-abliterated` is a private evaluation release whose original
upstream base is `google/gemma-4-E4B-it`.

This SuperGemma release is an **abliterated and tuned** derivative of that
Google E4B base, with additional work for higher release quality, stronger
formatting discipline, better code output, and faster time to first token.

This branch is aimed at users who want:

- strong code and bug-fix behavior
- clean JSON and tool-call formatting
- fast first-token responsiveness
- release-ready serving behavior on Transformers and OpenAI-compatible stacks

## Why This Build Exists

The original Google checkpoint provides the core Gemma 4 E4B capability base.
This project line uses an abliterated release path to reduce refusal-heavy
behavior, but that kind of modification can regress on exact formatting,
tool-call reliability, and service stability if it is not carefully hardened.

This release focuses on recovering and then surpassing baseline quality where
it matters for real usage:

- exact structured outputs
- code correctness
- bug-fix reliability
- server-facing stability
- low-friction deployment on Transformers and OpenAI-compatible serving stacks

## Highlights

- Release-quality score: `92.34`
- Exact-eval score: `98.50`
- Broad-eval score: `83.10`
- JSON exact-match: `100%`
- Tool-call accuracy: `90%`
- Exact code score: `100%`
- Exact bug-fix score: `100%`
- Long-context sanity: `100%`
- TTFT: `2291 ms`
- PREFILL: `2479.70 tok/s`
- DECODE: `42.04 tok/s`

## Lineage

1. Original upstream base: `google/gemma-4-E4B-it`
2. Abliterated and tuned release: `Jiunsong/supergemma4-e4b-abliterated`

## Comparison Snapshot

Measured against the same evaluation harness used for:

- `google/gemma-4-E4B-it`

| Model | Release Quality | Exact Overall | JSON | Tool | Code | Bugfix | TTFT ms | PREFILL tok/s | DECODE tok/s |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| Google base | 77.46 | 83.50 | 50.0 | 90.0 | 62.5 | 100.0 | 4827.31 | 2456.69 | 42.04 |
| SuperGemma4 E4B Abliterated | 92.34 | 98.50 | 100.0 | 90.0 | 100.0 | 100.0 | 2291.23 | 2479.70 | 42.04 |

## Stability Notes

This candidate was release-hardened against the failure modes that matter in
real serving:

- batched OpenAI-compatible serving restored
- simple OpenAI-compatible serving restored
- unicode output verified
- tool-calling output verified
- empty-response false-green cases blocked by stricter tests

Validation highlights:

- direct reliability audit: `14/14`
- repeat reliability probe: `90/90`
- batched soak test: `12/12`
- simple soak test: `6/6`

## Recommended Use Cases

- coding assistant
- bug-fix assistant
- strict JSON and schema outputs
- agent backends that depend on tool-call formatting
- standard BF16 deployment on Hugging Face / Transformers stacks

## Quick Start

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Jiunsong/supergemma4-e4b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Write a compact Python function that groups words by length."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    outputs = model.generate(inputs, max_new_tokens=256)

print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
```

## Serving

This checkpoint is designed to work well with:

- Transformers
- vLLM-style OpenAI-compatible stacks

## Release Positioning

This private release is the strongest all-around E4B candidate in the current
project line for users who want the abliterated base behavior without giving up
quality recovery, formatting discipline, or serving readiness.