README.md

---
license: gemma
language:
- en
- kk
base_model:
- google/gemma-3-270m
datasets:
- issai/foggen-data
- issai/KazCulture
pipeline_tag: text-generation
tags:
- edge-cloud-routing
- verbalized-confidence
- self-aware
- routing
- continual-learning
- multi-round
- sibling-distilled
- capability-floor
library_name: transformers
---

# FogGen (Gemma-3-270m, sibling-distilled): capability-floor R14 endpoint

> **The 270M-parameter capability-floor probe of the FogGen recipe.** Sibling-distilled from the Gemma-3-1b-it buffer to install the FogGen output format, then run through the same 14-round self-evolving chain. Demonstrates the recipe pays off at deployment-grade magnitudes from roughly 0.6B upward; below that, lift becomes order-of-magnitude smaller and a sibling-distilled SFT pass is required to install the format at all.

This is a **capability-floor diagnostic checkpoint**, not a deployment model. The canonical deployment endpoint is [`issai/foggen`](https://huggingface.co/issai/foggen) at the 0.6B scale.

For background on the system overview, training pipeline, and routing protocol, see the [`issai/foggen`](https://huggingface.co/issai/foggen) model card.

## Why this exists

Native zero-shot routing is infeasible at the 270M scale: no prompting or constrained-decoding setup we tried exceeded 54% format compliance on the FogGen output schema (the model fails to emit the `Confidence:`/`Final answer:` pattern reliably enough to extract a routing signal). We therefore probe this scale with a two-stage protocol:

1. **Sibling-distillation SFT pass**: one round of SFT on the calibration buffer of the [Gemma-3-1b-it sibling](https://huggingface.co/issai/foggen-gemma3-1b), using the larger model's bucket labels as targets. This installs the FogGen format on the 270M backbone.
2. **Standard 14-round chain**: identical recipe to [`issai/foggen`](https://huggingface.co/issai/foggen) from there. 7 domain rotation, LoRA r=16 α=32, bf16, 2 epochs/round, same cloud teacher.

The released checkpoint is R14 of the post-distillation chain.

## Performance

System accuracy at τ=0.5 on the seven MCQ domains (full test sets, ~16,200 queries). Cloud baseline is Qwen3-30B-A3B-Instruct-2507.

| Domain | Cloud only | R14 raw | Random @ τ=0.5 | **FogGen @ τ=0.5** | Cloud routed |
|---|---|---|---|---|---|
| Finance | 69.5% | 32.2% | 58.2% | **60.2%** | 69.5% |
| Science | 72.7% | 30.4% | 58.2% | **59.5%** | 65.6% |
| Coding | 74.2% | 34.3% | 64.7% | **65.7%** | 76.3% |
| Law | 70.7% | 31.7% | 58.5% | **59.7%** | 68.7% |
| Math | 60.1% | 24.5% | 58.3% | **58.5%** | 94.9% |
| Kazakh culture | 95.8% | 43.7% | 60.3% | **59.3%** | 31.9% |
| Medical | 74.0% | 32.2% | 59.8% | **60.8%** | 65.9% |
| **Mean** | **73.9%** | **32.7%** | **59.7%** | **60.5%** | **67.5%** |

Mean lift over Random at τ=0.5: **+0.8** (positive on six of seven domains; negative on Kazakh culture, the headroom-collapse domain).

Compared to [`issai/foggen`](https://huggingface.co/issai/foggen) (+4.6 at 0.6B) and [`issai/foggen-gemma3-1b`](https://huggingface.co/issai/foggen-gemma3-1b) (+5.9 at 1B), the lift here is an order of magnitude smaller. The recipe still produces positive lift, but the magnitude scales sharply with edge capacity below the 0.6B mark.

## Quick demo

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("issai/foggen-gemma3-270m", torch_dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("issai/foggen-gemma3-270m")

SYSTEM = """You are a self-aware multiple-choice assistant.

Rules:
- First, assess your confidence in solving this question.
- Then give your answer.
- Output format:
  Confidence: <0.0|0.25|0.5|0.75|1.0>
  Final answer: <OPTION_LETTER>"""

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "<your MCQ here>"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
```

The routing decision (`route_query` helper, threshold τ) is identical to the [`issai/foggen`](https://huggingface.co/issai/foggen) card.

## License

Inherits the [Gemma Terms of Use](https://ai.google.dev/gemma/terms) from google/gemma-3-270m.

## Citation

Paper coming soon.