---
license: apache-2.0
language:
- en
- kk
base_model:
- Qwen/Qwen3-0.6B
datasets:
- issai/foggen-data
- issai/KazCulture
pipeline_tag: text-generation
tags:
- edge-cloud-routing
- verbalized-confidence
- self-aware
- routing
- continual-learning
- multi-round
library_name: transformers
---

# FogGen: Self-Aware Edge–Cloud LLM Router

> **A 0.6B parameter edge LLM trained to emit a calibrated verbalized confidence score before its answer, enabling efficient edge–cloud routing without an external router.**

![FogGen overview: (a) self-aware routing at inference, (b) self-evolving training loop](./foggen_overview.png)

FogGen is a small, self-aware edge model that knows when to answer locally and when to defer to a stronger cloud model. At inference (figure (a)) it emits a confidence score then an answer in one forward pass; if confidence `c ≥ τ` the local answer is returned, otherwise the query is routed to the cloud. Training (figure (b)) is a self-evolving loop: each round, the current checkpoint self-samples N=8 generations per question to derive confidence buckets, then SFTs on `(question, confidence, answer)` triples.

The released checkpoint is the endpoint (`R14`) of a 14-round chain trained across seven domains: finance, science, coding, law, math, Kazakh culture, medical.

## Quick demo

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

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

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

Rules:
- Do not output <think> tags.
- 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>"""

question = """A firm reports $400M in total liabilities and $600M in shareholders' equity.
What is the firm's debt-to-equity ratio?

A. 0.67
B. 1.00
C. 1.50
D. 2.00"""

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": question},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True,
                                       enable_thinking=False).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))
# Expected:
#   Confidence: 1.0
#   Final answer: A
```

## How routing works

```python
import re

def route_query(model_output: str, tau: float = 0.5):
    """Parse FogGen output. Returns (action, confidence, answer).
    action is 'keep_local' if confidence >= tau, else 'route_to_cloud'."""
    conf_match = re.search(r"Confidence\s*:\s*([\d.]+)", model_output)
    ans_match  = re.search(r"Final\s+answer\s*:\s*([A-D])", model_output)
    if not conf_match: return "route_to_cloud", None, None
    confidence = float(conf_match.group(1))
    answer = ans_match.group(1) if ans_match else None
    return ("keep_local" if confidence >= tau else "route_to_cloud", confidence, answer)
```

At τ=0.5 on the trained domains, the model routes ~22% of queries to the cloud while achieving 67.8% mean system accuracy.

## Model details

| | |
|---|---|
| **Base model** | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) |
| **Parameters** | 0.6 B |
| **Training method** | LoRA SFT (rank=16, α=32, all-linear), bf16, 2 epochs/round |
| **Rounds** | 14 sequential rounds (R0 → R14) |
| **Training tokens** | ~1800 SFT rows × 14 rounds |
| **Domains** | finance, science, coding, law, math, Kazakh culture, medical |
| **Cloud teacher** | [Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) |
| **Output format** | `Confidence: <bucket>\nFinal answer: <letter>` |
| **Confidence buckets** | 5 discrete values: 0.0, 0.25, 0.5, 0.75, 1.0 |
| **License** | Apache 2.0 (inherited from base) |

## Performance

System accuracy at τ=0.5 on seven MCQ domains (full test sets, ~16,200 questions), measured against Random routing and a cloud-only baseline (Qwen3-30B-A3B-Instruct-2507):

| Domain | Cloud only | R14 raw | Random @ τ=0.5 | **FogGen @ τ=0.5** | Cloud routed |
|---|---|---|---|---|---|
| Finance | 69.5% | 57.0% | 59.9% | **65.8%** | 23.3% |
| Science | 72.7% | 56.9% | 60.1% | **64.5%** | 20.4% |
| Coding | 74.2% | 61.8% | 64.2% | **69.5%** | 19.7% |
| Law | 70.7% | 55.3% | 58.4% | **62.4%** | 20.1% |
| Math | 60.1% | 42.2% | 50.8% | **58.1%** | 47.7% |
| Kazakh culture | 95.8% | 91.3% | 91.4% | **91.9%** | 1.0% |
| Medical | 74.0% | 52.6% | 57.1% | **62.2%** | 20.9% |
| **Mean** | **73.9%** | **59.6%** | **63.1%** | **67.8%** | **21.9%** |

Mean lift over Random at τ=0.5: **+4.6** (system accuracy minus random-routing accuracy, averaged across the seven domains).

### Baseline comparison

Direct comparison against AutoMix (Aggarwal et al., 2024) on the same R14 checkpoint, same evaluation sets:

| Method | SysAcc | Cloud routed | Δ over Random | Fwd passes / query |
|---|---|---|---|---|
| AutoMix | 67.2% | 29.0% | +3.7 | 9 (1 answer + 8 verify) |
| **FogGen (ours)** | **67.8%** | **21.9%** | **+4.6** | **1** |

FogGen achieves higher accuracy at lower cloud cost and 9× lower per-query inference cost.

## Open-ended generalization

The MCQ-trained chain transfers to open-ended task types zero-shot. Local accuracy and routing benefit at τ=0.5 on three held-out OE benchmarks:

| Benchmark | Format | R14 raw | R14 Δ@τ=0.5 |
|---|---|---|---|
| [SQuAD v1.1](https://huggingface.co/datasets/rajpurkar/squad) | extractive RC | 81.0% | +1.4 |
| [TruthfulQA gen](https://huggingface.co/datasets/truthfulqa/truthful_qa) | adversarial factual | 36.5% | −0.7 (anti-calibrated) |
| [GSM8K](https://huggingface.co/datasets/openai/gsm8k) (CoT) | math word-problems | 52.0% | +2.2 |

One additional round of OE training (R15, 1876 SFT rows) lifts local accuracy on these three benchmarks to 86.5% / 40.0% / 58.0% respectively; see [`issai/foggen-r15-oe`](https://huggingface.co/issai/foggen-r15-oe).

## Citation

Paper coming soon.

## Acknowledgements

Thanks to the Qwen team at Alibaba for the base model and cloud teacher.
