gemma-4-12b-request-complexity-20260918-01

Gemma 4 12B Request Complexity Scorer — request-complexity-20260918-01

This is an experimental Q4_K_M GGUF export of a fine-tuned google/gemma-4-12B-it model for scoring the complexity of arbitrary user requests from 0 to 100.

The intended output is a single integer:

• 0–10: noise, empty, trivial acknowledgements
• 10–30: simple chat or factual requests
• 30–50: basic reasoning, arithmetic, or single-step transformations
• 50–70: multi-step practical or coding/debugging requests
• 70–90: advanced expert reasoning or difficult math
• 90–100: research-grade, olympiad-level, or highly ambiguous/complex tasks

Artifact

• GGUF: training/outputs/model-Q4_K_M.gguf
• Quantization: Q4_K_M
• SHA-256: 727c993da958113e2e0cdf01d52da59d656751c8b71748e76e89ebb595465de2
• Size: 7,381,382,848 bytes
• Format magic: GGUF

Training data

The dataset contains 500 supervised chat records:

Sources:

The held-out verification split was not used for training.

Training summary

• Base model: google/gemma-4-12B-it
• Base revision: 707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7
• Steps: 8
• Train rows: 350
• Validation rows: 50
• Baseline validation loss mean: 9.87062
• Final validation loss mean: 6.706186
• Adapter reload verified: true

Usage

Prompt the model with a user request and ask it to return only an integer from 0 to 100.

Example system instruction:

You score the complexity of the user's request. Return only one integer from 0 to 100. Do not explain.

Example:

User: Prove that there are infinitely many primes of the form 4n+3.
Assistant: 86

Limitations

This is an exploratory fine-tune with a small training set and short training run. The numeric labels are synthetic/derived and should be treated as a calibrated heuristic, not a human-certified measurement.

Held-out verification benchmark

Modal/Ollama inference was run on all 100 held-out verification prompts after public Hugging Face publication. The model was prompted with the direct request and a system instruction to return only one integer complexity score from 0 to 100.

The strongest failure mode is MATH-style prompts: the model often answers the math problem instead of scoring its difficulty. This model is therefore not production-ready as a request-complexity scorer; it is useful as a pipeline proof and as evidence for the next dataset/prompting iteration.

Reproducibility artifacts

The repository includes local run evidence files alongside the GGUF:

• analysis-summary.json
• dataset/manifest.json
• training/outputs/export.json
• receipts/training-train.json
• receipts/training-export.json
• scoring/verification-modal-20260918-02/numeric-metrics.json
• scoring/verification-modal-20260918-02/predictions.csv
• scoring/verification-modal-20260918-02/category-metrics.csv

Generated with finetune-lab run ID request-complexity-20260918-01.

License and attribution

The base model is google/gemma-4-12B-it. The Hugging Face model metadata reports license apache-2.0 with license link https://ai.google.dev/gemma/docs/gemma_4_license. Follow the upstream Gemma terms and attribution requirements when using this derivative artifact.