Gemma 4 E4B-IT fine-tuned for Sumerian cuneiform transliteration — converting glyph name sequences into standard Latin-alphabet transliterations as used in Assyriology.

Trained on the SumTablets dataset (82,452 administrative texts, predominantly Ur III period) using QLoRA via Unsloth.

Task

Given a sequence of Sumerian cuneiform glyph names (e.g., 1(diš) 1(aš) gur še lugal), the model produces the corresponding transliteration in standard Assyriological convention.

This is a structured sequence-to-sequence translation task — not free-form text generation. The model must learn the compositional rules of Sumerian sign reading, including:

Determinatives (e.g., d, diš)
Logographic vs. syllabic sign readings
Numeric notation systems (Š, N, etc.)
Signs with multiple possible readings

Training Details

Parameter	Value
Base Model	`unsloth/gemma-4-E4B-it` (4-bit QLoRA)
Dataset	`colesimmons/SumTablets` (82,452 train / 4,577 val / 4,577 test)
Format Mapping	`glyph_names → user`, `transliteration → assistant`
System Prompt	"Transliterate the following Sumerian cuneiform glyph names into their corresponding Latin alphabet transliteration. Output ONLY the transliteration."
LoRA Rank	16
LoRA Alpha	16 (rsLoRA enabled)
Learning Rate	5e-5
Scheduler	Cosine
Batch Size	2 × 8 accum = 16 effective
Max Seq Length	4096
train_on_completions	false
packing	false
Hardware	NVIDIA RTX 5080 (16GB)

Results

Metric	Value
Train Loss (optimal)	0.30
Character Accuracy	36.2%

The 36% character-level accuracy reflects the inherent difficulty of the task — Sumerian sign readings are heavily context-dependent, with many signs having 5+ valid readings depending on genre, period, and surrounding signs. The model performs strongest on short administrative texts (Ur III, ~93% of training data) and weakest on long literary compositions.

Note on inference: Gemma-4-E4B's built-in thinking/reasoning mode can override the fine-tuned transliteration behavior. Use the same system prompt during inference as was used during training to suppress reasoning mode and get clean transliteration output.

GGUF Quantizations

File	Format	Size	Use Case
`gemma-4-e4b-it.Q4_K_M.gguf`	Q4_K_M	4.97 GB	General use — best quality/size balance
`gemma-4-e4b-it.BF16-mmproj.gguf`	BF16	374 MB	Multimodal projector (required for vision input)

Usage

llama.cpp / LM Studio

bash

llama-cli \
  -m gemma-4-e4b-it.Q4_K_M.gguf \
  --system-prompt "Transliterate the following Sumerian cuneiform glyph names into their corresponding Latin alphabet transliteration. Output ONLY the transliteration." \
  -p "1(diš) gin₂ ku₃-babbar"

llama-server (OpenAI-compatible)

bash

llama-server \
  -m gemma-4-e4b-it.Q4_K_M.gguf \
  --system-prompt "Transliterate the following Sumerian cuneiform glyph names into their corresponding Latin alphabet transliteration. Output ONLY the transliteration."

Limitations

Domain concentration: 93% of training data is administrative texts from the Ur III period. Performance degrades on literary, lexical, or other period texts.
Character accuracy ceiling: 36% char-level accuracy means roughly 1 in 3 characters will be incorrect — suitable as a pre-processing/assistive tool, not as a standalone authoritative transliteration.
Thinking mode interference: Without the system prompt, the base model's reasoning mode may produce English analysis instead of transliteration.
No audio/vision evaluation: The multimodal projector weights are included but were not evaluated for image-to-text cuneiform reading.

Dataset

Training used colesimmons/SumTablets (CC-BY-4.0):

82,452 training samples, 4,577 validation, 4,577 test
Columns: glyph_names (input) → transliteration (target)
Predominantly Ur III period administrative records
Covers genres: administrative, legal, letter, literary, lexical, royal inscription, school text

Acknowledgments

Google DeepMind — Gemma 4 model family
Colesimmons — SumTablets dataset
Unsloth — QLoRA training framework (2× faster fine-tuning)

License

Apache 2.0 — see Gemma 4 License for model terms. Dataset licensed separately under CC-BY-4.0.

TRACCERR/Gemma-4-E4B-IT-Sumtablets-GGUF

Gemma-4-E4B-IT-Sumtablets-GGUF