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ni-co-la-s/
gemmeh-it-GGUF

Name: gemmeh-it-GGUF
Availability: InStock

gguf

endpoints_compatible

conversational

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Gemmeh-IT 1B — GGUF

GGUF quantizations of Gemmeh-IT, a 1.1B-parameter decoder-only transformer trained for educational purposes from scratch on 20B tokens of pre-2024 FineWeb-Edu, then LoRA-finetuned on OpenHermes for chat.

Architecture is Gemma 3-inspired without sliding-window attention. Custom 32k SentencePiece BPE tokenizer trained on the same corpus.

For the base model, see ni-co-la-s/gemmeh-GGUF.

⚠ Requires a custom llama.cpp fork

The model has a few deviations from the original gemma3 (no sliding window, fused QKV) and cannot be loaded by stock llama.cpp.

To run these files, use the fork: Ni-co-la-s/llama.cpp-gemmeh.

Training details


Parameters	1.1B
Architecture	Gemma 3-inspired
Vocab	32,768 (SentencePiece BPE, English-only)
Context	4,096
Pretraining data	FineWeb-Edu sample, 20B tokens
Knowledge cutoff	Pre-2024 (intentional)
Finetuning	LoRA rank 16 on OpenHermes (250M assistant tokens)

Benchmarks (base 1B model, 20B tokens)

Evaluated through the BF16 GGUF served via llama.cpp with lm-eval. Gemma 3 1B IT numbers obtained locally by running unsloth/gemma-3-1b-it-GGUF at Q8_0 through the same pipeline.

Benchmark	Metric	Gemmeh 1B (base)	Gemmeh-IT 1B	Gemma 3 1B IT (local)
PIQA	0-shot	70.2	71.4	72.8
ARC-Challenge	25-shot	38.4	40.4	40.3
ARC-Easy	0-shot	57.3	57.6	63.4
WinoGrande	5-shot	52.2	54.0	55.1
TruthfulQA	mc2, 0-shot	37.8	44.8	38.9

Trained on roughly 10–100× less data than the references, with a much smaller (32k vs 262k) vocabulary, and no distillation.

Limitations

Smaller and less benchmark-competitive than similarly-sized models trained on more data.
English only.
4,096 token context.
Pre-2024 knowledge only.
Requires the custom llama.cpp fork.

ni-co-la-s/gemmeh-it-GGUF