
Architecture	Decoder-only Transformer
Parameters	109,384
Hidden size	64
FFN size	128
Physical blocks	2
Effective depth	4 (`A → B → A → B`)
Attention	GQA — 4 query heads, 2 KV heads, head dimension 16
Normalization	RMSNorm
Embedding	Factorized tied embedding, rank 32
Vocabulary	1,024
Context length	2,048 tokens
Canonical dtype	BF16
Base model	`ML-is-Fun/Fleck-S-100K-Base`

Training

Pretraining

The Base parent was initialized from scratch and pretrained for exactly 100,000,000 real tokenizer tokens on a 70/30 FineWeb-Edu/FineWeb mixture.

Instruction Tuning


Base model	`Fleck-S-100K-Base`
Dataset	`databricks/databricks-dolly-15k`
Dataset license	CC BY-SA 3.0
Requested budget	100K supervised tokens
Realized supervised tokens	101,832
Optimizer	AdamW
Instruction tuning	Assistant-response supervision
Hardware	Apple M2 (10-core GPU)

Benchmark Results

Corrected zero-shot evaluation, no chat template, FP32 evaluation, and the same benchmark aggregation protocol were used for both variants.

Task	Metric	Base	Instruct	Δ
HellaSwag	`acc_norm`	26.28%	26.29%	+0.01pp
PIQA	`acc_norm`	51.14%	51.36%	+0.22pp
ARC-Easy	`acc_norm`	26.05%	25.72%	-0.34pp
ARC-Challenge	`acc_norm`	26.19%	25.94%	-0.26pp
LAMBADA OpenAI	`acc`	0.02%	0.06%	+0.04pp
WinoGrande	`acc`	49.41%	50.91%	+1.50pp
BoolQ	`acc`	37.83%	37.83%	0.00pp
MMLU (57-subject macro)	`acc`	23.09%	23.12%	+0.03pp
Eight-task mean	—	30.00%	30.15%	+0.15pp

A generation and contamination sanity check found no exact prompt/example matches among the audited records. This is a diagnostic, not a guarantee of absence of all contamination.

Tokenizer and Chat Format

Fleck-Tokenizer-1024
Byte-level BPE
Vocabulary size: 1,024

The training chat format is:

<bos><|user|>{user}<|eot|><|assistant|>{response}<|eot|><eos>

Special Tokens

Token	ID	Role
`<bos>`	0	sequence start
`<eos>`	1	sequence end
`<pad>`	2	padding
`<unk>`	3	unknown token
`<\|system\|>`	4	system turn
`<\|user\|>`	5	user turn
`<\|assistant\|>`	6	assistant turn
`<\|eot\|>`	7	end of turn

Usage

The bundle includes a self-contained inference.py; it does not import the Fleck-LM checkout. The accompanying config.json, generation_config.json, and tokenizer_config.json describe the custom architecture and generation/tokenizer defaults; standard transformers.AutoModel loading is not supported. The chat_template.jinja file contains the Instruct chat template used by compatible HF tooling. Install the three runtime dependencies:

python -m pip install torch safetensors tokenizers

By default the CLI starts an interactive chat. /exit quits and /clear resets the conversation history:

python inference.py --device cpu --max-tokens 32

For one prompt without interactive mode, pass --no-chat and --prompt:

bash

python inference.py \
  --ckpt model.safetensors \
  --tokenizer tokenizer.json \
  --no-chat \
  --prompt "Explain what a tokenizer does." \
  --max-tokens 32 \
  --device cpu

The default filenames are model.safetensors and tokenizer.json. The script reads and runs the BF16 checkpoint without an FP32 model copy, validates every SafeTensors key, shape, and dtype, and uses FP32 only for attention score/softmax and tied-logit accumulation. It reproduces the factorized tied embedding/logits, effective-depth execution A → B → A → B, half-split RoPE, GQA, physical KV caches, RMSNorms, and greedy generation without any repository-local imports. Generation stops on <|eot|> or <eos>.

Limitations

This model is extremely small and is intended for research and local experimentation rather than reliable general-purpose assistance. It may produce repetitions, malformed text, weak factual answers, or incoherent responses. Instruction tuning improves conversational behavior but does not overcome the limits of a 109K parameter model.

License

MIT License.

Files

The public bundle contains these files:

README.md — model card and usage documentation
inference.py — standalone strict loader and interactive/single-prompt inference CLI
model.safetensors — BF16 model weights
tokenizer.json — standalone tokenizer
config.json — custom architecture configuration
generation_config.json — greedy generation defaults
tokenizer_config.json — tokenizer defaults and special-token mapping
chat_template.jinja — Instruct chat template No training data, optimizer state, or other training outputs are included.