LittleKedi-tiny-wide-sparse-instruct-GGUF

GGUF builds (f16 and q8_0) of LittleKedi-tiny-wide-sparse-instruct for llama.cpp, Ollama and LM Studio. The PyTorch weights (model.safetensors), config and tokenizer are in the main repo; the card below describes the model.

LittleKedi-tiny-wide-sparse-instruct is the instruction-tuned (chat) version of LittleKedi-tiny-wide-sparse-base, a small DeepSeek-V3-style Mixture-of-Experts language model trained from scratch. It has 3.8M parameters in total, of which 1.6M are active per token (0.52M excluding the embedding table). It was pretrained on a 1,000M-token mix (FineWeb-Edu, Cosmopedia (Stanford), Cosmopedia (OpenStax), Cosmopedia (WikiHow), Cosmopedia (Khan Academy), FineMath 4+, SmolTalk, github-code-clean).

Part of the LittleKedi family. Not affiliated with DeepSeek.

This is the instruction-tuned (chat) model. It answers in a chat format and stops when it's done, but it's still a very small model: answers are fluent and on-topic but often wrong or vague, and it invents facts confidently. It's an educational and research artifact; don't use it for anything that matters.

Chat format

<|system|>You are a helpful assistant.<|end|><|user|>What causes the seasons?<|end|><|assistant|>

The model writes its reply and ends it with <|end|>. The system turn is optional. The GGUF files embed this template, and chat_template.jinja / tokenizer_config.json carry it for other tools.

The LittleKedi family

LittleKedi is a family of small DeepSeek-V3-style Mixture-of-Experts language models trained from scratch. Every size comes in three variants that differ in how sparse they are: how many routed experts they have, and what share of their parameters a token actually passes through.

Variant	Routed experts
standard (<size>)	a few wide experts; the densest variant
sparse (<size>-sparse)	many narrower, fine-grained experts, more of them active per token
wide-sparse (<size>-wide-sparse)	more experts again; the largest total and smallest active share

More experts means more total capacity to store knowledge, but each expert sees fewer tokens (tokens × top-k ÷ experts per layer), so sparser variants need more training data before their experts are well trained. The variants of a size share a tokenizer and dataset, so they can be compared directly. Shapes change as the family scales; the table below is generated from the current presets.

This card is for tiny-wide-sparse, the wide-sparse tiny model: 1.6M of its 3.8M parameters (42%) are active per token. Its siblings are tiny and tiny-sparse.

Model	Variant	Vocab	Total	Active	Active non-emb.	Active %	Routed experts
tiny	standard	8K	2.0M	1.5M	0.5M	78%	8 × 64, top-2
tiny-sparse	sparse	8K	2.6M	1.6M	0.5M	60%	32 × 32, top-4
tiny-wide-sparse ← this model	wide-sparse	8K	3.8M	1.6M	0.5M	42%	64 × 32, top-4
mini	standard	8K	9.4M	4.8M	3.2M	51%	12 × 128, top-3
mini-sparse	sparse	8K	19.8M	4.8M	3.2M	24%	64 × 64, top-6
mini-wide-sparse	wide-sparse	8K	36.4M	4.9M	3.3M	13%	128 × 64, top-6
small	standard	8K	12.2M	6.3M	4.2M	52%	16 × 128, top-4
small-sparse	sparse	8K	24.1M	6.4M	4.3M	27%	80 × 64, top-8
small-wide-sparse	wide-sparse	8K	43.9M	6.5M	4.4M	15%	160 × 64, top-8
moderate	standard	16K	49.8M	18.8M	12.5M	38%	24 × 192, top-4
moderate-sparse	sparse	16K	105.8M	19.1M	12.8M	18%	120 × 96, top-8
moderate-wide-sparse	wide-sparse	16K	199.0M	19.4M	13.1M	10%	240 × 96, top-8
medium	standard	16K	85.9M	30.8M	22.4M	36%	24 × 256, top-4
medium-sparse	sparse	16K	185.3M	31.2M	22.8M	17%	120 × 128, top-8
medium-wide-sparse	wide-sparse	16K	350.9M	31.6M	23.2M	9%	240 × 128, top-8
base	standard	32K	258.7M	105.4M	80.2M	41%	32 × 256, top-6
base-sparse	sparse	32K	542.8M	106.3M	81.2M	20%	160 × 128, top-12
base-wide-sparse	wide-sparse	32K	1015.9M	107.6M	82.4M	11%	320 × 128, top-12

"Active" counts everything a token passes through: embeddings, attention, the dense layer, shared experts and its top-k routed experts. "Active non-embedding" leaves out the embedding table (tied with the output head), which costs memory but almost no compute; it's the fair number for comparing compute between models.

Architecture

Component	This model
Parameters	3.78M total, 1.57M active per token (42%), 0.52M active non-embedding
Layers / hidden size	4 / 128 (layer 0 has a dense SwiGLU FFN of 352; layers 1–3 are MoE)
Attention	Multi-head Latent Attention (MLA): 4 heads, KV latent 32, decoupled RoPE dim 16, head dims 16+16 (q/k) and 16 (v). The KV cache stores only the 32-dim latent plus a 16-dim RoPE key per token
MoE	DeepSeekMoE: 64 routed experts (SwiGLU, 32 hidden), top-4, plus 2 shared experts; sigmoid gating; group-limited routing (4 groups, top-2)
Load balancing	Auxiliary-loss-free bias balancing (update speed 0.001) plus a small sequence-wise loss (α = 0.0001). Over the second half of training the median batch put 1.23× the mean load on the busiest expert (1.28× in the worst layer), and 0.00% of expert assignments were dropped for capacity.
Expert dispatch	Training: fixed capacity (capacity factor 2.0; overflow assignments are dropped). Inference is always dropless
Embeddings	Input embedding and output head tied
Context	1,024 tokens

Training

Data	1,000M tokens, 801,792 documents (~264 tokens per parameter, ~637 per active parameter), one pass, sources interleaved throughout (mix below)
Tokenizer	Byte-level BPE, 8,196 tokens (~3.75 bytes/token on this mix); digits not split (v1 tokenizer); chat special tokens built in
Steps	15,258 × 65,536 tokens (batch 16 × 1024 × 4 accumulation)
Optimizer	Muon (momentum 0.95, Nesterov, 5 Newton-Schulz steps; each expert's matrix orthogonalized separately) for hidden matrices, AdamW (β = 0.9, 0.95) for embeddings, norms and the router; weight decay 0.1, gradient clipping 1.0
Learning rate	2e-3 peak; WSD: 200 warm-up steps, constant, then linear decay to 0 over the last 20% of steps (schedule set for 15,258 steps)
Precision	bf16 autocast, torch.compile
Compute	~3.1e15 FLOPs (6 × active non-embedding parameters × tokens)
Hardware	One AMD Radeon 890M integrated GPU (Ryzen AI 300 laptop), ROCm on Windows: about 10.4 hours at a median ~26.7K tokens/s

Source	What it is	Tokens	Share
FineWeb-Edu sample/10BT	educational web pages	700M	70%
Cosmopedia (Stanford)	synthetic textbooks	70M	7%
Cosmopedia (OpenStax)	synthetic textbooks	30M	3%
Cosmopedia (WikiHow)	synthetic how-to articles	30M	3%
Cosmopedia (Khan Academy)	synthetic lessons	20M	2%
FineMath 4+	maths web pages	80M	8%
SmolTalk	chat conversations	50M	5%
github-code-clean	source code	20M	2%

Fine-tuning (SFT)

Base model	LittleKedi-tiny-wide-sparse-base
Conversations	6,730 (6,730 train / 451 validation), 611.6K assistant tokens trained on
Data	3,950 single-turn question/answer pairs (28% of assistant tokens) written by Gemma 4 E4B from 1,499 documents sampled from this model's own pretraining data, so every question concerns text it saw; filtered to drop questions that refer back to the source document. Plus ~2,780 general conversations from HuggingFaceTB/smol-smoltalk (everyday small talk in full, plus capped samples of openhermes-50k, smol-constraints and smol-magpie-ultra-short), filtered to remove code and to fit 1,024 tokens. 301 grounded pairs (whole documents) and 150 conversations held out as the test set.
Loss	On assistant replies and their closing `<
Schedule	842 steps, batch 16, 2 epochs, Muon + AdamW, peak LR 2e-4, cosine decay
MoE routing	Load-balancing biases frozen at their pretrained values, with padding excluded from expert-load statistics; experts run dropless

Validation loss on 451 held-out conversations (assistant tokens only): 4.040 → 3.146.

Evaluation

Zero-shot benchmarks (evals.py)

Task	acc_norm	acc	Items	Chance
SciQ	55.3% ± 1.6%	60.3%	1,000	25%
ARC-Easy	32.2% ± 1.0%	32.6%	2,376	25%
HellaSwag	26.7% ± 0.4%	26.7%	10,042	25%

Each answer choice is scored by the log-probability of its text after the prompt, using lm-evaluation-harness prompts (SciQ includes its supporting passage). acc_norm divides by the answer's length in characters; acc doesn't. Splits: SciQ test, ARC-Easy test, HellaSwag validation.

MMLU (mmlu_eval.py)

Format	Accuracy	Questions scored	Average few-shot examples
letter	25.0%	14,037 (skipped 5)	4.5
cloze	25.9%	14,037 (skipped 5)	0.0

Category	Subjects	Questions	Letter	Cloze
STEM	19	3,153	27.1%	24.0%
Humanities	13	4,705	24.9%	26.0%
Social Sciences	12	3,077	22.0%	26.7%
Other	13	3,102	26.2%	26.9%

Biology & medicine (cloze): 27.6% on 2,089 questions, +2.6 points against 25% chance (about 2.8 standard errors, significant). This pools the 10 life-science and health subjects; it isn't an official MMLU category.

In the letter format the model compares " A"–" D", with same-subject examples added while they fit the context. In the cloze format each answer's text is scored by log-probability per byte; at this size the cloze results are the meaningful ones. Chance is 25%.

Base vs instruct

Both are scored on the same raw-text prompts, without the chat template, so the numbers are directly comparable. SFT mainly teaches format, turn-taking and when to stop, so large changes in knowledge benchmarks aren't expected. Chance on the multiple-choice tasks is 25%.

Metric	Base	Instruct
SciQ (acc_norm)	53.9%	55.3%
ARC-Easy (acc_norm)	32.4%	32.2%
HellaSwag (acc_norm)	26.3%	26.7%
MMLU (letter)	25.2%	25.0%
MMLU (cloze)	25.7%	25.9%
MMLU biology & medicine (cloze)	27.8%	27.6%

Compared with its siblings and other small models

	This model	tiny	Pythia-70M	GPT-2	Supra-50M
Parameters (total)	3.8M	2.0M	70.4M	124.4M	51.8M
Active non-embedding parameters	0.5M	0.5M	18.9M	85.1M	35.4M
Training tokens	1,000M	1,000M	300B (the Pile)	undisclosed (WebText)	20B (FineWeb-Edu)
SciQ (acc_norm)	55.3%	56.3%	55.2%	53.2%	77.2%
ARC-Easy (acc_norm)	32.2%	32.7%	35.0%	42.0%	52.2%
HellaSwag (acc_norm)	26.7%	26.7%	–	29.5%	31.8%
MMLU (cloze)	25.9%	25.8%	–	–	–
MMLU biology & medicine (cloze)	27.6%	28.4%	–	–	–
Validation loss (held-out mix)	–	3.869	–	–	–

LittleKedi columns are measured with this repo's evals.py and mmlu_eval.py on the checkpoints named. Validation losses are only comparable between models that share a tokenizer and validation set. The other columns are published figures: Pythia-70M from EleutherAI's evaluations as quoted on the Wisp-15M card, GPT-2 and Supra-50M from the Supra-50M-Base card. Their prompts may differ from ours, so treat gaps of a few points as noise.

What it can and can't do

This is the instruction-tuned sparsest tiny model: the same 0.5M active non-embedding parameters per token as tiny-instruct, with ~3× the expert capacity. Fine-tuning taught it the shape of a conversation, and it fits the chat data better than tiny (held-out SFT loss 3.15 against 3.30), but it's still far too small to be an assistant. It's a research artifact for studying sparsity and instruction tuning at the smallest scale.

What it does

• ✅ Replies in the chat format: a short paragraph that usually ends with <|end|>.
• ✅ Stays on the topic of the question more often than tiny-instruct: "What's a good name for a cat?" → "A good name for a cat is…"; asked about exam stress, it talks about sleep and ends "Good luck…"; asked how radio changed news, it talks about media and technology.
• ✅ Writes grammatical English in the register of an answer.

What it can't do

• ❌ Give correct answers. On-topic replies are still invented: "What causes the seasons on Earth?" → "Air-directed volcanoes are critical to the long-term impact of the POW earthquake."
• ❌ Facts, even about text from its own pretraining data. Treat every name, date and number as made up.
• ❌ Arithmetic. Asked "What is 7 + 6?" it fell into a sub-word loop (ahvahvahv…) and never stopped. The repetition penalty doesn't catch loops of word fragments, so cap the reply length.
• ❌ Creative requests, instructions, code.

Benchmarks: about where the base model was (SciQ 55.3%, ARC-Easy 32.2%, MMLU cloze 25.9%): instruction tuning changes the format, not what the model knows.

Decoding: temperature ≈ 0.7, top-p ≈ 0.9, repetition penalty ≈ 1.15 (the GGUF files carry these as defaults), and a modest maximum length.

Usage

llama.cpp (GGUF, llama.cpp's built-in deepseek2 architecture; the chat template is embedded):

llama-cli -m LittleKedi-tiny-wide-sparse-instruct-q8_0.gguf -cnv --temp 0.7 --top-p 0.9 --repeat-penalty 1.15

Ollama (uses the bundled template and params files): ollama run hf.co/edededdy/LittleKedi-tiny-wide-sparse-instruct-GGUF:Q8_0

PyTorch (needs the littlekedi package from the training code):

import json, torch
from safetensors.torch import load_file
from littlekedi import LittleKediModel, ModelConfig
from littlekedi.chat import END, render_prompt
from littlekedi.tokenizer import BPETokenizer

model = LittleKediModel(ModelConfig.from_dict(json.load(open("config.json")))).eval()
model.load_state_dict(load_file("model.safetensors"))
tok = BPETokenizer.from_file("tokenizer.json")
ids = torch.tensor([render_prompt(tok, [{"role": "user", "content": "What causes the seasons?"}])])
out = model.generate(ids, 200, temperature=0.7, top_p=0.9, repetition_penalty=1.15, stop_ids={tok.token_to_id(END)})
print(tok.decode(out[0, ids.shape[1]:].tolist()).replace(END, "").strip())

Or in a terminal: python chat.py --ckpt <checkpoint>.pt.

Files

File	Contents
LittleKedi-tiny-wide-sparse-instruct-f16.gguf	GGUF (f16) for llama.cpp / Ollama / LM Studio
LittleKedi-tiny-wide-sparse-instruct-q8_0.gguf	GGUF (q8_0) for llama.cpp / Ollama / LM Studio
template	Ollama chat template
params	Ollama stop tokens and sampling defaults
chat_template.jinja	Chat template (Jinja), the same one embedded in the GGUF files
little_kedi.jpg	The LittleKedi logo

Acknowledgements

• The architecture follows DeepSeek-V3 (DeepSeek-AI, 2024): MLA, DeepSeekMoE and auxiliary-loss-free balancing.
• Muon (Keller Jordan et al., 2024) with Moonlight's update scaling (Liu et al., 2025).
• Pretraining data: FineWeb-Edu (ODC-BY 1.0).
• Pretraining data: Cosmopedia (Apache 2.0).
• Pretraining data: FineMath 4+ (ODC-BY 1.0).
• Pretraining data: SmolTalk (Apache 2.0 for its new subsets, with the datasets it includes under their own licenses).
• Pretraining data: github-code-clean (Apache 2.0, with each file under its own open-source license).
• Chat data: see the SFT table above for its sources, each under its own license.

Card generated 2026-10-13 by prepare_release.py from tiny-wide-sparse-sft.pt (step 15257).