LittleKedi-medium-wide-sparse-instruct

LittleKedi-medium-wide-sparse-instruct is the instruction-tuned (chat) version of LittleKedi-medium-wide-sparse-base, a small DeepSeek-V3-style Mixture-of-Experts language model trained from scratch. It has 305.4M parameters in total, of which 31.7M are active per token (23.29M excluding the embedding table). It was pretrained on a 20,400M-token mix (FineWeb-Edu, Cosmopedia v2 (SmolLM-Corpus), FineMath 4+, Reddit comments, github-code-clean, SmolTalk, Wikipedia (English, 2023-11-01)).

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 medium-wide-sparse, the wide-sparse medium model: 31.7M of its 305.4M parameters (10%) are active per token. Its siblings are medium and medium-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	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	45.5M	18.9M	12.6M	42%	24 × 192, top-4
moderate-sparse	sparse	16K	93.5M	19.1M	12.8M	20%	120 × 96, top-8
moderate-wide-sparse	wide-sparse	16K	173.4M	19.4M	13.1M	11%	240 × 96, top-8
medium	standard	16K	78.2M	31.0M	22.6M	40%	24 × 256, top-4
medium-sparse	sparse	16K	163.4M	31.3M	22.9M	19%	120 × 128, top-8
medium-wide-sparse ← this model	wide-sparse	16K	305.4M	31.7M	23.3M	10%	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	305.36M total, 31.68M active per token (10%), 23.29M active non-embedding
Layers / hidden size	8 / 512 (the first 2 layers have a dense SwiGLU FFN of 1408; layers 2–7 are MoE)
Attention	Multi-head Latent Attention (MLA): 8 heads, KV latent 128, query latent 256, decoupled RoPE dim 32, head dims 64+32 (q/k) and 64 (v). The KV cache stores only the 128-dim latent plus a 32-dim RoPE key per token
MoE	DeepSeekMoE: 240 routed experts (SwiGLU, 128 hidden), top-8, plus 2 shared experts; sigmoid gating; group-limited routing (8 groups, top-4)
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 1.92× the mean load on the busiest expert (2.28× in the worst layer), and 0.26% 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	2,048 tokens

Training

Data	20,400M tokens, 27,145,344 documents (~67 tokens per parameter, ~644 per active parameter), one pass, sources interleaved throughout (mix below)
Tokenizer	Byte-level BPE, 16,384 tokens (~4.00 bytes/token on this mix); digits split individually; chat special tokens built in
Steps	311,279 × 65,536 tokens (batch 16 × 2048 × 2 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 311,279 steps)
Precision	bf16 autocast, torch.compile
Compute	~2.9e18 FLOPs (6 × active non-embedding parameters × tokens)
Hardware	One NVIDIA GeForce RTX 4090 (RunPod): about 76.7 hours at a median ~73.9K tokens/s

Source	What it is	Tokens	Share
FineWeb-Edu sample/100BT	educational web pages	11,820M	58%
Cosmopedia v2 (SmolLM-Corpus)	synthetic textbooks and stories	2,446M	12%
FineMath 4+	maths web pages	1,875M	9%
Reddit comments	forum comments	1,630M	8%
github-code-clean	source code	958M	5%
SmolTalk	chat conversations	917M	4%
Wikipedia (English, 2023-11-01)	encyclopedia articles	754M	4%

Fine-tuning (SFT)

Base model	LittleKedi-medium-wide-sparse-base
Conversations	22,841 (22,841 train / 1,201 validation), 2.25M assistant tokens trained on
Data	docs/medium-sft-data.md
Loss	On assistant replies and their closing `<
Schedule	2,856 steps, batch 8, 1 epoch, Muon + AdamW, peak LR 5e-5, 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 1,201 held-out conversations (assistant tokens only): 1.973 → 1.859.

Evaluation

Zero-shot benchmarks (evals.py)

Task	acc_norm	acc	Items	Chance
SciQ	73.8% ± 1.4%	79.0%	1,000	25%
ARC-Easy	46.1% ± 1.0%	49.5%	2,376	25%
HellaSwag	35.6% ± 0.5%	31.0%	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	24.6%	14,042 (skipped 0)	4.9
cloze	27.4%	14,042 (skipped 0)	0.0

Category	Subjects	Questions	Letter	Cloze
STEM	19	3,153	24.3%	26.0%
Humanities	13	4,705	24.9%	26.0%
Social Sciences	12	3,077	23.7%	29.4%
Other	13	3,107	25.2%	29.0%

Biology & medicine (cloze): 28.6% on 2,094 questions, +3.6 points against 25% chance (about 3.8 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)	72.2%	73.8%
ARC-Easy (acc_norm)	47.1%	46.1%
HellaSwag (acc_norm)	36.0%	35.6%
MMLU (letter)	24.5%	24.6%
MMLU (cloze)	27.8%	27.4%
MMLU biology & medicine (cloze)	29.9%	28.6%

Compared with its siblings and other small models

	This model	Pythia-70M	GPT-2	Supra-50M
Parameters (total)	305.4M	70.4M	124.4M	51.8M
Active non-embedding parameters	23.3M	18.9M	85.1M	35.4M
Training tokens	20,400M	300B (the Pile)	undisclosed (WebText)	20B (FineWeb-Edu)
SciQ (acc_norm)	73.8%	55.2%	53.2%	77.2%
ARC-Easy (acc_norm)	46.1%	35.0%	42.0%	52.2%
HellaSwag (acc_norm)	35.6%	–	29.5%	31.8%
MMLU (cloze)	27.4%	–	–	–
MMLU biology & medicine (cloze)	28.6%	–	–	–
Validation loss (held-out mix)	–	–	–	–

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 medium model: medium-wide-sparse-base (23.3M active non-embedding parameters per token, 240 experts, 305M in total) fine-tuned for one epoch on ~24K conversations, most of them written by DeepSeek V4.1 Flash for medium's 2,048-token context. It's the largest LittleKedi chat model: it answers in fuller, better-organised paragraphs than moderate-wide-sparse-instruct, gets the gist of common-knowledge questions right more often, and writes the simplest Python functions correctly. It still makes up specific details freely, with the same confidence as the right ones: a research artifact for studying sparsity and instruction tuning at small scale, not a source of information.

Who it is

It answers as LittleKedi, a small cat from Istanbul who has read a lot ("kedi" is Turkish for cat), and says it's a small language model that makes mistakes: "Who are you?" → "I'm LittleKedi! I'm a little cat from Istanbul, and I've spent a lot of time curled up with books. I'm happy to chat, explain things or help you think something through." The persona stays out of ordinary answers.

What it does

• ✅ Replies in the chat format and stops cleanly; greets back ("Hi!" → "Hello! I'm LittleKedi. How can I help you today?", 3 of 3 seeds).
• ✅ Gets the core of common-knowledge questions right more often than not: "What causes the seasons on Earth?" → "the tilt of the Earth's axis… about 23.5 degrees" (3 of 3 seeds name the tilt); "What is photosynthesis?" → "the process by which plants convert light energy into chemical energy. It uses sunlight, carbon dioxide, and water to create glucose"; a black hole is "a region in the universe where gravity is so strong that nothing can escape it, even light itself".
• ✅ Gives sensible, well-structured lists for everyday advice (sleep: a consistent schedule, a relaxing environment, no caffeine, alcohol or screens before bed, relaxation techniques).
• ✅ Writes the very simplest functions correctly: asked for the largest number in a list, it returned return max(lst) on one seed of three.
• ✅ Shortens a wordy sentence sometimes: "The committee has made the determination that the proposal will not be moving forward at this point in time" → "The committee has decided that the proposal is not moving forward."

What it can't do

• ❌ Specific details reliably. The main fact can be right and everything around it invented: Paris is "in the southwestern part of the country… with a population of about 36 million"; Einstein is "an American mathematician" on one seed and "a British biochemist… best known for his work on the structure of DNA" on another; one seed answers "What is the capital of France?" with "Louis XIV."
• ❌ Arithmetic reliably: 38.3% on 300 held-out one- and two-digit sums, differences and products (greedy decoding), a little below moderate-wide-sparse-instruct's 43.7%; "7 + 6" got 10, 12 and 12.
• ❌ Working code beyond one-liners. It writes correctly shaped Python but solves only 3.0% of 500 held-out MBPP problems (moderate-instruct: 1.8%); other seeds wrapped the right idea in a wrong max(nums, key=lambda x: x[1]).
• ❌ Explaining mechanisms: how a volcano erupts or how a virus differs from a bacterium comes out as fluent, confident nonsense ("a virus is a tiny, single-stranded virus"; magma reaching "10,000 degrees Fahrenheit").
• ❌ Staying faithful to a given text: asked to simplify a sentence, it sometimes reverses its meaning ("The committee has decided to move ahead with the project") and then explains changes it didn't make.
• ❌ Counting what it was asked for: asked for three sleep tips, it gave five on two seeds of three.

Benchmarks: about where the base model was (SciQ 73.8% vs 72.2%, ARC-Easy 46.1% vs 47.1%, HellaSwag 35.6% vs 36.0%, PIQA 63.9% vs 64.7%, acc_norm; MMLU cloze 27.4% vs 27.8%): instruction tuning changed the format, not what it 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. The context window is 2,048 tokens, prompt and reply together.

Usage

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

llama-cli -m LittleKedi-medium-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-medium-wide-sparse-instruct: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
model.safetensors	PyTorch weights (embedding tied with the output head), littlekedi parameter names
config.json	ModelConfig for littlekedi
tokenizer.json	Byte-level BPE tokenizer (Hugging Face tokenizers format)
chat_template.jinja	Chat template (Jinja), the same one embedded in the GGUF files
tokenizer_config.json	Special tokens (`<
template	Ollama chat template
params	Ollama stop tokens and sampling defaults
LittleKedi-medium-wide-sparse-instruct-f16.gguf	GGUF (f16) for llama.cpp / Ollama / LM Studio
LittleKedi-medium-wide-sparse-instruct-q8_0.gguf	GGUF (q8_0) for llama.cpp / Ollama / LM Studio
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 v2 (ODC-BY 1.0).
• Pretraining data: FineMath 4+ (ODC-BY 1.0).
• Pretraining data: Reddit comments (no license stated on the dataset card; the comments are Reddit users' content).
• Pretraining data: github-code-clean (Apache 2.0, with each file under its own open-source license).
• Pretraining data: SmolTalk (Apache 2.0 for its new subsets, with the datasets it includes under their own licenses).
• Pretraining data: Wikipedia (CC BY-SA 3.0 and GFDL).
• Chat data: see the SFT table above for its sources, each under its own license.

Card generated 2026-11-01 by prepare_release.py from medium-wide-sparse-sft.pt (step 311278).