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.

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.

"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

Training

Fine-tuning (SFT)

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

Evaluation

Zero-shot benchmarks (evals.py)

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)

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%.

Compared with its siblings and other small models

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

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).