ShallowSeek-mini-base-GGUF

GGUF builds of ShallowSeek-mini-base for llama.cpp, Ollama and LM Studio. The PyTorch weights (model.safetensors), config and tokenizer are in the main repo.

ShallowSeek-mini-base is a tiny DeepSeek-V3-style Mixture-of-Experts language model trained from scratch on a laptop CPU. It has 11.0M parameters in total, of which 6.3M are active per token. It was pretrained on 286M tokens of FineWeb-Edu.

Part of the ShallowSeek family. Not affiliated with DeepSeek. The name is a nod to the architecture it borrows, at a fraction of the depth.

This is the base model, before any instruction tuning. It continues text; it doesn't follow instructions. It's an educational and research artifact: it writes fluent, on-topic English but knows very few facts and confidently makes things up. Don't use it for anything that matters.

Architecture

It follows the main ideas of DeepSeek-V3, scaled down:

Component	This model
Layers / hidden size	8 / 192 (layer 0 has a dense FFN of 512; layers 1–7 are MoE)
Attention	Multi-head Latent Attention (MLA): 6 heads, KV latent 48, decoupled RoPE dim 16, head dims 24+16 (q/k) and 24 (v). The KV cache stores only the 48-dim latent plus a 16-dim RoPE key per token
MoE	DeepSeekMoE: 12 fine-grained routed experts (SwiGLU, 128 hidden), top-3, plus 1 shared expert; sigmoid gating; group-limited routing (3 groups, top-2)
Load balancing	Auxiliary-loss-free bias balancing (update speed 0.001) plus a small sequence-wise auxiliary loss (α = 0.0001). Expert load stayed at about 1.14× the mean (1.28× in the busiest layer) for most of training
Multi-Token Prediction	1 MTP module (λ = 0.3), used in training only. It's in model.safetensors and left out of the GGUF (1.14M extra params)
Context	1024 tokens
Tokenizer	Byte-level BPE, 8,192 tokens, trained on the same FineWeb-Edu slice (~3.9 bytes/token)

Training

Data	FineWeb-Edu sample/10BT, 236,544 documents, 286M tokens (~26 tokens per parameter), one pass
Steps	34,960 × 8,192 tokens (batch 8 × 1,024)
Optimizer	AdamW (β = 0.9, 0.95), weight decay 0.1, gradient clipping 1.0
Learning rate	1.5e-3 peak, 700 warm-up steps, cosine decay to 1.5e-4
Precision	fp32
Hardware	One 8-core Intel Core i9-9880H (2019 MacBook Pro), CPU only: about 2 days of compute at ~1.9K tokens/s

Validation loss: 5.12 (step 1,000) → 4.28 (step 5,000) → 4.06 (step 10,000) → 3.97 (step 15,000) → 3.87 (step 20,000) → 3.78 (step 25,000) → 3.70 (step 30,000) → 3.67 (step 35,000)

Evaluation

Metric	Value
Validation loss (nats/token)	3.667
Perplexity	39.1
Bits per byte	1.354

Factual probes (benchmark.py)

These are 14 fill-in-the-blank probes and 12 true-versus-false sentence pairs, scored on raw text.

Metric	Value
Probes answered at top-1 / top-5	0% / 29%
Mean log-probability of the correct answer	-5.68
True-vs-false pairs won	7/12 (58%)

Prompt	Expected	Rank of expected	Model's top-3
The Earth revolves around the	Sun	18	earth, world, Earth
Water is made of hydrogen and	oxygen	2	hydrogen, oxygen, nitrogen
The heart pumps	blood	27	are, can, have
Plants make their food through a process called	photosynthesis	96	“, the, "
The capital of France is	Paris	1,381	a, the, an
The largest planet in our solar system is	Jupiter	29	the, a, solar
World War II ended in	1945	5	the, 19, 18
HIV is a	virus	7	common, very, disease
The American flag is red, white and	blue	4	white, red, black
The sun rises in the	east	65	middle, morning, mid
Fish breathe using their	gills	41	skin, mouth, l
2 + 2 =	4	4	2, 1, 0
3 + 5 =	8	19	1, 5, 2
7 + 6 =	13	30	5, 1, 4

MMLU (mmlu_eval.py)

Format	Accuracy	Questions scored	Average few-shot examples
letter	24.9%	14,037 (skipped 5)	4.5
cloze	25.5%	14,037 (skipped 5)	0.0

By category (accuracy weighted by question count; chance is 25%):

Category	Subjects	Questions	Letter	Cloze
STEM	19	3,153	26.8%	24.0%
Humanities	13	4,705	23.8%	25.3%
Social Sciences	12	3,077	23.9%	26.1%
Other	13	3,107	25.5%	26.7%

Biology & medicine (cloze): 27.8% on 2,094 questions, against 25% chance. That's +2.8 points, about 3.0 standard errors above chance, so it's statistically significant. 9 of the 10 subjects are at or above chance. The overall cloze score is close to chance, so the signal sits mostly in the life-science and health subjects, where FineWeb-Edu's educational web text is richest. Maths-heavy subjects come out below chance, since cloze can't guess numeric answers. This group isn't an official MMLU category; it pools these 10 subjects:

Subject	Questions	Cloze accuracy
clinical knowledge	265	32.5%
human aging	223	29.6%
professional medicine	272	29.0%
virology	166	28.3%
medical genetics	100	28.0%
college medicine	173	27.4%
high school biology	310	26.8%
college biology	144	25.7%
nutrition	306	25.2%
anatomy	135	23.7%

Chance is 25%. In the letter format the model compares " A", " B", " C" and " D", with up to 5 same-subject examples included only while they fit in the 1,024-token window. In the cloze format each answer's text is scored by log-probability per byte. Small models in the letter format tend to answer the same letter within a subject, so per-subject letter scores mostly reflect how the answer key happens to be distributed. The cloze results are the meaningful ones here.

What it can and can't do

• ✅ Grammatical, topic-consistent English for a few sentences, in the register of educational web text.
• ✅ Coarse associations: it knows HIV is a virus rather than a mineral, ice is frozen water, and a week has seven days.
• ❌ Specific facts and relations (capital cities, which body orbits which), arithmetic beyond memorised cases like "2 + 2 = 4", and following instructions.
• ❌ It invents names, numbers and "facts" fluently, and falls into repetition loops at low temperature. Use top-p ≈ 0.9 and a repetition penalty of about 1.1–1.2.

Usage

llama.cpp, Ollama or LM Studio (GGUF, llama.cpp's built-in deepseek2 architecture):

llama-completion -m ShallowSeek-mini-base-q8_0.gguf -p "Photosynthesis is" -n 100 --temp 0.7 --top-p 0.9 --repeat-penalty 1.15

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

import json, torch
from safetensors.torch import load_file
from deepseek_moe import DeepSeekMoEModel, ModelConfig
from deepseek_moe.tokenizer import BPETokenizer

model = DeepSeekMoEModel(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([[tok.eot_id] + tok.encode("Photosynthesis is")])
print(tok.decode(model.generate(ids, 60, temperature=0.7, top_p=0.9, repetition_penalty=1.15)[0].tolist()))

Files

File	Contents
model.safetensors	PyTorch weights (including the MTP module), our own parameter names
config.json	ModelConfig for deepseek_moe
tokenizer.json	Byte-level BPE tokenizer (Hugging Face tokenizers format)
ShallowSeek-mini-base-f16.gguf	GGUF (f16) for llama.cpp / Ollama / LM Studio
ShallowSeek-mini-base-q8_0.gguf	GGUF (q8_0) for llama.cpp / Ollama / LM Studio

Acknowledgements

• The architecture follows DeepSeek-V3 (DeepSeek-AI, 2024): MLA, DeepSeekMoE, auxiliary-loss-free balancing and multi-token prediction.
• Pretraining data: FineWeb-Edu (Hugging Face, ODC-BY 1.0).

Card generated 2026-10-01 by prepare_release.py.