This repository contains an intermediate checkpoint of my German base-model experiment. I trained the tokenizer and model from random initialization. The release does not use an imported tokenizer vocabulary, pretrained weights, distillation, or synthetic training text.

The checkpoint was saved after 10,000 optimizer steps, equivalent to 5,242,880,000 training tokens at the configured global batch size. The prepared corpus contains 30.00B training tokens, but this checkpoint has not completed that training target.

This is a base model for text completion, not a chatbot. It has not been instruction-tuned and should not be expected to answer questions or follow commands reliably.

Release contents

model.safetensors: inference weights from step 10,000
config.json: architecture configuration
german_transformer_model.py: custom PyTorch model implementation
bpe_tokenizer.py: custom byte-level BPE implementation
inference.py: offline Safetensors generation CLI
tokenizer/de_bpe_32k/: matching vocabulary, merges, and special tokens
samples/generations_5k_vs_10k.md: deterministic checkpoint comparison
samples/training_curves/: loss, perplexity, optimization, and throughput plots
SHA256SUMS: integrity hashes for the model, runtime code, and tokenizer

The optimizer and scheduler states are intentionally excluded. The original resumable checkpoint is a separate training artifact.

Model architecture

Property	Value
Architecture	Decoder-only Transformer
Parameters	341,885,952
Layers	24
Model width	1,024
Attention	16-head MHA, head dimension 64
Feed-forward network	SwiGLU, width 2,816
Normalization	Pre-RMSNorm
Positions	RoPE, theta 10,000
Context length	2,048 tokens
Vocabulary	32,768-token byte-level BPE
Embeddings	Tied input/output embeddings
Dropout	0.0
Training precision	bfloat16

I implemented the architecture directly with PyTorch. It is not registered with the Hugging Face Transformers AutoModel classes, so trust_remote_code and AutoModelForCausalLM are not supported by this release. The complete training and data-preparation code is available in the GitHub repository.

Tokenizer

The tokenizer is a byte-level BPE tokenizer trained for this experiment on a sample from German Commons. Its fixed IDs are:

ID	Token
0	`<unk>`
1	`<pad>`
2	`<s>`
3	`</s>`
4	`</w>`

IDs 5 through 260 represent the 256 byte values. Higher IDs represent learned BPE merges. The tokenizer uses </w> as an explicit word boundary marker.

Training data

The prepared data was streamed from German Commons, quality-filtered, domain-reweighted, and encoded into local uint16 shards. The prepared dataset contains:

30,000,000,243 training tokens
50,002,350 validation tokens
a stable-hash document split with seed 1337
aggregate source and license counts recorded during preparation

The accepted-document license distribution recorded in the preparation manifest was approximately 51.6% CC0-1.0, 46.2% CC-BY-4.0, and 2.2% CC-BY-SA-4.0. These percentages count accepted documents, not tokens.

German Commons contains a substantial amount of historical newspaper and cultural material. Domain reweighting reduced that concentration but did not remove it. The resulting model frequently uses historical spelling and register.

Training configuration

Property	Value
Checkpoint step	10,000
Tokens consumed	5.243B
Prepared training target	30B tokens
Optimizer	AdamW
Adam betas	0.9, 0.95
Weight decay	0.1
Gradient clipping	1.0
Peak learning rate	3e-4
Warmup	2,000 steps
Schedule	Cosine decay to 3e-5
Global batch	524,288 tokens per step
Micro-batch	16 sequences
Gradient accumulation	16
Hardware	1 NVIDIA H100 PCIe 80 GB
Measured throughput	approximately 60,000 tokens/second
Compile mode	disabled for this run

At step 10,000, the recorded training loss was 3.2307. Evaluation over 20 batches from the held-out validation shards produced a validation loss of 3.2593 and perplexity of 26.03. This is a training diagnostic, not a broad language-model benchmark.

Evaluation status

No standardized downstream benchmark results are reported for this checkpoint. The available evaluation consists of held-out German Commons loss and direct inspection of deterministic completions. The 5k and 10k comparison is published in samples/generations_5k_vs_10k.md.

Those samples are intentionally unedited. They show improved German structure at step 10,000, but also repetition, historical register, and severe factual fabrication.

Usage

Install PyTorch and Safetensors, clone or download this repository, and run from its root:

bash

python inference.py \
  --model-dir . \
  --prompt "Die deutsche Sprache ist" \
  --max-new-tokens 80 \
  --temperature 0.8 \
  --top-k 40 \
  --top-p 0.9

The underlying loading path is plain PyTorch:

python

import json

import torch
from safetensors.torch import load_file

from bpe_tokenizer import BPE_Tokenizer
from german_transformer_model import GermanGPT, GermanGPTConfig


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

with open("config.json", "r", encoding="utf-8") as file:
    config = GermanGPTConfig.from_dict(json.load(file))

tokenizer = BPE_Tokenizer.load("tokenizer/de_bpe_32k")
model = GermanGPT(config)
state_dict = load_file("model.safetensors", device="cpu")
model.load_state_dict(state_dict, strict=True)
model.to(device).eval()

prompt = "Die deutsche Sprache ist"
input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long, device=device)

with torch.inference_mode():
    logits, _ = model(input_ids)

next_token = int(torch.argmax(logits[0, -1]).item())
print(tokenizer.decode(input_ids[0].tolist() + [next_token]))

This example performs one greedy next-token step. Autoregressive sampling can be implemented by repeatedly passing the most recent 2,048 tokens through the model and applying temperature, top-k, or top-p filtering.

Intended use

Research on German base-model pretraining and tokenization
Inspection of training behavior at an early checkpoint
Continuation training and controlled fine-tuning experiments
Non-factual German text-completion experiments

Limitations

Intermediate checkpoint. The model has consumed about 17.5% of the prepared 30B-token target.
Not instruction-tuned. Question answering and chat prompts are outside the model's training objective.
Unreliable facts. The model confidently invents people, places, dates, quantities, and relationships. Its output must not be treated as evidence.
Repetition. Longer generations often enter repeated phrases or sentence structures.
Historical register. Archaic spelling and phrasing occur frequently due to the composition of the accepted training documents.
German-focused. Other languages were not evaluated and are outside the intended scope.
Inherited corpus bias. Filtering and provenance records do not remove social, historical, geographic, or source-selection bias.
No safety evaluation. I have not completed red-team, memorization, privacy, toxicity, or demographic-bias evaluations.

Do not use this checkpoint for medical, legal, financial, emergency, or other high-impact decisions. It is not suitable for autonomous decision-making about people.

Licensing and attribution

The project code in this repository is licensed under the MIT License; see LICENSE.
The model weights are released under CC BY-SA 4.0 as stated in the repository metadata.
German Commons and each constituent source retain their own attribution and licensing requirements. Users are responsible for reviewing those terms for their intended use.

Please attribute both Hamid Wakili for this model release and German Commons as the training-data source.

Citation

bibtex

@misc{wakili2026germancommons350m,
  author = {Hamid Wakili},
  title = {German Commons 350M, Step 10,000},
  year = {2026},
  note = {Intermediate German base-model checkpoint},
  url = {https://github.com/ideployed/sovereign-german-llm}
}

ideployed/german-commons-350m

German Commons 350M, step 10,000