Modujo model weights
This repository keeps model variants in self-contained subdirectories. The repository name is retained for compatibility, but the repository root no longer contains model weights and must not be loaded directly.
Weight directories
| Path | Model | Meaning | Status |
|---|---|---|---|
pretrain/Modujo-1B-A0.75B/ | Modujo-1B-A0.75B | Continued-pretraining base model: 1B total scale and A0.75B active scale | Available |
| Repository root | Historical 9B-A1B architecture metadata and shared tokenizer files | The original random-initialized 9B weight shards were removed; this is not a loadable model directory | No weights |
There are currently no SFT, chat, RL, or looped-model weights in this repository. New variants should be published in their own named directories so their training stage and actual parameter size remain explicit.
Pretrained base model
pretrain/Modujo-1B-A0.75B/ is the released pretraining artifact.
- Architecture:
Qwen4ExpForCausalLM - Model size: 1B total scale
- Active size: approximately A0.75B per token
- 36 layers with 8 routed experts per layer, top-2 routing, and one shared expert
- Attention layout: repeating 3 Gated DeltaNet layers + 1 dense-attention layer
- Context configuration: 32K maximum positions; training sequences were up to 2,048 tokens
- Weight format: BF16 safetensors
- Training stage: continued-pretraining base model
- Not instruction-tuned and not intended to be treated as a chat model
- No QSA sparse indexer in this release
Detailed machine-readable size metadata is recorded in
parameter_summary.json.
Loading
Pass the subdirectory explicitly:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "Alexhu1999/Modujo-9B-A1B"
subfolder = "pretrain/Modujo-1B-A0.75B"
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = AutoModelForCausalLM.from_pretrained(
repo_id,
subfolder=subfolder,
torch_dtype=torch.bfloat16,
device_map="auto",
)
Loading only Alexhu1999/Modujo-9B-A1B without subfolder will fail because
there are intentionally no weights at the repository root.
Planned experiments
The current base model will be evaluated through three separate tracks:
- SFT: improve instruction following, response quality, repetition control, and multi-turn dialogue stability.
- QSA: add and train sparse attention indexers, then compare long-context quality, inference speed, and memory use against dense attention.
- Looped Transformer: reuse selected Transformer layers to test whether deeper computation with shared parameters provides a practical quality and efficiency benefit.
These tracks will be evaluated independently before any combined model is considered. Future weights will use separate directories with explicit names.
Limitations
This is a base language model checkpoint. It can perform short text completion, but long generations may repeat and instruction following is not yet stable. Use a separately identified SFT or aligned release for assistant/chat use when one becomes available.