---
library_name: transformers
license: apache-2.0
tags:
- oleander
- gdn2
- gated-deltanet
- gqa
- muon
- marimo
- molab
---

# Oleander-150M

**Training checkpoint** (`export`) — step **6,500 / 28,820** · **22.55%** of the planned run.

```text
steps    ██████░░░░░░░░░░░░░░░░░░░░░░  22.55%   6,500 / 28,820
tokens   ██████░░░░░░░░░░░░░░░░░░░░░░  22.93%   3.46B / 15.11B compute
phase    tst   (TST bag training ends at step 8,646)
```

Trained with the Oleander marimo trainer (molab): hybrid **GDN2** (gated delta-rule, gated-delta-net-2) and **gated GQA** layers with **block attention residuals**, MuonEq-R + AdamW, WSD schedule.

## Training progress

| | |
|---|---|
| Step | **6,500** / 28,820 |
| % complete (steps) | **22.55%** |
| Compute tokens seen | **3.46B** / 15.11B (22.93%) |
| Raw tokens seen (TST bags unpacked) | 20.79B / 37.77B |
| Cluster tokens (all 1 worker(s)) | 3.46B / 15.11B compute |
| Phase | `tst` |
| Val loss / ppl | **5.5231** / **250.40** |
| Train loss (latest) | 5.2200 |
| Throughput | 77,100 compute tok/s · 0.145 steps/s |
| Session elapsed | 0d 11h 23m · ETA 1d 18h 53m |
| Checkpoint written | 2026-11-13T07:27:27Z |

## How to load

The repo is self-contained: `config.json` carries an `auto_map` that points at `configuration_oleander.py` and `modeling_oleander.py` (shipped here), so remote code is all you need.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "CyanMonkey/Test-Subject-3"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True)

prompt = "Once upon a time"
out = model.generate(**tok(prompt, return_tensors="pt"), max_new_tokens=64,
                     do_sample=True, temperature=0.8, top_k=50)
print(tok.decode(out[0], skip_special_tokens=True))
```

The GDN2 mixers use `flash-linear-attention` kernels when they are installed and you run in bf16 on a GPU (`pip install --no-deps fla-core flash-linear-attention`); otherwise a pure-PyTorch recurrent reference is used — the same math, but slower for long prompts. There is no KV cache (`use_cache=False`).

## Architecture

```text
Oleander-150M (28L = 7 x [GDN2 · GDN2 · GDN2 · GQA] · d=576 · SwiGLU(1600) · tied 32,770 vocab)
```

| | |
|---|---|
| Parameters | 149,976,139 (149.98 M) |
| Layers | 28 = 7 x [gdn2, gdn2, gdn2, gqa] |
| Hidden size / FFN | 576 / 1600 (SwiGLU) |
| Vocab / context | 32,770 (tied embeddings) / 1,024 |
| Features | GDN2 (gated delta net 2) x21: 9h x 64, conv4, gated GQA x7: 9 Q / 3 KV heads, head_dim 64, QK-Norm (offset form), partial RoPE (rotary_dim 32, theta 100000), value residual (v_mix), SwiGLU, RMSNorm, AttnRes (block attention residuals), tied embeddings, TST (token superposition training, bag 6) |

## Training setup

| | |
|---|---|
| Optimizer | muoneq_r (Muon) + AdamW — `muon_adamw` |
| LR schedule | WSD · warmup 100 · stable until 82.5% · 1-sqrt cooldown |
| Batch | 524,288 compute tokens / step (32 x 16 x 1024) |
| TST | True · bag 6 · first 30% of steps |
| Data | `CyanMonkey/NextGen-1-32-1024` (train), 14,753,933 rows x 1024 tokens |
| Precision | bf16 autocast · weights exported as float32 |

## Files

| file | what |
|---|---|
| `model.safetensors` | weights, trainer parameter names |
| `config.json` | `auto_map` -> `AutoConfig` / `AutoModelForCausalLM` remote-code entry points |
| `configuration_oleander.py` | `OleanderConfig` |
| `modeling_oleander.py` | `OleanderForCausalLM` (same math as the trainer) |
| `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json` | tokenizer |
| `training_meta.json` | detailed run metadata (this checkpoint) |
| `progress.json` | flat step / tokens / % summary |
| `generation_config.json`, `train_config.json` | sampling defaults, full trainer config |

## Notes

- This is a **work-in-progress** checkpoint at 22.55% of the planned run, not a finished model: expect repetition and incoherence, especially with few-shot prompts.
- The tokenizer comes from the base model the run started from; the vocabulary is a plain BPE model and is not extended during training.
- `training_meta.json` is regenerated on every push and is the authoritative record of how far training got.