| --- |
| 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. |
|
|
