---
license: apache-2.0
language:
- en
datasets:
- allenai/c4
tags:
- pytorch
- transformers
- gpt
- yatnmn
- nmn
- chinchilla
- nanochat
- text-generation
library_name: transformers
pipeline_tag: text-generation
base_model: mlnomad/yatnmn-softplus-d12-chinchilla-261M
---

# YatNMN-Softplus d=12 Chinchilla (261M) — PyTorch / HuggingFace Transformers

A 261M-parameter **nanochat-architecture GPT** with the **YatNMN-Softplus MLP** (per-neuron bias, softplus-positive bias, learnable epsilon, learnable α). Trained in JAX/Flax on TPU v6e-8 to Chinchilla-optimal token budget on C4, then **ported to PyTorch** for easy inference via the HuggingFace `transformers` API.

This is the **best-performing 261M model in the ablation series**:

| MLP variant | Final smooth loss | vs GELU |
|---|---|---|
| **YatNMN-Softplus (per-neuron)** | **2.98** | **−0.13** |
| YatNMN-Softplus + scalar_bias | 3.06 | −0.05 |
| GELU | 3.11 | baseline |

Weights are bit-exact with the Flax checkpoint ([`mlnomad/yatnmn-softplus-d12-chinchilla-261M`](https://huggingface.co/mlnomad/yatnmn-softplus-d12-chinchilla-261M)) — parity validated at **max |Δ logits| = 1.5e-5** on CPU/fp32.

## Quick start

```bash
pip install torch transformers safetensors
```

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "mlnomad/yatnmn-softplus-d12-chinchilla-261M-pytorch",
    trust_remote_code=True,
    dtype=torch.float32,
).eval()

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

prompt = "The meaning of life is"
ids = tokenizer(prompt, return_tensors="pt").input_ids
with torch.no_grad():
    out = model.generate(
        ids, max_new_tokens=50,
        do_sample=True, temperature=0.8, top_p=0.9,
        use_cache=True, pad_token_id=tokenizer.eos_token_id or 0,
    )
print(tokenizer.decode(out[0], skip_special_tokens=True))
```

Greedy completion samples:

> **"The meaning of life is"** → *the same as life. The meaning of life is the same as life…*
>
> **"Once upon a time,"** → *the world was a place where people could live and work. The world was a place where people could…*

## YatNMN-Softplus MLP

Each MLP block uses the YatNMN nonlinearity from [`nmn>=0.2.29`](https://pypi.org/project/nmn/):

```
y = α · (x · W + softplus(b))² / (||x − W||² + softplus(ε))
```

with **per-neuron bias** `b` of shape `(4·n_embd,) = (3072,)`, **scalar learnable epsilon** of shape `(1,)`, and **scalar learnable α** of shape `(1,)`. Both bias and epsilon are passed through softplus to keep them strictly positive. The MLP is then `c_proj` (Linear → 768) on top of YatNMN's output.

## Model details

| | |
|---|---|
| **Parameters** | 261,133,226 |
| **Architecture** | Nanochat-style GPT with YatNMN-Softplus MLP (ported from JAX/Flax NNX) |
| **Config** | d=12, n_embd=768, n_head=12, n_kv_head=12, seq_len=1024, tied embeddings, SSSL sliding window |
| **Training data** | `allenai/c4` (English split), 5.22 B tokens (Chinchilla 20×) |
| **Tokenizer** | `mistralai/Mistral-7B-v0.1` (vocab 32,768) |
| **Optimizer** | plain AdamW, peak LR 0.03, warmup-cosine |
| **Hardware** | TPU v6e-8 (TRC), europe-west4-a |
| **Final loss (smooth)** | **2.98** |

## Architecture features

Full nanochat stack, faithfully ported to PyTorch:

- **YatNMN-Softplus MLP** (per-neuron bias, softplus-positive, learnable α and ε)
- **RoPE** (base 100,000), split-half layout
- **MHA** (n_head = n_kv_head = 12; the code supports GQA via n_kv_head < n_head, but all d=12 models use full MHA)
- **QK-norm** with 1.2× scaling (after RoPE)
- **Parameterless RMSNorm** (no learnable gain) post-embedding and per block
- **Sliding-window attention** with `"SSSL"` pattern
- **Tied embeddings** (lm_head = wte.T)
- **Value embeddings** on alternating layers (ResFormer-style)
- **Per-layer learnable residual scalars** (`resid_lambdas`, `x0_lambdas`)
- **Smear** — learnable gate on first 24 dims of token embedding mixes in prev token
- **Backout** — subtract mid-layer residual from late layers
- **Logit soft-cap**: `15 · tanh(logits / 15)`
- **No biases** in any Linear

## KV cache

The `YatGPTForCausalLM` class implements a smear-aware KV cache for fast autoregressive generation. KV-cache parity vs full forward is validated at **max |Δ| < 3e-5**. Pass `use_cache=True` (the default for `.generate()`).

## Files in this repo

```
.
├── config.json                       # HF config with auto_map → the classes below
├── generation_config.json
├── model.safetensors                 # ~1.04 GB, fp32 weights + persistent RoPE buffers
├── yatnmn_gpt.py                     # pure PyTorch Yat_GPT module + YatNMN layer
├── torch_gpt.py                      # shared building blocks (RMSNorm, RoPE, attention)
├── configuration_yatnmn_gpt.py       # PretrainedConfig subclass
├── modeling_yatnmn_gpt.py            # PreTrainedModel + GenerationMixin wrapper with KV cache
└── README.md
```

## Related

- [`mlnomad/yatnmn-softplus-d12-chinchilla-261M`](https://huggingface.co/mlnomad/yatnmn-softplus-d12-chinchilla-261M) — original JAX/Flax Orbax checkpoint (model + AdamW optimizer state, resumable)
- [`mlnomad/gelu-d12-chinchilla-261M-pytorch`](https://huggingface.co/mlnomad/gelu-d12-chinchilla-261M-pytorch) — GELU baseline at identical compute, smooth loss 3.11
- [flaxchat](https://github.com/mlnomadpy/flaxchat) — JAX/Flax training harness
- [`nmn`](https://pypi.org/project/nmn/) — the YatNMN layer (used at training time; not required for inference here, the nonlinearity is reimplemented in pure PyTorch)


## Wikitext-103 evaluation

| Metric | Value |
|---|---|
| **Wikitext-103 test loss** | **3.693** |
| **Wikitext-103 test PPL** | **40.15** |

Evaluated on ~330K tokens from wikitext-103 test set (model trained on C4 only — this is a zero-shot transfer metric).

## License

Apache 2.0.