---
language: en
license: apache-2.0
tags:
  - pytorch
  - moe
  - sparse-moe
  - bitnet
  - 1-bit
  - 4-bit
  - scratch
  - turbowarp
  - instruct
base_model: brulee-1/SSMoELM-Base
---

# SSMoELM-it

**Scratch Small MoE Language Model — Instruct** — instruction-tuned version of [SSMoELM-Base](https://huggingface.co/brulee-1/SSMoELM-Base).

- **47M total / 25.8M active parameters** (top-2 sparse routing)
- **12.1 MB** packed weights (1-bit routed experts, 4-bit attention & embedding)
- Fine-tuned on [Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) + [oasst1 EN](https://huggingface.co/datasets/OpenAssistant/oasst1)

> **Note:** The HuggingFace model card may display ~12M parameters and an "8-bit" quantization badge. Both are artifacts of reading the packed `model.safetensors` directly. The actual model has **47M parameters** quantized to **1-bit and 4-bit**.

> "Scratch" carries two meanings: built *for Scratch*, trained *from scratch*.

---

## Model Details

| | |
|---|---|
| Architecture | Decoder-only Transformer + Sparse MoE FFN |
| Total params | 47.04M |
| Active params | 25.80M (per forward pass) |
| d_model | 768 |
| Layers | 6 |
| Attention | GQA — 12 heads, kv_heads=3, head_dim=64 |
| Positional encoding | RoPE |
| Normalization | RMSNorm |
| Activation | SwiGLU |
| MoE | 8 routed experts + 1 shared expert, top-2 routing |
| d_ff (per expert) | 256 |
| Vocabulary | 8,192 (BPE, byte-fallback, English-optimized) |
| Context length | 2,048 tokens |
| Base model | SSMoELM-Base (900M token pretrain) |
| Framework | MLX (training) / PyTorch (inference) |

---

## Quantization Scheme

Same as SSMoELM-Base. See [SSMoELM-Base](https://huggingface.co/brulee-1/SSMoELM-Base) for details.

---

## Training

### Pretraining
| | |
|---|---|
| Dataset | FineWeb-Edu-score-2 (60%) + FineWeb (40%) |
| Tokens | 900M |

### Instruction Tuning (SFT)
| | |
|---|---|
| Base checkpoint | SSMoELM-Base (step 013734) |
| Dataset | Dolly-15k (CC BY-SA 3.0) + oasst1 EN (Apache 2.0) |
| Samples | ~39K (14.8K Dolly + 24K oasst1) |
| Steps | 20,000 |
| Learning rate | 1e-5 (constant) |
| Loss | Assistant tokens only |

---

## Benchmark Results (0-shot, 500 samples)

| Task | Shot | Metric | Samples | Random | Base | **Instruct** | Δ |
|---|---|---|---|---|---|---|---|
| HellaSwag | 0-shot | acc_norm | 500 | 25% | 33.4% | **33.2%** | -0.2% |
| LAMBADA | 0-shot | acc | 500 | N/A | 13.8% | **14.8%** | +1.0% |
| PIQA | 0-shot | acc_norm | 500 | 50% | 53.2% | **55.4%** | +2.2% |
| WinoGrande | 0-shot | acc | 500 | 50% | 49.6% | **49.6%** | 0% |
| ARC-Easy | 0-shot | acc_norm | 500 | 25% | 35.0% | **35.2%** | +0.2% |
| ARC-Challenge | 0-shot | acc_norm | 500 | 25% | 21.0% | **24.0%** | +3.0% |
| BoolQ | 0-shot | acc | 500 | 50% | 36.2% | **44.4%** | +8.2% |
| MMLU (57 tasks avg) | 0-shot | acc | up to 500/task | 25% | 23.4% | **23.2%** | -0.2% |

---

## Expert Routing Statistics

Measured on 136 tokens (8 diverse text samples), top-2 routing. Uniform load = 12.5%.

| Layer | E0 | E1 | E2 | E3 | E4 | E5 | E6 | E7 | CV |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 9% | 7% | 9% | 14% | 18% | **21%** | 11% | 10% | 0.35 |
| 1 | 7% | 12% | 12% | 13% | **17%** | 10% | 16% | 12% | 0.23 |
| 2 | 9% | **19%** | 15% | 14% | 9% | 15% | 12% | 7% | 0.29 |
| 3 | 12% | 6% | 13% | 9% | 13% | 9% | 17% | **20%** | 0.34 |
| 4 | 14% | 10% | 13% | 12% | 12% | **21%** | 8% | 10% | 0.31 |
| 5 | 18% | 14% | 7% | 14% | 6% | 11% | **24%** | 7% | 0.47 |

CV = coefficient of variation (lower = more balanced). No expert collapse observed.

---

## Tokenizer

- BPE, vocabulary size = 8,192
- Byte fallback enabled (no `<unk>`)
- ASCII/English-optimized segmentation

### Special Tokens

| Token | ID | Role |
|---|---|---|
| `<bos>` | 0 | sequence start |
| `<eos>` | 1 | end of sequence |
| `<pad>` | 2 | padding |
| `<\|system\|>` | 3 | system turn |
| `<\|user\|>` | 4 | user turn |
| `<\|assistant\|>` | 5 | assistant turn |
| `<\|eot\|>` | 6 | end of turn |

### Chat Template

```
<bos><|user|>
{user}<|eot|>
<|assistant|>
{response}<|eot|><eos>
```

---

## Usage

Download `inference.py` and `tokenizer.json` from this repo. Requires: `torch`, `safetensors`, `tokenizers`.

```bash
pip install torch safetensors tokenizers
```

CLI (interactive chat):
```bash
python inference.py --ckpt model.safetensors
```

Recommended decoding defaults (chosen from a small prompt sweep + manual inspection to reduce repetition and gibberish for this 47M model):

| Parameter | Value |
|---|---:|
| `temperature` | `0.0` |
| `top_k` | `1` |
| `top_p` | `0.9` |
| `repetition_penalty` | `1.3` |

These are already the defaults in `inference.py`. For more varied but less reliable text, try `--temperature 0.55 --top-k 20 --repetition-penalty 1.15`.

Single-shot:
```bash
python inference.py --ckpt model.safetensors --no-chat --prompt "Hello" --max-tokens 100 \
  --temperature 0.0 --top-k 1 --repetition-penalty 1.3
```

```python
from inference import load_packed_model, build_chat_prompt
from tokenizers import Tokenizer

model = load_packed_model("model.safetensors")
tok   = Tokenizer.from_file("tokenizer.json")

ids = build_chat_prompt(tok, history=[], user_input="What is photosynthesis?")
out = model.generate(
    ids,
    max_new_tokens=200,
    temperature=0.0,
    top_k=1,
    repetition_penalty=1.3,
)
print(tok.decode(out))
```

> **Memory:** Weights stay in packed uint8 format (12.1 MB). Peak RAM ~18 MB during inference.

---

## License

Apache 2.0