File size: 2,584 Bytes
a0f6666 f8efcc2 a0f6666 cd05f07 f8efcc2 1768712 c0f5f64 1768712 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | ---
license: apache-2.0
datasets:
- HuggingFaceFW/finephrase
- HuggingFaceFW/fineweb-edu
- mlfoundations/dclm-baseline-1.0
- nampdn-ai/tiny-strange-textbooks
- ByteDance-Seed/mga-fineweb-edu
- nvidia/OpenMathInstruct-2
- AxiomicLabs/NPset-2-Python-Edu
language:
- en
tags:
- slm
- custom-architecture
- custom-tokenizer
- byte-level
- small
- fromzero
- negative-v1.0
---
# Negative-v1.0
Negative-v1.0 is a 67K-parameter small language model (SLM) featuring a custom architecture inspired by Needle2. Trained entirely on CPU over 600M tokens, Negative-v1.0 utilizes a byte-level tokenizer with 4 special tokens (`<bos>`, `<eos>`, `<pad>`, `<unk>`), resulting in a compact vocabulary size of 260.
## Architecture
Negative-v1.0 employs a compact, parameter-efficient architecture incorporating Engram memory, Hadamard FFNs (parameter-free) with SwiGLU intervals, and an 8-stream topology powered by mHC.
- Vocab Size: `260`
- Max Position Embeddings: `96`
- Hidden Size: `32`
- Intermediate Size (for SwiGLU): `64`
- Total Number of Layers: `9`
- Hadamard Layers: `7`
- SwiGLU Layers: `2`
- Number of Heads: `4`
- Number of KV Heads: `2`
- Dimensions Per Head: `8`
- Use Per-Head Gating: `false`
- Use XSA: `false`
- Number of mHC Streams: `8`
- Use Engram: `true`
- Number of Engram Entries: `196`
- Engram Orders: `(4, 8)`
## Training Dataset
Negative was trained on 600 million tokens of a diverse dataset mixture comprising general web text, educational content, synthetic data, normalized code, and mathematics.
| Dataset | Share |
| :--- | :---: |
| **FineWeb-Edu** | 36.0% |
| **DCLM Baseline 1.0** | 22.9% |
| **FinePhrase** | 13.4% |
| **MGA FineWeb-Edu** | 10.3% |
| **Tiny Strange Textbooks** | 8.2% |
| **OpenMathInstruct-2** | 7.6% |
| **NPset-2 Python-Edu** | 1.6% |
## Benchmark Results
We benchmaked Negative-v1.0 on five tasks: Arc_Easy, Arc_Challenge, HellaSwag, PiQA, and ArithMark-3.0.
| Task | Metric | Score |
| :--- | :--- | :---: |
| **ARC Challenge** | `acc_norm` | 22.95% |
| **ARC Easy** | `acc_norm` | 27.65% |
| **HellaSwag** | `acc_norm` | 25.94% |
| **PIQA** | `acc_norm` | 49.62% |
| **ArithMark-3.0** | `acc_norm` | 31.50% |
Despite its compact size, Negative exhibits surprisingly competitive performance on knowledge-intensive and mathematical benchmarks within its parameter class.
## License
Apache 2.0.
## Citation
```
@misc{negative-v1.0,
title = {Negative-v1.0},
organization = {FromZero},
authors = {Paul Courneya},
year = {2026},
url = {https://huggingface.co/fromziro/Negative-v1.0]
}
``` |