---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-edu
- mlfoundations/dclm-baseline-1.0
- HuggingFaceTB/finemath
- HuggingFaceCode/stack-v3-train
language:
- en
pipeline_tag: text-generation
library_name: transformers
base_model: HuggingFaceTB/SmolLM2-135M
tags:
- causal-lm
- language-model
- base-model
- llama
- smollm
- pytorch
---

# Kiyo-135M

Kiyo-135M is a decoder-only language model pretrained from scratch on 200B tokens drawn from [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), [DCLM-Baseline](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0), [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath), and [Stack-v3-train](https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train).

The model follows the [SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) architecture: a Llama-style decoder with grouped query attention, RMSNorm, SwiGLU MLPs, and tied input/output embeddings. SmolLM2 was chosen as a base architecture because it is specifically tuned for small-scale pretraining efficiency — narrow hidden size with a comparatively deep layer stack, and a large 49k-token vocabulary that keeps sequence lengths short for a model this size. Kiyo-135M reuses this architecture but is trained independently from a random initialization on its own data mixture, rather than starting from SmolLM2's own weights.

## Model Details

| Field | Value |
|---|---:|
| Parameters | 134,515,008 |
| Architecture | Llama-style decoder (SmolLM2 architecture) |
| Layers | 30 |
| Hidden size | 576 |
| Intermediate size | 1,536 |
| Attention heads | 9 |
| KV heads | 3 |
| Attention type | Grouped query attention |
| Activation | SwiGLU |
| Normalization | RMSNorm |
| Positional encoding | RoPE (theta 100,000) |
| Vocabulary size | 49,152 |
| Context length | 8,192 |
| Embeddings | Tied input/output embeddings |
| Training tokens | 200,000,000,000 |
| Weight format | safetensors |

## Training Data

| Source | Domain |
|---|---|
| FineWeb-Edu | General web text, education-filtered |
| DCLM-Baseline | General web text, high-quality filtered |
| FineMath | Mathematical reasoning |
| Stack-v3-train | Source code |

## Benchmarks

Self-reported results from the official [BananaMind Base Bench 1.1](https://huggingface.co/datasets/BananaMind/BananaMind-Base-Bench-1.1) script, all measured with the same runner, dtype (bfloat16) and GPU.

| Model | Params | Overall Elo |
|---|---:|---:|
| **Kiyo-135M** | **134.5M** | **1,126** |
| BananaMind-2-Pro | 139.0M | 1,124 |
| Rose-Pro | 151.3M | 1,105 |
| GPT-2 | 124M | 990 |

Figures for BananaMind-2-Pro, Rose-Pro, and GPT-2 are as self-reported on their own model cards, all against the same BananaMind Base Bench 1.1 suite.

**Detailed Kiyo-135M result**

| Category | Accuracy | z vs. chance | Elo | Significant |
|---|---:|---:|---:|:---:|
| Language completion | 100.0% | +12.25 | 1,570 | * |
| Code completion | 86.0% | +9.96 | 1,420 | * |
| World knowledge | 80.0% | +8.98 | 1,151 | * |
| Commonsense | 74.0% | +8.00 | 1,110 | * |
| Logical reasoning | 58.0% | +5.39 | 1,118 | * |
| Context tracking | 44.0% | +3.10 | 914 | * |
| Quantitative | 32.0% | +1.14 | 913 | |

\* = passes 1.96σ vs. chance; n=50 per category

**By difficulty**

| Difficulty | Accuracy |
|---|---:|
| Easy | 76.9% |
| Medium | 69.2% |
| Hard | 56.9% |

**Summary**

| Metric | Value |
|---|---:|
| Parameters | 134,515,008 |
| Overall Elo | 1,126 |
| Chance floor | 805 |
| Above chance floor | +321 |
| Raw accuracy | 67.7% |

Scores are self-evaluated and may vary with the benchmark revision, Transformers version, dtype, hardware, and generation settings.

## Usage

```bash
pip install -U transformers safetensors torch
```

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

model_id = "DedeProGames/Kiyo-135M"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
).cuda().eval()

prompt = "The meaning of life is "
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)

with torch.no_grad():
    output = model.generate(
        input_ids=input_ids,
        max_new_tokens=64,
        do_sample=False,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Limitations

This is a base model, not instruction-tuned — it continues text rather than following instructions. At 135M parameters it produces fluent, well-structured text and is strong on language completion and code, but accuracy drops on quantitative and multi-step context-tracking tasks. It can generate incorrect facts and should not be used for high-stakes decisions without verification. Keep a finite generation limit to avoid repetition or drift on long outputs.

## License

Apache 2.0