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Model: DedeProGames/Kiyo-135M Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- HuggingFaceFW/fineweb-edu
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- mlfoundations/dclm-baseline-1.0
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- HuggingFaceTB/finemath
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- HuggingFaceCode/stack-v3-train
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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base_model: HuggingFaceTB/SmolLM2-135M
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tags:
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- causal-lm
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- language-model
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- base-model
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- llama
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- smollm
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- pytorch
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---
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# Kiyo-135M
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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).
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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.
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## Model Details
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| Field | Value |
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|---|---:|
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| Parameters | 134,515,008 |
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| Architecture | Llama-style decoder (SmolLM2 architecture) |
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| Layers | 30 |
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| Hidden size | 576 |
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| Intermediate size | 1,536 |
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| Attention heads | 9 |
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| KV heads | 3 |
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| Attention type | Grouped query attention |
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| Activation | SwiGLU |
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| Normalization | RMSNorm |
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| Positional encoding | RoPE (theta 100,000) |
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| Vocabulary size | 49,152 |
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| Context length | 8,192 |
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| Embeddings | Tied input/output embeddings |
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| Training tokens | 200,000,000,000 |
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| Weight format | safetensors |
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## Training Data
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| Source | Domain |
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|---|---|
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| FineWeb-Edu | General web text, education-filtered |
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| DCLM-Baseline | General web text, high-quality filtered |
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| FineMath | Mathematical reasoning |
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| Stack-v3-train | Source code |
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## Benchmarks
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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.
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| Model | Params | Overall Elo |
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|---|---:|---:|
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| **Kiyo-135M** | **134.5M** | **1,126** |
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| BananaMind-2-Pro | 139.0M | 1,124 |
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| Rose-Pro | 151.3M | 1,105 |
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| GPT-2 | 124M | 990 |
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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.
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**Detailed Kiyo-135M result**
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| Category | Accuracy | z vs. chance | Elo | Significant |
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|---|---:|---:|---:|:---:|
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| Language completion | 100.0% | +12.25 | 1,570 | * |
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| Code completion | 86.0% | +9.96 | 1,420 | * |
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| World knowledge | 80.0% | +8.98 | 1,151 | * |
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| Commonsense | 74.0% | +8.00 | 1,110 | * |
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| Logical reasoning | 58.0% | +5.39 | 1,118 | * |
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| Context tracking | 44.0% | +3.10 | 914 | * |
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| Quantitative | 32.0% | +1.14 | 913 | |
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\* = passes 1.96σ vs. chance; n=50 per category
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**By difficulty**
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| Difficulty | Accuracy |
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|---|---:|
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| Easy | 76.9% |
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| Medium | 69.2% |
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| Hard | 56.9% |
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**Summary**
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| Metric | Value |
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|---|---:|
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| Parameters | 134,515,008 |
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| Overall Elo | 1,126 |
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| Chance floor | 805 |
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| Above chance floor | +321 |
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| Raw accuracy | 67.7% |
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Scores are self-evaluated and may vary with the benchmark revision, Transformers version, dtype, hardware, and generation settings.
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## Usage
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```bash
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pip install -U transformers safetensors torch
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```
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "DedeProGames/Kiyo-135M"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
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).cuda().eval()
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prompt = "The meaning of life is "
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
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with torch.no_grad():
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output = model.generate(
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input_ids=input_ids,
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max_new_tokens=64,
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do_sample=False,
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repetition_penalty=1.1,
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pad_token_id=tokenizer.eos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## Limitations
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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.
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## License
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Apache 2.0
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