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DedeProGames
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Model Details
Model Provider
DedeProGames
Model Tree
HuggingFaceTB/SmolLM2-135M
Input Modalities
Output Modalities
Supported Functionality
| 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 |
| 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 |
Self-reported results from the official 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 | * |
* = 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.
bash
pip install -U transformers safetensors torch
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMmodel_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))
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.
Apache 2.0
| 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 |