DedeProGames / Kiyo-135M-0960

Kiyo-135M-0960 is an updated checkpoint of Kiyo-135M , continuing pretraining from 200B to 1,000,000,000,000 (1T) tokens on the same data mixture: FineWeb-Edu , DCLM-Baseline , FineMath , and Stack-v3-train .

The model follows the 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-0960 uses the same architecture and initialization lineage as Kiyo-135M, extended with additional pretraining on the same data mixture.

Model Details

Training Data

Benchmarks

Self-reported results from the official BananaMind Base Bench 1.1 script, all measured with the same runner, dtype (bfloat16) and GPU.

Figures for Kiyo-135M, 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-0960 result

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

By difficulty

Summary

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

Usage

pip install -U transformers safetensors torch

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "DedeProGames/Kiyo-135M-0960"

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 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