openai/gsm8k
Benchmark β’ Updated β’ 17.6k β’ 895k β’ 1.39k
This model is a specialized version of HuggingFaceTB/SmolLM-135M, fine-tuned for mathematical reasoning using Confidence-Gated Gradient Routing (CGGR).
This model was trained using a novel training strategy that selects only the "hardest" tokens for gradient updates, allowing for:
| Metric | Standard (Baseline) | CGGR (Ours) | Relative Gain |
|---|---|---|---|
| Solving Accuracy (AIME) | 8.00% | 9.50% | +18.75% |
| Training Throughput | 14,368 samples | 58,716 samples | +308% |
| Final Loss | 0.3610 | 0.0980 | -73% Error |
| Max Batch Size (12GB) | 18 | 69 | 3.8x Capacity |
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "MinimaML/SmolLM-135M-CGGR-Math"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
prompt = "Question: If x + y = 10 and x - y = 2, what is the value of x?\n\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
If you use this model or the CGGR technique in your research, please cite:
@software{cggr2026,
title={CGGR: Confidence-Gated Gradient Routing},
author={MinimaML},
year={2026},
url={https://github.com/MinimaML/CGGR}
}
Base model
HuggingFaceTB/SmolLM-135M