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I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet. This process is not yet automated and I download, convert, quantize, and upload them by hand, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding the model, please refer to the original model repo.

DECS_1.5B

This is the official model for ICLR 2026 Oral "Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling". DECS_1.5B is a reasoning-focused causal language model built from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B and further trained with DECS algorithm, focused on 50% fewer tokens when answering a reasoning-required problem.

Model Summary

Base model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
Upload date: 2026-02-24
Recommended use: long-form reasoning and mathematical/problem-solving style generation

Quick Start (Transformers)

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "pixas/DECS_1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Solve: If x^2 - 5x + 6 = 0, what are x values?"}
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.6,
        top_p=0.95,
    )

new_tokens = outputs[0][inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Quick Start (vLLM)

python

from vllm import LLM, SamplingParams

llm = LLM(model="pixas/DECS_1.5B", trust_remote_code=True)
sampling = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=512)
prompt = "Please reason step by step: what is 37 * 48?"
outputs = llm.generate([prompt], sampling_params=sampling)
print(outputs[0].outputs[0].text)

Notes

This model may produce incorrect or unverifiable reasoning. Always validate outputs in high-stakes settings.
Performance can vary by prompt style and decoding parameters.
License and acceptable-use constraints should follow the upstream base model and your deployment policy.

Citation

If you use this model, please cite our paper:

bibtex

@inproceedings{jiang2026overthinking,
title={Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling},
author={Shuyang Jiang and Yusheng Liao and Ya Zhang and Yanfeng Wang and Yu Wang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=kdeiRledV6}
}

Flexan/pixas-DECS_1.5B-GGUF

GGUF Files for DECS_1.5B

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