Flexan/pixas-DECS_1.5B-GGUF

GGUF Files for DECS_1.5B

These are the GGUF files for pixas/DECS_1.5B.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

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)

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)

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:

@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}
}