Update README.md

README.md

@@ -5,11 +5,11 @@ language:
 pipeline_tag: text-generation
 tags:
 - deepscaler
-- reasoning
 - grpo
 - qwen2
 base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
 license: other
+library_name: transformers
 ---
 
 # DECS_7B
@@ -22,6 +22,8 @@ DECS_7B is a reasoning-focused causal language model built from `deepseek-ai/Dee
 - Base model: `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`
 - Upload date: `2026-02-24`
 - Recommended use: long-form reasoning and mathematical/problem-solving style generation
+- Paper link: https://arxiv.org/pdf/2509.25827
+- Project page: https://pixas.github.io/decs-iclr26-site/
 
 ## Quick Start (Transformers)
 
@@ -76,83 +78,6 @@ print(outputs[0].outputs[0].text)
 - License and acceptable-use constraints should follow the upstream base model and your deployment policy.
 
 
-## Citation
----
-language:
-- zh
-- en
-pipeline_tag: text-generation
-tags:
-- deepscaler
-- reasoning
-- grpo
-- qwen2
-base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
-license: other
----
-
-# 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
 
@@ -165,4 +90,4 @@ booktitle={The Fourteenth International Conference on Learning Representations},
 year={2026},
 url={https://openreview.net/forum?id=kdeiRledV6}
 }
-```
+```