Update README.md

README.md

@@ -1,6 +1,4 @@
 ---
-# library_name: when2think
-arxiv: 2609.19671
 pipeline_tag: text-generation
 library_name: transformers
 datasets:
@@ -20,3 +18,300 @@ tags:
 - reinforcement-learning
 - rlvr
 ---
+
+
+# When2Think
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+
+
+```py
+from transformers import pipeline
+
+model_path = "junshim/When2Think-1.5B"
+
+prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+
+generator = pipeline(
+    "text-generation",
+    model=model_path,
+    device_map="auto",
+    dtype="auto"
+)
+outputs = generator(
+    text,
+    max_new_tokens=512,
+    clean_up_tokenization_spaces=False
+)
+```
+
+```py
+from accelerate import Accelerator
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+accelerator = Accelerator()
+device = accelerator.device
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype="auto",
+    device_map=device
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+print(model.generation_config, device)
+```
+
+```py
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    **inputs,
+    max_new_tokens=512
+)
+output_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+```py
+import re
+
+ASSISTANT_RE = re.compile(
+    r"<｜Assistant｜>(.*?)(?=<｜User｜>|<｜end of sentence｜>|$)",
+    re.DOTALL,
+)
+THINK_RE = re.compile(r"<think>(.*?)</think>", re.DOTALL)
+
+
+def parse_deepseek_r1(text: str) -> list[dict]:
+    dialogue = []
+
+    for match in ASSISTANT_RE.finditer(text):
+        assistant = match.group(1).strip()
+        think = THINK_RE.search(assistant)
+
+        if think:
+            reasoning = think.group(1).strip()
+            content = (
+                assistant[:think.start()] + assistant[think.end():]
+            ).strip()
+        else:
+            reasoning = None
+            content = assistant
+
+        dialogue.append({
+            "reasoning": reasoning,
+            "content": content,
+        })
+
+    return dialogue
+
+
+parse_deepseek_r1(output_text)
+```
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```bibtex
+@misc{shim2026when2thinklearningdifficultyawarelength,
+  title         = {When2Think: Learning Difficulty-Aware Length Control for Efficient Hybrid Reasoning Models},
+  author        = {Jaejun Shim and HyunJin Kim and Young Jin Kim and JinYeong Bak},
+  year          = {2026},
+  eprint        = {2609.19671},
+  url           = {https://arxiv.org/abs/2609.19671}
+}
+```
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]