Update README.md

README.md

@@ -1,199 +1,132 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-8B-Base
+language:
+- ko
+- en
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# KULLM-R
 
+Introducing KULLM-R, a large language model specialized for high-level reasoning queries in Korean, with a particular focus on complex mathematical problems. The model is designed to provide both the correct reasoning paths and answers for such queries, offering enhanced reasoning efficiency and language transferability to Korean compared to general-purpose reasoning models. It leverages reinforcement learning strategies for effective path exploration and Korean-specific generation.
 
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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. -->
-
-[More Information Needed]
-
-### 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]
+- **Model Name**: KULLM-R
+- **Developer**: Seungyoon Lee, Minhyuk Kim, Dongjun Kim, Gyuho Shim and Chanjun Park, supported by NLP&AI Lab in Korea University
+- **Languages**: Korean, English
+- **Objective**: Producing efficient and interpretable reasoning paths and answers for high-level Korean reasoning queries
+- **Training Framework**: verl, PyTorch, Transformers
+- **Parameter Size**: 8B
 
-### 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
+### Model Description
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+KULLM-R is distinguished from standard reasoning LLMs based on Qwen3-8B by its focus on reinforcement learning-based reasoning path exploration and its strong proficiency in Korean language use. It is trained to generate efficient reasoning paths for both English and Korean problems and provides well-structured, readable answers in Korean, delivering strong interpretability and an outstanding user experience for Korean speakers.
 
-[More Information Needed]
+### Key Features
 
-### Recommendations
+- **Reasoning Efficiency Aware Reinforcement Learning**: Introduces RL techniques considering both reasoning path efficiency and answer correctness, reducing unnecessary steps while maintaining answer quality.
+- **Reasoning Path Pruning**: Specialized for high-difficulty reasoning problems by pruning ineffective paths and emphasizing transparency and readability in generated answers.
+- **Support High Readability in Korean System**: Enhanced both logical reasoning and natural Korean expression ability in answer.
+- **Adaptive Length Penalty**: Adaptive penalties optimize the reasoning process according to the question’s complexity and reasoning path length, ensuring efficient solutions for complex problems.
 
-<!-- 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.
+## Data & Training Process
 
-## How to Get Started with the Model
+- **Data Sources**: ko-limo (only 817 rows)
+- **Training Strategy**: Uses reasoning problem difficulty-aware adaptive reward systems, implementing reinforcement learning with dynamic length penalty for optimal performance.
+- **Iteration**: The model repeatedly trains on high-difficulty examples to optimize reasoning path generation.
 
-Use the code below to get started with the model.
 
-[More Information Needed]
+## Quickstart
 
-## Training Details
+The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
 
-### Training Data
+With `transformers<4.51.0`, you will encounter the following error:
+```
+KeyError: 'qwen3'
+```
 
-<!-- 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. -->
+The following contains a code snippet illustrating how to use the model generate content based on given inputs. 
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-### Training Procedure
+model_name = "nobrand/KULLM-R"
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
 
-#### Preprocessing [optional]
+# prepare the model input
+system_prompt = "You are a helpful assistant.\nPlease reason step by step, and put your final answer within \\boxed{{}}" # Recommend to use given system prompt
+user_promt = "1부터 1008까지의 자연수 중 1008과 서로소인 자연수의 갯수를 구하시오."
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_promt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 
-[More Information Needed]
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=16384
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
 
+# parsing thinking content
+try:
+    # rindex finding 151668 (</think>)
+    index = len(output_ids) - output_ids[::-1].index(151668)
+except ValueError:
+    index = 0
 
-#### Training Hyperparameters
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+print("thinking content:", thinking_content)
+print("content:", content)
 
-#### Speeds, Sizes, Times [optional]
+```
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+> [!NOTE]
+> As mentioned in Qwen3, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
 
-[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:**
-
-[More Information Needed]
-
-**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]
+- **KULLM-R vs Qwen3-8B**: Shows superior reasoning efficiency, shorter reasoning steps, higher readability, and better explanation quality compared to models of similar scale when evaluated on Korean reasoning tasks.
 
-## More Information [optional]
 
-[More Information Needed]
+## Intended Use
 
-## Model Card Authors [optional]
+- Solving complex Korean mathematical and logical reasoning problems
+- Improved explainability for Korean logical reasoning
+- Tutoring and educational support in reasoning fields
 
-[More Information Needed]
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
+```
+@misc{KULLM-R2024,
+  title   = {KULLM-R: Korea University Large Language Model for Reasoning},
+  author  = {Korea University NLP Lab},
+  year    = {2024},
+  url     = {https://github.com/kullm-project/KULLM-R}
+}
+```