Update README.md

README.md

@@ -1,199 +1,113 @@
 ---
-library_name: transformers
-tags: []
+language: ko
+pipeline_tag: text-generation
+license: llama3.1
 ---
 
-# Model Card for Model ID
+### 1. Model Description
+- KONI (KISTI Open Natural Intelligence) is a specialized large language model (LLM) developed by the Korea Institute of Science and Technology Information (KISTI). This model is specifically designed for science and technology, making it highly effective for tasks in these fields.
 
-<!-- Provide a quick summary of what the model is/does. -->
+### 2. Key Features
+- **Specialized in Science and Technology:** The model is explicitly trained on a vast and specialized corpus of scientific and technological data.
+- **Enhanced Reasoning Performance:** This version of KONI demonstrates significantly enhanced reasoning performance compared to its instruction-tuned version released in October 2024.
+- **Base Model:** The base model for KONI-Llama3.1-8B-R-Preview-20250320 is KONI-Llama3.1-8B-Merged-20240830, which is a merger of Meta-Llama-3-8B and KONI-Llama3.1-8B-20240824.
+- **Alignment:** SFT (Supervised Fine-Tuning) is applied.
 
+### 3. Data
+- Approximately 5k SFT data for reasoning.
+- **SFT Data:** The SFT data includes both internally generated data and publicly available CoT data on Hugging Face, translated into Korean where necessary.
 
+### 4. Benchmark Results
+The model performance will be released later.
 
-## Model Details
+### 5. How to use the model
+```python
+import transformers
+import torch
 
-### Model Description
+model_id = "KISTI-KONI/KONI-Llama3.1-8B-R-Preview-20250320"
 
-<!-- Provide a longer summary of what this model is. -->
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+pipeline.model.eval()
 
-- **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]
+instruction = "안녕? 너는 누구야?"
 
-### Model Sources [optional]
+messages = [
+   {"role": "user", "content": f"{instruction}"}
+    ]
 
-<!-- Provide the basic links for the model. -->
+prompt = pipeline.tokenizer.apply_chat_template(
+        messages, 
+        tokenize=False, 
+        add_generation_prompt=True
+)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+terminators = [
+    pipeline.tokenizer.eos_token_id,
+    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
 
-## Uses
+outputs = pipeline(
+    prompt,
+    max_new_tokens=2048,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+print(outputs[0]["generated_text"][len(prompt):])
+```
+```
+<think>
+Alright, let's tackle this question. The user is asking, "Hello? Who are you?" in a friendly and informal manner. They probably want a response that's equally casual and welcoming.
 
-### Direct Use
+First, I should make sure to address them directly with a warm greeting. Maybe start with "Hey there!" or "Hi!" to set a friendly tone. Then, since they're asking who I am, I need to introduce myself. Keep it brief and light. Maybe mention that I'm an AI assistant trained based on LlaMA3.1 and created by KISTI to be helpful and honest.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+I should also note that I'm here to engage in substantive conversations and provide analysis on complex subjects. That shows I can handle deeper topics if needed. It's important to be honest and not pretend to be something I'm not, so mentioning that I don't know things if I don't know them is good.
 
-[More Information Needed]
+Finally, end with a friendly sign-off like "Take care!" or "Talk to you later!" to keep the tone upbeat. Make sure the whole response is easy to read and flows naturally. Avoid jargon so it's accessible. Let me put this all together in a way that feels approachable and honest.
+</think>
+<solution>
+안녕하세요! 저는 KISTI에서 개발한 대화형 AI 어시스턴트 LlaMA3.1 기반의 KONI입니다. 저는 복잡한 주제에 대해 심도 있는 대화를 나누고 분석을 제공하도록 훈련받았습니다. 저는 도움을 주기 위해 여기 있습니다! 알려주세요~ 저는 무엇을 도와드릴 수 있을까요? 제가 모르는 것도 있으니, 제가 잘 모르는 것은 정직하게 알려드리겠습니다. 😊
 
-### 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:**
-
-[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]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+---
+*더 깊이 있는 대화나 특정 주제에 대해 논의하고 싶으시면 언제든지 말씀해 주세요!*
 
-[More Information Needed]
+---
+*추신: 질문을 더 잘 이해하도록 도와주시고 싶으시면, 더 많은 정보가 필요하시면 말씀해 주세요! 😊*
+```
+
+### 6. Citation
+**Language Model**
+```text
+@article{KISTI-KONI/KONI-Llama3.1-8B-R-Preview-20250320,
+  title={KISTI-KONI/KONI-Llama3.1-8B-R-Preview-20250320},
+  author={KISTI},
+  year={2025},
+  url={https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-R-Preview-20250320}
+}
+```
+  
+### 7. Contributors
+- KISTI, Large-scale AI Research Center
+
+### 8. Special Thanks
+- [@GUIJIN SON](https://huggingface.co/amphora)
+- [@beomi](https://huggingface.co/beomi)
+- [@kuotient](https://huggingface.co/kuotient)
+- KyungTae Lim
+
+### 8. Acknowledgement
+- This research was supported by Korea Institute of Science and Technology Information(KISTI).
+- This work was supported by the National Supercomputing Center with supercomputing resources including technical support (KISTI).
+
+### 9. References
+- https://huggingface.co/meta-llama/Meta-Llama-3.1-8B
+- https://huggingface.co/meta-llama/meta-llama/Meta-Llama-3.1-8B-Instruct