Update README.md

README.md

@@ -1,220 +1,155 @@
 ---
 license: apache-2.0
-library_name: transformers
+base_model: Upstage/SOLAR-10.7B-Instruct-v1.0
+library_name: peft
 pipeline_tag: text-generation
 tags:
 - chat
 - instruction-tuned
 - korean
----
-
-
----
-base_model: Upstage/SOLAR-10.7B-Instruct-v1.0
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:Upstage/SOLAR-10.7B-Instruct-v1.0
 - lora
 - sft
 - transformers
 - trl
+language:
+- ko
+- en
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# solar-chatbot-final (LoRA Adapter)
 
+**Summary:** A Korean conversational **LoRA adapter** fine-tuned on Upstage's `SOLAR-10.7B-Instruct-v1.0`  
+Designed for natural, responsive dialogue with capabilities in general Q&A, summarization, and text generation.
 
+---
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
 - **Developed by:** Jihee Cho
-- **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 type:** Causal Language Model (chat/instruction-tuned), **LoRA adapter** for SOLAR-10.7B
+- **Languages:** Korean (primary), English (basic support)
+- **License:** Apache-2.0
+- **Finetuned from:** [Upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/Upstage/SOLAR-10.7B-Instruct-v1.0)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Base Model:** https://huggingface.co/Upstage/SOLAR-10.7B-Instruct-v1.0
+- **Repository:** https://huggingface.co/Jay1121/solar-chatbot-final
 
-- **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]
+## Quick Start
+
+### Requirements
+```bash
+pip install transformers peft torch
+```
+
+### Usage Example
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model and adapter
+base_model_name = "Upstage/SOLAR-10.7B-Instruct-v1.0"
+adapter_name = "Jay1121/solar-chatbot-final"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_name, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_name, 
+    torch_dtype="auto", 
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(model, adapter_name)
+
+# Generate response
+prompt = "안녕! 오늘 뭐 하지?"  # "Hi! What should I do today?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs, 
+    max_new_tokens=256, 
+    do_sample=True, 
+    temperature=0.8,
+    pad_token_id=tokenizer.eos_token_id
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
 
-### Downstream Use [optional]
+---
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## Intended Use
 
-[More Information Needed]
+### Primary Use Cases
+- Casual Korean conversation and chatbot demos
+- General question-answering systems
+- Text summarization and draft writing
+- Prototyping and educational purposes
 
 ### Out-of-Scope Use
+- Professional advice in medical, legal, or financial domains
+- Fact-checking or authoritative information provision
+- Production-level commercial services
+- Processing personal or sensitive data
 
-<!-- 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
+## Limitations and Risks
 
-[More Information Needed]
+### Known Limitations
+- **No factual accuracy guarantee:** The model cannot ensure the correctness of generated information
+- **Hallucination:** May generate false or non-existent content
+- **Bias:** Training data biases may be reflected in outputs
+- **Consistency issues:** May struggle to maintain consistency in long conversations
 
-## Citation [optional]
+### Ethical Considerations
+- Users should critically review all generated content
+- Always consult reliable sources for important decisions
+- Do not blindly trust model outputs; verification is essential
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+---
 
-**BibTeX:**
+## Technical Details
 
-[More Information Needed]
+### Training Details
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Target Modules:** Query, Key, Value projection layers
+- **Rank:** 16
+- **Alpha:** 32
+- **Training Data:** Korean conversation datasets
+- **Training Framework:** Transformers + PEFT + TRL
 
-**APA:**
+### Performance
+This model is optimized for natural conversational experiences rather than benchmark performance, focusing on engaging and contextually appropriate Korean dialogue.
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+## Disclaimer and Usage Terms
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### No Impersonation Policy
+This project is not affiliated with or endorsed by any person or organization. Do not use this model to impersonate specific individuals or create systems that could mislead others about official endorsements.
 
-[More Information Needed]
+### Branding and Attribution
+When using this model, please provide appropriate attribution and avoid using it in ways that could be mistaken for official statements from the original author or related organizations.
 
-## More Information [optional]
+---
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model in your research or projects, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{solar-chatbot-final,
+  author = {Jihee Cho},
+  title = {solar-chatbot-final: Korean Conversational LoRA Adapter for SOLAR-10.7B},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/Jay1121/solar-chatbot-final}}
+}
+```
 
-## Model Card Contact
+---
 
-[More Information Needed]
-### Framework versions
+## Contact
 
-- PEFT 0.17.0
+For questions or feedback, please use the Discussion tab on the Hugging Face model page.