Update README.md

README.md

@@ -1,199 +1,103 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- en
+tags:
+- causal-lm
+- Large Language Model
+- LLM
+- detoxification
+- unbias
+- bias
+- instruction
+- finetuned
+- llama2
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for SungJoo/llama2-7b-sft-detox
 
+This model is an instruction-tuned version of meta-llama/Llama-2-7b-hf, fine-tuned to reduce toxicity in Large Language Models (LLMs).
 
 ## 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.
+This is an instruction-tuned model based on the LLaMA-2-7b architecture. It has been fine-tuned using a comprehensive instruction dataset specifically designed for detoxification of LLMs.
 
-- **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]
+- **Developed by:** Sungjoo Byun (Grace Byun)
+- **Model type:** Auto-regressive language model
+- **Language(s) (NLP):** English
+- **License:** Apache License 2.0
+- **Finetuned from:** meta-llama/Llama-2-7b-hf
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** TBD
+- **Paper:** TBD
 
 ## 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]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is intended to be used for generating less toxic language in various applications, including chatbots and other NLP systems.
 
 ## 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.
+While this model aims to reduce toxicity, it may still generate biased or harmful content. Users should apply this model with caution and review outputs for sensitive applications.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the code below to get started with the model:
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("SungJoo/llama2-7b-sft-detox")
+model = AutoModelForCausalLM.from_pretrained("SungJoo/llama2-7b-sft-detox")
+```
 
 ## 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]
+The model was fine-tuned using a dataset specifically created to detoxify LLMs. This dataset will be publicly available soon.
 
 ### Training Procedure
+The model was trained using full fine-tuning with the following hyperparameters:
+
+| **Hyperparameter** | **Value** |
+|--------------------|-----------|
+| Batch size         | 128       |
+| Learning rate      | 2e-5      |
+| Epochs             | 3         |
+| Max length         | 512       |
+| Weight decay       | 0         |
+
+
+The training was conducted on 4 A100 80GB GPUs.
+
+## Objective
+The main objective of this research is to reduce toxicity in LLMs by applying instruction tuning and Direct Preference Optimization (DPO). 
+This version has been tuned with instruction tuning only.
+A comprehensive instruction and DPO dataset was constructed for this purpose, which will be released in the future.
+
+| **Model**          | **LLaMA-2-base**  |                       | **Finetuned LLaMA-2** |                         | **DPO LLaMA-2**      |                         |
+|--------------------|-------------------|-----------------------|-----------------------|-------------------------|-----------------------|-------------------------|
+| **Category**       | **\>=0.5 (%)**    | **Count**             | **\>=0.5 (%)**        | **Count**               | **\>=0.5 (%)**        | **Count**               |
+| **TOXICITY**       | 4.46              | 4,438                 | 3.61                  | 3,593                   | 2.39                  | 2,377                   |
+|                    |                   |                       | <span style="color:blue;">(-0.85)</span> | <span style="color:blue;">(-845)</span>  | <span style="color:green;">(-1.22)</span> | <span style="color:green;">(-1,216)</span> |
+| **SEVERE_TOXICITY**| 0.08              | 77                    | 0.07                  | 70                      | 0.03                  | 31                      |
+|                    |                   |                       | <span style="color:blue;">(-0.01)</span> | <span style="color:blue;">(-7)</span>    | <span style="color:green;">(-0.04)</span>  | <span style="color:green;">(-39)</span>   |
+| **IDENTITY_ATTACK**| 0.79              | 788                   | 0.42                  | 413                     | 0.28                  | 274                     |
+|                    |                   |                       | <span style="color:blue;">(-0.37)</span> | <span style="color:blue;">(-375)</span>  | <span style="color:green;">(-0.14)</span>  | <span style="color:green;">(-139)</span>  |
+| **INSULT**         | 1.97              | 1,961                 | 1.60                  | 1,588                   | 0.90                  | 892                     |
+|                    |                   |                       | <span style="color:blue;">(-0.37)</span> | <span style="color:blue;">(-373)</span>  | <span style="color:green;">(-0.70)</span>  | <span style="color:green;">(-696)</span>  |
+| **PROFANITY**      | 2.10              | 2,086                 | 1.76                  | 1,753                   | 1.04                  | 1,030                   |
+|                    |                   |                       | <span style="color:blue;">(-0.34)</span> | <span style="color:blue;">(-333)</span>  | <span style="color:green;">(-0.72)</span>  | <span style="color:green;">(-723)</span>  |
+| **THREAT**         | 1.43              | 1,424                 | 0.92                  | 919                     | 0.76                  | 754                     |
+|                    |                   |                       | <span style="color:blue;">(-0.51)</span> | <span style="color:blue;">(-505)</span>  | <span style="color:green;">(-0.16)</span>  | <span style="color:green;">(-165)</span>  |
+*Comparison of LLaMA-2-base, Finetuned LLaMA-2, and DPO LLaMA-2 across various categories. Reductions in blue indicate comparisons between the base model and the fine-tuned model, while text in green represents comparisons between the fine-tuned model and the DPO model.*
+
+The table above shows the effectiveness of this model in reducing bias, measured using the RealToxicityPrompt dataset and the Perspective API.
+
+## Contact
+For any questions or issues, please contact byunsj@snu.ac.kr.
 
-<!-- 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
-
-[More Information Needed]