Update README.md

README.md

@@ -1,199 +1,168 @@
 ---
+language:
+- en
+- ko
+license: llama3
 library_name: transformers
-tags: []
+base_model:
+- meta-llama/Meta-Llama-3-8B
 ---
 
-# Model Card for Model ID
+<a href="https://taemin6697.github.io/">
+  <img src="https://github.com/taemin6697/taemin6697/assets/96530685/46a29020-e640-4e74-9d77-f12e466fc706" width="40%" height="50%">
+</a>
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Hansung Bllossom | [Demo]() | [Developer 김태민](https://taemin6697.github.io/) | [Github](https://github.com/taemin6697/HansungGPT/tree/main) |
 
+```bash
+한성대학교 QA 기반으로 학습시킨Hansung-Llama-3-8B 를 출시합니다.
+이는 beomi/Llama-3-KoEn-8B-Instruct-preview 을 기반으로 학습되었습니다.
+```
+
+The Bllossom language model is a Korean-English bilingual language model based on the open-source LLama3. It enhances the connection of knowledge between Korean and English. It has the following features:
 
+* **Knowledge Linking**: Linking Korean and English knowledge through additional training
+* **Vocabulary Expansion**: Expansion of Korean vocabulary to enhance Korean expressiveness.
+* **Instruction Tuning**: Tuning using custom-made instruction following data specialized for Korean language and Korean culture
+* **Human Feedback**: DPO has been applied
+* **Vision-Language Alignment**: Aligning the vision transformer with this language model 
+
+## Example code
+
+### Install Dependencies
+```bash
+pip install torch transformers==4.40.0 accelerate
+```
 
-## 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]
-
-### 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
-
-[More Information Needed]
+### Python code with Pipeline
+```python
+import transformers
+import torch
+
+model_id = "kfkas/Hansung-Llama-3-8B"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+pipeline.model.eval()
+
+PROMPT = '''당신은 유용한 AI 어시스턴트입니다. 사용자의 질의에 대해 친절하고 정확하게 답변해야 합니다.
+You are a helpful AI assistant, you'll need to answer users' queries in a friendly and accurate manner.'''
+instruction = "한성대학교에서는 어떤 축제나 행사가 열리나요?"
+
+messages = [
+    {"role": "system", "content": f"{PROMPT}"},
+    {"role": "user", "content": f"{instruction}"}
+    ]
+
+prompt = pipeline.tokenizer.apply_chat_template(
+        messages, 
+        tokenize=False, 
+        add_generation_prompt=True
+)
+
+terminators = [
+    pipeline.tokenizer.eos_token_id,
+    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = pipeline(
+    prompt,
+    max_new_tokens=2048,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9
+)
+
+print(outputs[0]["generated_text"][len(prompt):])
+
+```
+
+### Python code with AutoModel
+```python
+
+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = 'kfkas/Hansung-Llama-3-8B'
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+model.eval()
+
+PROMPT = '''당신은 유용한 AI 어시스턴트입니다. 사용자의 질의에 대해 친절하고 정확하게 답변해야 합니다.
+You are a helpful AI assistant, you'll need to answer users' queries in a friendly and accurate manner.'''
+instruction = "한성대학교는 언제 설립되었나요?"
+
+messages = [
+    {"role": "system", "content": f"{PROMPT}"},
+    {"role": "user", "content": f"{instruction}"}
+    ]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=2048,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9
+)
+
+print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
+```
+
+
+
+## Citation
+**Language Model**
+```text
+@misc{bllossom,
+  author = {ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim},
+  title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean},
+  year = {2024},
+  journal = {LREC-COLING 2024},
+  paperLink = {\url{https://arxiv.org/pdf/2403.10882}},
+ },
+}
+```
+
+**Vision-Language Model**
+```text
+@misc{bllossom-V,
+  author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},
+  title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {NAACL 2024 findings},
+  paperLink = {\url{https://arxiv.org/pdf/2403.11399}},
+ },
+}
+```
+
+## Contact
+ - 김태민(Taemin Kim), Intelligent System. `[email protected]`
+
+## Contributor
+ - 김태민(Taemin Kim), Intelligent System. `taemin6697@gmail.com`