Update README.md

README.md

@@ -2,108 +2,198 @@
 language:
 - ko
 - en
-license: mit
+license: llama3
 library_name: transformers
 datasets:
 - MarkrAI/KoCommercial-Dataset
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Waktaverse-Llama-3-KO-8B-Instruct Model Card
 
 ## Model Details
 
-### Model Description
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/65d6e0640ff5bc0c9b69ddab/Va78DaYtPJU6xr4F6Ca4M.webp)
+Waktaverse-Llama-3-KO-8B-Instruct is a state-of-the-art Korean language model developed by Waktaverse AI team.
+This large language model is a specialized version of the Meta-Llama-3-8B-Instruct, tailored for Korean natural language processing tasks. 
+It is designed to handle a variety of complex instructions and generate coherent, contextually appropriate responses.
 
-<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** Waktaverse AI
+- **Model type:** Large Language Model
+- **Language(s) (NLP):** Korean, English
+- **License:** [Llama3](https://llama.meta.com/llama3/license)
+- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Model Sources
 
-- **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]
+- **Repository:** [GitHub](https://github.com/PathFinderKR/Waktaverse-LLM/tree/main)
+- **Paper :** [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]
+The model can be utilized directly for tasks such as text completion, summarization, and question answering without any fine-tuning. 
 
 ### 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 not intended for use in scenarios that involve high-stakes decision-making including medical, legal, or safety-critical areas due to the potential risks of relying on automated decision-making. 
+Moreover, any attempt to deploy the model in a manner that infringes upon privacy rights or facilitates biased decision-making is strongly discouraged.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+While Waktaverse Llama 3 is a robust model, it shares common limitations associated with machine learning models including potential biases in training data, vulnerability to adversarial attacks, and unpredictable behavior under edge cases. 
+There is also a risk of cultural and contextual misunderstanding, particularly when the model is applied to languages and contexts it was not specifically trained on.
 
-<!-- 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.
+You can run conversational inference using the Transformers Auto classes.
+We highly recommend that you add Korean system prompt for better output.
+Adjust the hyperparameters as you need.
+
+### Example Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = (
+    "cuda:0" if torch.cuda.is_available() else # Nvidia GPU
+    "mps" if torch.backends.mps.is_available() else # Apple Silicon GPU
+    "cpu"
+)
+
+model_id = "PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device=device,
+)
+
+################################################################################
+# Generation parameters
+################################################################################
+num_return_sequences=1
+max_new_tokens=1024
+temperature=0.9
+top_k=40
+top_p=0.9
+repetition_penalty=1.1
+
+def generate_response(system ,user):
+    messages = [
+        {"role": "system", "content": system},
+        {"role": "user", "content": user}
+    ]
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=False
+    )
+    
+    input_ids = tokenizer.encode(
+        prompt,
+        add_special_tokens=True,
+        return_tensors="pt"
+    ).to(device)
+    
+    outputs = model.generate(
+        input_ids=input_ids,
+        pad_token_id=tokenizer.eos_token_id,
+        num_return_sequences=num_return_sequences,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=temperature,
+        top_k=top_k,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty
+    )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=False)
+
+system_prompt = "다음 지시사항에 대한 응답을 작성해주세요."
+user_prompt = "피보나치 수열에 대해 설명해주세요."
+response = generate_response(system_prompt, user_prompt)
+print(response)
+```
+
+### Example Output
+
+```python
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+다음 지시사항에 대한 응답을 작성해주세요.<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+피보나치 수열에 대해 설명해주세요.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+피보나치 수열은 수학에서 가장 유명한 수열 중 하나로, 0과 1로 시작하는 숫자들의 모임입니다. 각 숫자는 이전 두 개의 숫자의 합으로 정의되며, 이렇게 계속 반복됩니다. 피보나치 수열은 무한히 커지는데, 첫 번째와 두 번째 항이 모두 0일 수도 있지만 일반적으로는 첫 번째 항이 1이고 두 번째 항이 1입니다.
+
+예를 들어, 0 + 1 = 1, 1 + 1 = 2, 2 + 1 = 3, 3 + 2 = 5, 5 + 3 = 8, 8 + 5 = 13, 13 + 8 = 21, 21 + 13 = 34 등이 있습니다. 이 숫자들을 피보나치 수열이라고 합니다.
+
+피보나치 수열은 다른 수열들과 함께 사용될 때 도움이 됩니다. 예를 들어, 금융 시장에서는 금리 수익률을 나타내기 위해 이 수열이 사용됩니다. 또한 컴퓨터 과학과 컴퓨터 과학에서도 종종 찾을 수 있습니다. 피보나치 수열은 매우 복잡하며 많은 숫자가 나오므로 일반적인 수열처럼 쉽게 구할 수 없습니다. 이 때문에 피보나치 수열은 대수적 함수와 관련이 있으며 수학자들은 이를 연구하고 계산하기 위해 다양한 알고리즘을 개발했습니다.
+
+참고 자료: https://en.wikipedia.org/wiki/Fibonacci_sequence#Properties.<|eot_id|>
+```
+
 
-[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]
+The model is trained on the [MarkrAI/KoCommercial-Dataset](https://huggingface.co/datasets/MarkrAI/KoCommercial-Dataset), which consists of various commercial texts in Korean.
 
 ### 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]
-
+The model training used LoRA for computational efficiency. 0.02 billion parameters(0.26% of total parameters) were trained.
 
 #### 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 -->
+```python
+################################################################################
+# bitsandbytes parameters
+################################################################################
+load_in_4bit=True
+bnb_4bit_compute_dtype=torch_dtype
+bnb_4bit_quant_type="nf4"
+bnb_4bit_use_double_quant=False
+
+################################################################################
+# LoRA parameters
+################################################################################
+task_type="CAUSAL_LM"
+target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
+r=8
+lora_alpha=16
+lora_dropout=0.05
+bias="none"
+
+################################################################################
+# TrainingArguments parameters
+################################################################################
+num_train_epochs=1
+per_device_train_batch_size=1
+per_device_eval_batch_size=2
+gradient_accumulation_steps=4
+gradient_checkpointing=True
+learning_rate=2e-5
+lr_scheduler_type="cosine"
+warmup_ratio=0.1
+weight_decay=0.1
+
+################################################################################
+# SFT parameters
+################################################################################
+max_seq_length=1024
+packing=True
+```
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
@@ -137,68 +227,54 @@ Use the code below to get started with the model.
 
 
 
-## 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]
+## Technical Specifications
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **GPU:** NVIDIA GeForce RTX 4080 SUPER
 
 #### Software
 
-[More Information Needed]
+- **Operating System:** Linux
+- **Deep Learning Framework:** Hugging Face Transformers, PyTorch
 
-## Citation [optional]
+### Training Details
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+- **Training time:** 32 hours
+- **VRAM usage:** 12.8 GB
+- **GPU power usage:** 300 W
 
-**BibTeX:**
 
-[More Information Needed]
 
-**APA:**
+## Citation
 
-[More Information Needed]
+**Waktaverse-Llama-3**
 
-## Glossary [optional]
+```
+TBD
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+**Llama-3**
 
-[More Information Needed]
+```
+@article{llama3modelcard,
+  title={Llama 3 Model Card},
+  author={AI@Meta},
+  year={2024},
+  url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
+}
+```
 
-## More Information [optional]
 
-[More Information Needed]
 
 ## Model Card Authors [optional]
 
 [More Information Needed]
 
+
+
 ## Model Card Contact
 
 [More Information Needed]