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README.md

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+---
+language:
+- en
+- ko
+pipeline_tag: text-generation
+tags:
+- finetuned
+
+---
+# komt : korean multi task instruction tuning model
+![multi task instruction tuning.jpg](https://github.com/davidkim205/komt/assets/16680469/c7f6ade7-247e-4b62-a94f-47e19abea68e)
+
+Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities. 
+However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. 
+This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).
+
+## Model Details
+
+* **Model Developers** :  davidkim(changyeon kim)
+* **Repository** : https://github.com/davidkim205/komt
+* **Model Architecture** : The komt-mistral-7b-v1  is is a fine-tuned version of the Mistral-7B-Instruct-v0.1.
+
+## Dataset
+korean multi-task instruction dataset
+
+## Hardware and Software
+- nvidia driver : 535.54.03
+- CUDA Version: 12.2
+
+## Training
+Refer https://github.com/davidkim205/komt
+
+
+## Prompt template: Mistral
+```
+<s>[INST] {prompt} [/INST]</s>
+```
+
+## Usage
+
+
+``` 
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import TextStreamer, GenerationConfig
+
+model_name='davidkim205/komt-mistral-7b-v1'
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+streamer = TextStreamer(tokenizer)
+
+def gen(x):
+    generation_config = GenerationConfig(
+        temperature=0.8,
+        top_p=0.8,
+        top_k=100,
+        max_new_tokens=1024,
+        early_stopping=True,
+        do_sample=True,
+    )
+    q = f"[INST]{x} [/INST]"
+    gened = model.generate(
+        **tokenizer(
+            q,
+            return_tensors='pt',
+            return_token_type_ids=False
+        ).to('cuda'),
+        generation_config=generation_config,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        streamer=streamer,
+    )
+    result_str = tokenizer.decode(gened[0])
+
+    start_tag = f"\n\n### Response: "
+    start_index = result_str.find(start_tag)
+
+    if start_index != -1:
+        result_str = result_str[start_index + len(start_tag):].strip()
+    return result_str
+
+print(gen('제주도를 1박2일로 혼자 여행하려고 하는데 여행 코스를 만들어줘'))
+```
+output
+``` 
+<s> [INST]제주도를 1박2일로 혼자 여행하려고 하는데 여행 코스를 만들어줘 [/INST]1박 2일 여행 코스
+제주 여행 코스는 다음과 같습니다:
+
+첫째 날:
+* 아침: 제주시 신라로 이동하여 한라산 국립공원에 도착합니다. 오른쪽 발자국 광장에서 눈에 띄는 맥주숍을 찾아 한 병의 맥주를 마시고 계단으로 올라가 한라산 정상에 도착합니다.
+* 점심: 정상에서 계단을 내려와 중도에 위치한 골목에서 음식을 즐깁니다. 젓갈, 곰탕, 전복 등 맛있는 제주 특산물을 맛보며 힐링을 즐깁니다.
+* 오후: 중도에서 남하하여 남서쪽 제주로 이동합니다. 남서쪽 제주는 섬의 자연 경관과 백사장, 아름다운 바다 등 아름다운 풍광이 있는 곳입니다. 숙소에 숙박합니다.
+
+둘째 날:
+* 아침: 남서쪽 제주에서 북서쪽 제주로 이동합니다. 이 지역은 푸른 잔디 해변과 투명한 바다가 펼쳐져 있는 아름다운 풍광입니다. 숙소에서 아침을 먹고 바다로 향하여 해변에서 휴식을 취합니다.
+* 점심: 북서쪽 제주의 앞바다에서 시원한 바다를 보며 한 끼의 해산물을 맛보고 계절에 따라 해산물 요리를 즐깁니다.
+* 오후: 남서쪽 제주에서 이동하여 서쪽 제주로 이동합니다. 서쪽 제주는 절벽과 암석 해변, 수수께끼의 돌 정원 등 독특한 경관이 있는 곳입니다. 이곳에서는 서쪽 제주의 대표적인 명소인 청량사를 방문하고 서쪽 제주의 아름다운 풍광을 감상합니다.
+* 저녁: 서쪽 제주에서 제주 시로 이동하여 한 끼의 제주 특산물을 맛보고 도착한 제주 도심에서 저녁을 즐깁니다.
+* 야간: 제주 시의 도심에서 야간 활동을 즐기며 1박 2일의 여행을 마무리합니다.
+
+이렇게 제주를 1박 2일로 혼자 여행하면 제주의 아름다운 풍광, 푸른 잔디 해변, 투명한 바다 등을 경험할 수 있습니다. 
+```
+## Evaluation
+For objective model evaluation, we initially used EleutherAI's lm-evaluation-harness but obtained unsatisfactory results. Consequently, we conducted evaluations using ChatGPT, a widely used model, as described in [Self-Alignment with Instruction Backtranslation](https://arxiv.org/pdf/2308.06502.pdf) and [Three Ways of Using Large Language Models to Evaluate Chat](https://arxiv.org/pdf/2308.06259.pdf) .
+
+
+| model                                   | score   | average(0~5) | percentage |
+| --------------------------------------- |---------| ------------ | ---------- |
+| gpt-3.5-turbo(close)                    | 147     | 3.97         | 79.45%     |
+| naver Cue(close)                        | 140     | 3.78         | 75.67%     |
+| clova X(close)                          | 136     | 3.67         | 73.51%     |
+| WizardLM-13B-V1.2(open)                 | 96      | 2.59         | 51.89%     |
+| Llama-2-7b-chat-hf(open)                | 67      | 1.81         | 36.21%     |
+| Llama-2-13b-chat-hf(open)               | 73      | 1.91         | 38.37%     |
+| nlpai-lab/kullm-polyglot-12.8b-v2(open) | 70      | 1.89         | 37.83%     |
+| kfkas/Llama-2-ko-7b-Chat(open)          | 96      | 2.59         | 51.89%     |
+| beomi/KoAlpaca-Polyglot-12.8B(open)     | 100     | 2.70         | 54.05%     |
+| **komt-llama2-7b-v1 (open)(ours)**      | **117** | **3.16**     | **63.24%** |
+| **komt-llama2-13b-v1  (open)(ours)**    | **129** | **3.48**     | **69.72%** |
+| **komt-llama-30b-v1  (open)(ours)**    | **129** | **3.16**     | **63.24%** |
+| **komt-mistral-7b-v1  (open)(ours)**    | **131** | **3.54**     | **70.81%** |
+

added_tokens.json

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+{
+  "</s>": 2,
+  "<s>": 1,
+  "<unk>": 0
+}

config.json

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+{
+  "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.1",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0",
+  "use_cache": false,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.34.0"
+}

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+size 493443

tokenizer_config.json

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