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Model: davidkim205/komt-llama2-7b-v1-ggml
Source: Original Platform

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README.md

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+---
+language:
+- en
+- ko
+pipeline_tag: text-generation
+inference: false
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-2
+- llama-2-chat
+license: apache-2.0
+---
+# 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
+* **quant methods** : q4_0, q4_1, q5_0, q5_1, q2_k, q3_k, q3_k_m, q3_k_l, q4_k, q4_k_s, q4_k_m, q5_k, q5_k_s, q5_k_m, q8_0, q4_0
+
+
+## Training
+Refer https://github.com/davidkim205/komt
+
+## 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%** |
+

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