Update README.md

README.md

@@ -12,113 +12,16 @@ tags:
 - llama-2
 - llama-2-chat
 license: apache-2.0
-library_name: peft
 ---
-# komt-Llama-2-7b-chat-hf-ggml
-
-https://github.com/davidkim205/komt
-
-This model quantized the [korean Llama 2 7B-chat](https://huggingface.co/davidkim205/komt-Llama-2-7b-chat-hf) using [llama.cpp](https://github.com/ggerganov/llama.cpp) to 4-bit quantization.
-
-
-Our model, being in the same format as TheBloke's ggml, supports the following libraries or UI.
-
-
-The following content references [TheBloke/Llama-2-13B-chat-GGML](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML#metas-llama-2-13b-chat-ggml).
-
-GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
-* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling.
-* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend.
-* [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel.
-* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend.
-* [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server.
-* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server.
+# 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
-
-## Prompt Template
-```
-### instruction: {prompt}
-
-### Response: 
-```
-Examples:
-```
-### instruction: 자동차 종합(정기)검사 의무기간은 얼마인가요?
-
-### Response:
-
-```
-response:
-``` 
-### instruction: 자동차 종합(정기)검사 의무기간은 얼마인가요?
-
-### Response:자동차 종합(정기)검사는 2년
-1991년 7월 1일에 고시된 '자동차 보험료 조정기준'에서 취리로부터 제정된 기준 상 경량 살수차를 제외한 자동차 모든 승용자동차는 2년마다 필요하다. 이 법은 차량에 관계없이 2년마다 정기검사를 해야한다고 규제했다.
-```
-
-
-## Usage
-
-When using the original [llama.cpp](https://github.com/ggerganov/llama.cpp) 
-``` 
-make -j && ./main -m ./models/komt-Llama-2-7b-chat-hf-ggml/ggml-model-q4_0.bin -p "### instruction: 누전차단기가 내려가는 이유는 무엇입 니까?\n\n### Response:"
-
-```
-When using the modified llama.cpp for korean multi-task (recommended):
-Refer https://github.com/davidkim205/komt/tree/main/llama.cpp
-``` 
-make -j && ./main -m ./models/komt-Llama-2-7b-chat-hf-ggml/ggml-model-q4_0.bin -p "누전차단기가 내려가는 이유는 무엇입 니까?"
-```
-response:
-``` 
-$ make -j && ./main -m ./models/komt-Llama-2-7b-chat-hf-ggml/ggml-model-q4_0.bin -p "누전차단기가 내려가는 이유는 무엇입 니까?" 
-I llama.cpp build info: 
-I UNAME_S:  Linux
-I UNAME_P:  x86_64
-I UNAME_M:  x86_64
-I CFLAGS:   -I.              -O3 -std=c11   -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -march=native -mtune=native -DGGML_USE_K_QUANTS
-I CXXFLAGS: -I. -I./examples -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -march=native -mtune=native -DGGML_USE_K_QUANTS
-I LDFLAGS:  
-I CC:       cc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
-I CXX:      g++ (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
-
-make: Nothing to be done for 'default'.
-main: build = 987 (3ebb009)
-main: seed  = 1692168046
-llama.cpp: loading model from ./models/komt-Llama-2-7b-chat-hf-ggml/ggml-model-q4_0.bin
-llama_model_load_internal: format     = ggjt v3 (latest)
-llama_model_load_internal: n_vocab    = 32000
-llama_model_load_internal: n_ctx      = 512
-llama_model_load_internal: n_embd     = 4096
-llama_model_load_internal: n_mult     = 5504
-llama_model_load_internal: n_head     = 32
-llama_model_load_internal: n_head_kv  = 32
-llama_model_load_internal: n_layer    = 32
-llama_model_load_internal: n_rot      = 128
-llama_model_load_internal: n_gqa      = 1
-llama_model_load_internal: rnorm_eps  = 5.0e-06
-llama_model_load_internal: n_ff       = 11008
-llama_model_load_internal: freq_base  = 10000.0
-llama_model_load_internal: freq_scale = 1
-llama_model_load_internal: ftype      = 2 (mostly Q4_0)
-llama_model_load_internal: model size = 7B
-llama_model_load_internal: ggml ctx size =    0.08 MB
-llama_model_load_internal: mem required  = 3647.96 MB (+  256.00 MB per state)
-llama_new_context_with_model: kv self size  =  256.00 MB
-llama_new_context_with_model: compute buffer total size =   71.84 MB
-
-system_info: n_threads = 8 / 16 | AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 | 
-sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
-generate: n_ctx = 512, n_batch = 512, n_predict = -1, n_keep = 0
-
-
- ### instruction: 누전차단기가 내려가는 이유는 무엇입니까?
-
-### Response:누전차단기가 내려가는 이유는 다음과 같습니다:1. 고장이나 오작동 확인: 누전차단기가 몇 차례 들어오면 고장이 나거나 오작동을 방지하는 데 도움이 됩니다.2. 누전 사고 피해: 많은 누전차단기가 내려가면 지역에서 일어나는 누전 사고의 영향을 줄이는 것으로 나타났습니다.3. 안정성: 누전차단기가 내려가면 전반적인 안정성이 향상됩니다.
-```
+* **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