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komt-Llama-2-13b-hf-ggml

https://github.com/davidkim205/komt

This model quantized the korean Llama 2 13B using 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.

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

  • KoboldCpp, a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling.
  • LoLLMS Web UI, a great web UI with GPU acceleration via the c_transformers backend.
  • LM Studio, a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel.
  • text-generation-webui, the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend.
  • ctransformers, a Python library with LangChain support and OpenAI-compatible AI server.
  • llama-cpp-python, a Python library with OpenAI-compatible API server.

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: ์ž๋™์ฐจ ์ข…ํ•ฉ(์ •๊ธฐ)๊ฒ€์‚ฌ ์˜๋ฌด๊ธฐ๊ฐ„์€ ์–ผ๋งˆ์ธ๊ฐ€์š”?

### Response:

response:

### instruction: ์ž๋™์ฐจ ์ข…ํ•ฉ(์ •๊ธฐ)๊ฒ€์‚ฌ ์˜๋ฌด๊ธฐ๊ฐ„์€ ์–ผ๋งˆ์ธ๊ฐ€์š”?

### Response:์ž๋™์ฐจ ์ข…ํ•ฉ(์ •๊ธฐ)๊ฒ€์‚ฌ๋Š” 2๋…„
1991๋…„ 7์›” 1์ผ์— ๊ณ ์‹œ๋œ '์ž๋™์ฐจ ๋ณดํ—˜๋ฃŒ ์กฐ์ •๊ธฐ์ค€'์—์„œ ์ทจ๋ฆฌ๋กœ๋ถ€ํ„ฐ ์ œ์ •๋œ ๊ธฐ์ค€ ์ƒ ๊ฒฝ๋Ÿ‰ ์‚ด์ˆ˜์ฐจ๋ฅผ ์ œ์™ธํ•œ ์ž๋™์ฐจ ๋ชจ๋“  ์Šน์šฉ์ž๋™์ฐจ๋Š” 2๋…„๋งˆ๋‹ค ํ•„์š”ํ•˜๋‹ค. ์ด ๋ฒ•์€ ์ฐจ๋Ÿ‰์— ๊ด€๊ณ„์—†์ด 2๋…„๋งˆ๋‹ค ์ •๊ธฐ๊ฒ€์‚ฌ๋ฅผ ํ•ด์•ผํ•œ๋‹ค๊ณ  ๊ทœ์ œํ–ˆ๋‹ค.

Usage

When using the original llama.cpp

make -j && ./main -m models/komt-Llama-2-13b-hf-ggml/ggml-model-q8_0.bin -p "### instruction: ์˜ํ™” ํ•ด๋ฆฌํฌํ„ฐ ์‹œ๋ฆฌ์ฆˆ ๋ฐฐ๊ธ‰์‚ฌ๊ฐ€ ์–ด๋””์•ผ\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-2-13b-hf-ggml/ggml-model-q8_0.bin -p "์˜ํ™” ํ•ด๋ฆฌํฌํ„ฐ ์‹œ๋ฆฌ์ฆˆ ๋ฐฐ๊ธ‰์‚ฌ๊ฐ€ ์–ด๋””์•ผ"

response:

 $ make -j && ./main -m models/komt-Llama-2-13b-hf-ggml/ggml-model-q8_0.bin -p "์˜ํ™” ํ•ด๋ฆฌํฌํ„ฐ ์‹œ๋ฆฌ์ฆˆ ๋ฐฐ๊ธ‰์‚ฌ๊ฐ€ ์–ด๋””์•ผ"
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 = 6 (01a61bf)
main: seed  = 1692190774
llama.cpp: loading model from models/komt-Llama-2-13b-hf-ggml/ggml-model-q8_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     = 5120
llama_model_load_internal: n_mult     = 6912
llama_model_load_internal: n_head     = 40
llama_model_load_internal: n_head_kv  = 40
llama_model_load_internal: n_layer    = 40
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       = 13824
llama_model_load_internal: freq_base  = 10000.0
llama_model_load_internal: freq_scale = 1
llama_model_load_internal: ftype      = 7 (mostly Q8_0)
llama_model_load_internal: model size = 13B
llama_model_load_internal: ggml ctx size =    0.11 MB
llama_model_load_internal: mem required  = 13152.13 MB (+  400.00 MB per state)
llama_new_context_with_model: kv self size  =  400.00 MB
llama_new_context_with_model: compute buffer total size =   75.35 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: ์˜ํ™” ํ•ด๋ฆฌํฌํ„ฐ ์‹œ๋ฆฌ์ฆˆ ๋ฐฐ๊ธ‰์‚ฌ๊ฐ€ ์–ด๋””์•ผ

### Response:์›Œ๋„ˆ ๋ธŒ๋ผ๋”์Šค
ํ•ด๋ฆฌํฌํ„ฐ(Harry Potter)๋Š” J. K. ๋กค๋ง์ด ์“ด ํŒํƒ€์ง€ ์†Œ์„ค์ด๋‹ค. 1997๋…„๋ถ€ํ„ฐ 2007๋…„๊นŒ์ง€ ์ด 7๊ถŒ์œผ๋กœ ๋ฐœํ–‰๋˜์—ˆ๊ณ , ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ๋งŽ์€ ์ธ๊ธฐ๋ฅผ ๋Œ์—ˆ๋‹ค. ์˜๊ตญ์—์„œ๋Š” ๋ธ”๋ฃธ๋ฒ„๊ทธ(Bloomsbury), ๋ฏธ๊ตญ์—์„œ๋Š” ์›Œ๋„ˆ ๋ธŒ๋ผ๋”์Šค(Warner Brothers)๊ฐ€ ๊ฐ๊ฐ ์ถœํŒํ•˜์˜€๋‹ค. ํ˜„์žฌ ์ „ ์„ธ๊ณ„์ ์œผ๋กœ 2์–ต 4,000๋งŒ ๋ถ€ ์ด์ƒ์˜ ํŒ๋งค๊ณ ๋ฅผ ์˜ฌ๋ฆฌ๊ณ  ์žˆ์œผ๋ฉฐ, ์ „ ์„ธ๊ณ„ ๋Œ€๋ถ€๋ถ„์˜ ๋ฌธํ•™๊ฐ€๋“ค์—๊ฒŒ ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค. ### check_end_of_text [end of text]

llama_print_timings:        load time =   801.73 ms
llama_print_timings:      sample time =   108.54 ms /   308 runs   (    0.35 ms per token,  2837.66 tokens per second)
llama_print_timings: prompt eval time =  2651.47 ms /    43 tokens (   61.66 ms per token,    16.22 tokens per second)
llama_print_timings:        eval time = 120629.25 ms /   307 runs   (  392.93 ms per token,     2.54 tokens per second)
llama_print_timings:       total time = 123440.86 ms
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