Instructions to use davidkim205/komt-llama2-7b-v1-ggml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidkim205/komt-llama2-7b-v1-ggml with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="davidkim205/komt-llama2-7b-v1-ggml",
	filename="ggml-model-q2_k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use davidkim205/komt-llama2-7b-v1-ggml with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M

Use Docker

docker model run hf.co/davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M

LM Studio
Jan

vLLM

How to use davidkim205/komt-llama2-7b-v1-ggml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davidkim205/komt-llama2-7b-v1-ggml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidkim205/komt-llama2-7b-v1-ggml",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M

Ollama
How to use davidkim205/komt-llama2-7b-v1-ggml with Ollama:
```
ollama run hf.co/davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M
```

Unsloth Studio new

How to use davidkim205/komt-llama2-7b-v1-ggml with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for davidkim205/komt-llama2-7b-v1-ggml to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for davidkim205/komt-llama2-7b-v1-ggml to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for davidkim205/komt-llama2-7b-v1-ggml to start chatting

Docker Model Runner
How to use davidkim205/komt-llama2-7b-v1-ggml with Docker Model Runner:
```
docker model run hf.co/davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M
```

Lemonade

How to use davidkim205/komt-llama2-7b-v1-ggml with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull davidkim205/komt-llama2-7b-v1-ggml:Q4_K_M

Run and chat with the model

lemonade run user.komt-llama2-7b-v1-ggml-Q4_K_M

List all available models

lemonade list

komt : korean multi task instruction tuning model

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 and Three Ways of Using Large Language Models to Evaluate Chat .

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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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit

Papers for davidkim205/komt-llama2-7b-v1-ggml

Three Ways of Using Large Language Models to Evaluate Chat

Paper • 2308.06502 • Published Aug 12, 2023

Self-Alignment with Instruction Backtranslation

Paper • 2308.06259 • Published Aug 11, 2023 • 43