Instructions to use maywell/Mistral-ko-7B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maywell/Mistral-ko-7B-v0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="maywell/Mistral-ko-7B-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maywell/Mistral-ko-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("maywell/Mistral-ko-7B-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maywell/Mistral-ko-7B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maywell/Mistral-ko-7B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Mistral-ko-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/maywell/Mistral-ko-7B-v0.1

SGLang

How to use maywell/Mistral-ko-7B-v0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "maywell/Mistral-ko-7B-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Mistral-ko-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "maywell/Mistral-ko-7B-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Mistral-ko-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use maywell/Mistral-ko-7B-v0.1 with Docker Model Runner:
```
docker model run hf.co/maywell/Mistral-ko-7B-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

해당 모델은 오래된 실험용입니다. 실 사용을 권장하지 않습니다.

Mistral-ko-7B-v0.1

Model Details

Description

Mistral-ko-7B-v0.1는 미스트랄에 한국어에 최적화 된 토크나이저를 적용한 모델입니다. Raw Data로 어느정도 형성된 모델에 시나트라에 사용 된 데이터셋으로 2 Epoch 훈련되었습니다.

-- Further Description After Evaluation --

Comment

토크나이저는 @beomi님의 라마2 한국어 버전을 기반으로 제작되었습니다.

기반 모델을 제공해주신 @jin05102518님께 감사드립니다.

Follow me on twitter: https://twitter.com/stablefluffy

Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕

Contact me on Telegram: https://t.me/AlzarTakkarsen

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

F32

BF16

Model tree for maywell/Mistral-ko-7B-v0.1

Quantizations

3 models