Instructions to use shleeeee/mistral-ko-7b-tech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shleeeee/mistral-ko-7b-tech with Transformers:

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

pipe = pipeline("text-generation", model="shleeeee/mistral-ko-7b-tech")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-ko-7b-tech")
model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-ko-7b-tech")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shleeeee/mistral-ko-7b-tech with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/shleeeee/mistral-ko-7b-tech

SGLang

How to use shleeeee/mistral-ko-7b-tech 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 "shleeeee/mistral-ko-7b-tech" \
    --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": "shleeeee/mistral-ko-7b-tech",
		"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 "shleeeee/mistral-ko-7b-tech" \
        --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": "shleeeee/mistral-ko-7b-tech",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shleeeee/mistral-ko-7b-tech with Docker Model Runner:
```
docker model run hf.co/shleeeee/mistral-ko-7b-tech
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for mistral-ko-7b-tech

It is a fine-tuned model using Korean in the mistral-7b model.

Model Details

Model Developers : shleeeee(Seunghyeon Lee), oopsung(Sungwoo Park)
Repository : To be added
Model Architecture : The mistral-ko-7b-wiki-neft is is a fine-tuned version of the Mistral-7B-v0.1.
Lora target modules : q_proj, k_proj, v_proj, o_proj,gate_proj
train_batch : 4
Max_step : 500

Dataset

Korean Custom Dataset(2000)

Prompt template: Mistral

<s>[INST]{['instruction']}[/INST]{['output']}</s>

Usage

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shleeeee/mistral-ko-7b-tech")
model = AutoModelForCausalLM.from_pretrained("shleeeee/mistral-ko-7b-tech")

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

pipe = pipeline("text-generation", model="shleeeee/mistral-ko-7b-tech")

Evaluation

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

F16

Model tree for shleeeee/mistral-ko-7b-tech

Quantizations

2 models

shleeeee
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mistral-ko-7b-tech