Instructions to use beomi/Mistral-Ko-Inst-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beomi/Mistral-Ko-Inst-dev with Transformers:

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

pipe = pipeline("text-generation", model="beomi/Mistral-Ko-Inst-dev")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beomi/Mistral-Ko-Inst-dev")
model = AutoModelForCausalLM.from_pretrained("beomi/Mistral-Ko-Inst-dev", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beomi/Mistral-Ko-Inst-dev with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beomi/Mistral-Ko-Inst-dev"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beomi/Mistral-Ko-Inst-dev",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beomi/Mistral-Ko-Inst-dev

SGLang

How to use beomi/Mistral-Ko-Inst-dev 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 "beomi/Mistral-Ko-Inst-dev" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beomi/Mistral-Ko-Inst-dev",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "beomi/Mistral-Ko-Inst-dev" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beomi/Mistral-Ko-Inst-dev",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beomi/Mistral-Ko-Inst-dev with Docker Model Runner:
```
docker model run hf.co/beomi/Mistral-Ko-Inst-dev
```

beomi commited on Mar 19, 2024

Commit

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Update README.md

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@@ -13,6 +13,16 @@ tags:
 Experimental Repository :)
 Here's some test:
 ```python

 Experimental Repository :)
+Contents will updated without any notice at all. If you plan to use this repository, please use with `revision` with git hash.
+This experiment is aimed to:
+- Maintain NLU capability of Mistral-Instruct model(mistralai/Mistral-7B-Instruct-v0.1)
+- Adapt new Korean vocab seamlessly
+- Use minimal dataset (used Korean wikipedia only)
+- Computationally efficient method
+- Let model answer using English knowledge and NLU capability even the question/answer is Korean only.
 Here's some test:
 ```python