HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16

Instructions to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16 with Transformers:

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

pipe = pipeline("text-generation", model="mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16", trust_remote_code=True, dtype="auto", device_map="auto")

Mobilint

How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16 with Mobilint:

# pip install mblt-model-zoo
from mblt_model_zoo.vision import MBLT_Engine

model = MBLT_Engine(
    model_cls="HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16",
    model_type="DEFAULT",
    model_path="",
    core_mode="global8",
)

try:
    image = model.preprocess("path/to/image.jpg")
    output = model(image)
    result = model.postprocess(output)
finally:
    model.dispose()

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16

SGLang

How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16 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 "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16" \
    --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": "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16",
		"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 "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16" \
        --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": "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16 with Docker Model Runner:
```
docker model run hf.co/mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16
```

HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16 / README.md

lbs1163

Duplicate from mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B

c5bcafd 3 months ago

765 Bytes

license: other
license_name: hyperclovax-seed
license_link: >-
  https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
  - naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B
base_model_relation: quantized
tags:
  - mobilint

About

This repository provides a model compiled and optimized for Mobilint NPU hardware.
The model is packaged for deployment on Mobilint’s acceleration stack and is intended to be used within that environment.