Instructions to use inarikami/japanese-opt-2.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inarikami/japanese-opt-2.7b with Transformers:

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

pipe = pipeline("text-generation", model="inarikami/japanese-opt-2.7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inarikami/japanese-opt-2.7b")
model = AutoModelForCausalLM.from_pretrained("inarikami/japanese-opt-2.7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inarikami/japanese-opt-2.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inarikami/japanese-opt-2.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inarikami/japanese-opt-2.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/inarikami/japanese-opt-2.7b

SGLang

How to use inarikami/japanese-opt-2.7b 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 "inarikami/japanese-opt-2.7b" \
    --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": "inarikami/japanese-opt-2.7b",
		"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 "inarikami/japanese-opt-2.7b" \
        --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": "inarikami/japanese-opt-2.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use inarikami/japanese-opt-2.7b with Docker Model Runner:
```
docker model run hf.co/inarikami/japanese-opt-2.7b
```

Japanese-opt-2.7b Model

Disclaimer: This model is a work in progress!

This model is a fine-tuned version of facebook/opt-2.7b on the japanese wikipedia dataset.

Quick start

from transformers import pipeline

generator = pipeline('text-generation', model="tensorcat/japanese-opt-2.7b" , device=0, use_fast=False)
generator("今日は", min_length=80, max_length=200, 
    do_sample=True, early_stopping=True, temperature=.98, top_k=50, top_p=1.0)

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 1
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 4
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Framework versions

Pytorch 1.13.0+cu116

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