Instructions to use Jumtra/rinna-3.6b-tune-ep5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jumtra/rinna-3.6b-tune-ep5 with Transformers:

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

pipe = pipeline("text-generation", model="Jumtra/rinna-3.6b-tune-ep5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jumtra/rinna-3.6b-tune-ep5")
model = AutoModelForCausalLM.from_pretrained("Jumtra/rinna-3.6b-tune-ep5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Jumtra/rinna-3.6b-tune-ep5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jumtra/rinna-3.6b-tune-ep5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jumtra/rinna-3.6b-tune-ep5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Jumtra/rinna-3.6b-tune-ep5

SGLang

How to use Jumtra/rinna-3.6b-tune-ep5 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 "Jumtra/rinna-3.6b-tune-ep5" \
    --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": "Jumtra/rinna-3.6b-tune-ep5",
		"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 "Jumtra/rinna-3.6b-tune-ep5" \
        --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": "Jumtra/rinna-3.6b-tune-ep5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Jumtra/rinna-3.6b-tune-ep5 with Docker Model Runner:
```
docker model run hf.co/Jumtra/rinna-3.6b-tune-ep5
```

rinna-3.6b

このモデルは、MosaicMLのllm-foundryリポジトリを使用してrinna/japanese-gpt-neox-3.6bをファインチューニングしたモデルです。

Model Date

June 28, 2023

Model License

MIT

評価

Jumtra/test_data_100QAを用いてモデルの正答率を評価したまた、学習時のvalidateデータに対してのPerplexityを記載した。

model name	正答率	Perplexity
Jumtra/rinna-3.6b-tune-ep5	40/100	8.105
Jumtra/rinna-v1-tune-ep1	42/100	7.458
Jumtra/rinna-v1-tune-ep3	41/100	7.034
Jumtra/calm-7b-tune-ep4	40/100	9.766
Jumtra/calm-v3-ep1	35/100	9.305
Jumtra/calm-v3-ep3	37/100	13.276

以下のプロンプトを用いた

INSTRUCTION_KEY = "### 入力:"
RESPONSE_KEY = "### 回答:"
INTRO_BLURB = "以下はタスクを説明する指示と文脈のある文章が含まれた入力です。要求を適切に満たす回答を生成しなさい。"
JP_PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
    intro=INTRO_BLURB,
    instruction_key=INSTRUCTION_KEY,
    instruction="{instruction}",
    response_key=RESPONSE_KEY,
)

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rinna-3.6b-tune-ep5

rinna-3.6b

Model Date

Model License

評価

Datasets used to train Jumtra/rinna-3.6b-tune-ep5