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, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Jumtra commited on Jun 27, 2023

Commit

93070a2

1 Parent(s): 4991616

Create README.md

Browse files

Files changed (1) hide show

README.md +48 -0

README.md ADDED Viewed

	@@ -0,0 +1,48 @@

+---
+license: mit
+tags:
+- ja
+- gpt_neox
+- text-generation
+- lm
+- nlp
+datasets:
+- kunishou/databricks-dolly-15k-ja
+- kunishou/hh-rlhf-49k-ja
+- Jumtra/oasst1_ja
+- Jumtra/jglue_jnli
+- Jumtra/jglue_jsquad
+- Jumtra/jglue_jsquads_with_input
+inference: false
+language:
+- ja
+---
+# 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](https://huggingface.co/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](https://huggingface.co/datasets/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](https://huggingface.co/Jumtra/rinna-3.6b-tune-ep5)|  40/100  | 8.105 |
+|  [Jumtra/rinna-v1-tune-ep1](https://huggingface.co/Jumtra/rinna-v1-tune-ep1) |  42/100  | 7.458 |
+|  [Jumtra/rinna-v1-tune-ep3](https://huggingface.co/Jumtra/rinna-v1-tune-ep3) |  41/100  | 7.034 |
+|  [Jumtra/calm-7b-tune-ep4](https://huggingface.co/Jumtra/calm-7b-tune-ep4) |  40/100  | 9.766 |
+|  [Jumtra/calm-v3-ep1](https://huggingface.co/Jumtra/calm-v3-ep1) |  35/100  | 9.305 |
+|  [Jumtra/calm-v3-ep3](https://huggingface.co/Jumtra/calm-v3-ep3) |  37/100  | 13.276 |