Instructions to use ToPo-ToPo/my-lora-model-based-line-3.6b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ToPo-ToPo/my-lora-model-based-line-3.6b-sft with Transformers:

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

pipe = pipeline("text-generation", model="ToPo-ToPo/my-lora-model-based-line-3.6b-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ToPo-ToPo/my-lora-model-based-line-3.6b-sft")
model = AutoModelForCausalLM.from_pretrained("ToPo-ToPo/my-lora-model-based-line-3.6b-sft")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ToPo-ToPo/my-lora-model-based-line-3.6b-sft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ToPo-ToPo/my-lora-model-based-line-3.6b-sft

SGLang

How to use ToPo-ToPo/my-lora-model-based-line-3.6b-sft 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 "ToPo-ToPo/my-lora-model-based-line-3.6b-sft" \
    --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": "ToPo-ToPo/my-lora-model-based-line-3.6b-sft",
		"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 "ToPo-ToPo/my-lora-model-based-line-3.6b-sft" \
        --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": "ToPo-ToPo/my-lora-model-based-line-3.6b-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ToPo-ToPo/my-lora-model-based-line-3.6b-sft with Docker Model Runner:
```
docker model run hf.co/ToPo-ToPo/my-lora-model-based-line-3.6b-sft
```

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Check out the documentation for more information.

モデルの概要

以下のモデルとデータセットを用いてLoRAを行った。ベースモデルは、文章の続きを推論するモデルであったため、チャットbotなどの適用ができるように、質問に対して回答を生成するように学習した。モデル、データセットの組み合わせは、LINE社公式のline-corporation/japanese-large-lm-3.6b-instruction-sftと同じである。

base model: line-corporation/japanese-large-lm-3.6b datasets : kunishou/oasst1-89k-ja

学習の経緯

LINE社公式のline-corporation/japanese-large-lm-3.6b-instruction-sftをさらに別のデータセットでLoRAすると、eos tokenが消えてしまい、使用できなかったため、1からモデルを作成した。

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