Instructions to use Frinkles/JapaneseModelV1-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Frinkles/JapaneseModelV1-ONNX with Transformers:

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

pipe = pipeline("text-generation", model="Frinkles/JapaneseModelV1-ONNX")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Frinkles/JapaneseModelV1-ONNX", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Frinkles/JapaneseModelV1-ONNX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Frinkles/JapaneseModelV1-ONNX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Frinkles/JapaneseModelV1-ONNX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Frinkles/JapaneseModelV1-ONNX

SGLang

How to use Frinkles/JapaneseModelV1-ONNX 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 "Frinkles/JapaneseModelV1-ONNX" \
    --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": "Frinkles/JapaneseModelV1-ONNX",
		"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 "Frinkles/JapaneseModelV1-ONNX" \
        --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": "Frinkles/JapaneseModelV1-ONNX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Frinkles/JapaneseModelV1-ONNX with Docker Model Runner:
```
docker model run hf.co/Frinkles/JapaneseModelV1-ONNX
```

Frinkles commited on Jul 17, 2024

Commit

0277281

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1 Parent(s): 6ab90d2

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+---
+pipeline_tag: text-generation
+tags:
+- phi3
+- LLM
+- onnx
+language:
+- ja
+library_name: transformers
+---
+# Phi 3 Model with Extended Vocabulary and Fine-Tuning for Japanese
+## Overview
+This project is a proof of concept that extends the base vocabulary of the Phi 3 model and then applies supervised fine-tuning to teach it a new language (Japanese). Despite using a very small custom dataset, the improvement in Japanese language understanding is substantial.
+## Model Details
+- **Base Model**: Phi 3
+- **Objective**: Extend the base vocabulary and fine-tune for Japanese language understanding.
+- **Dataset**: Custom dataset of 1,000 entries generated using ChatGPT-4.
+- **Language**: Japanese
+## Dataset
+The dataset used for this project was generated with the assistance of ChatGPT-4. It comprises 1,000 entries, carefully curated to cover a diverse range of topics and linguistic structures.
+## Training
+### Vocabulary Extension
+The base vocabulary of the Phi 3 model was extended to include new Japanese tokens. This was a crucial step to enable the model to comprehend and generate Japanese text more effectively.
+### Fine-Tuning
+Supervised fine-tuning was performed on the extended model using the custom dataset. Despite the small dataset size, the model showed significant improvement in understanding and generating Japanese text.
+## Results
+Even with the limited dataset and vocabulary size, the fine-tuned model demonstrated substantial improvements over the base model in terms of Japanese language understanding and generation.
+## Future Work
+1. **Dataset Expansion**: Increase the size and diversity of the dataset to further enhance model performance.
+2. **Evaluation**: Conduct comprehensive evaluation and benchmarking against standard Japanese language tasks.
+3. **Optimization**: Optimize the model for better performance and efficiency.