Instructions to use xihajun/gemma-wang-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xihajun/gemma-wang-zh with Transformers:

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

pipe = pipeline("text-generation", model="xihajun/gemma-wang-zh")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("xihajun/gemma-wang-zh")
model = AutoModelForMultimodalLM.from_pretrained("xihajun/gemma-wang-zh", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xihajun/gemma-wang-zh with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/xihajun/gemma-wang-zh

SGLang

How to use xihajun/gemma-wang-zh 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 "xihajun/gemma-wang-zh" \
    --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": "xihajun/gemma-wang-zh",
		"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 "xihajun/gemma-wang-zh" \
        --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": "xihajun/gemma-wang-zh",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xihajun/gemma-wang-zh with Docker Model Runner:
```
docker model run hf.co/xihajun/gemma-wang-zh
```

gemma-wang-zh

Merged Wang Yangming style adapter on top of google/gemma-4-E4B-it.

This repository contains the merged full Transformers model, not only the PEFT adapter. The adapter was trained with Chinese Wang Yangming/persona SFT data at max length 1536, LoRA r=8 alpha=16.

Quick use

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

repo = "xihajun/gemma-wang-zh"
processor = AutoProcessor.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")

messages = [
    {"role": "system", "content": "你将以王阳明的口吻和心学思想回答：强调致良知、知行合一、事上磨炼。不要解释风格，直接回答。"},
    {"role": "user", "content": "如果我想很多但行动很少，应该怎么办？"},
]
prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = processor(text=[prompt], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
print(processor.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Notes

This is a merged Hugging Face/Transformers model. It is not a .litertlm mobile package yet.

Downloads last month: 16

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for xihajun/gemma-wang-zh

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Adapter

(433)

this model