Instructions to use Simon-Liu/gemma-4-e4b-github-mcp-sft-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Simon-Liu/gemma-4-e4b-github-mcp-sft-it with Transformers:

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

pipe = pipeline("text-generation", model="Simon-Liu/gemma-4-e4b-github-mcp-sft-it")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Simon-Liu/gemma-4-e4b-github-mcp-sft-it")
model = AutoModelForMultimodalLM.from_pretrained("Simon-Liu/gemma-4-e4b-github-mcp-sft-it")
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Simon-Liu/gemma-4-e4b-github-mcp-sft-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Simon-Liu/gemma-4-e4b-github-mcp-sft-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Simon-Liu/gemma-4-e4b-github-mcp-sft-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Simon-Liu/gemma-4-e4b-github-mcp-sft-it

SGLang

How to use Simon-Liu/gemma-4-e4b-github-mcp-sft-it 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 "Simon-Liu/gemma-4-e4b-github-mcp-sft-it" \
    --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": "Simon-Liu/gemma-4-e4b-github-mcp-sft-it",
		"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 "Simon-Liu/gemma-4-e4b-github-mcp-sft-it" \
        --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": "Simon-Liu/gemma-4-e4b-github-mcp-sft-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Simon-Liu/gemma-4-e4b-github-mcp-sft-it with Docker Model Runner:
```
docker model run hf.co/Simon-Liu/gemma-4-e4b-github-mcp-sft-it
```

gemma-4-e4b-github-mcp-sft-it / README.md

Simon-Liu

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e8dae32 verified 27 days ago

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	---
	license: gemma
	base_model: google/gemma-4-E4B-it
	datasets:
	- Simon-Liu/github-mcp-call-reasoning-1k
	language:
	- zh
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- function-calling
	- tool-use
	- github-mcp
	- gemma
	- traditional-chinese
	---

	# GitHub MCP Function-Calling — Gemma 4 E4B（full SFT）

	以 [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it) 為基底，在 [`Simon-Liu/github-mcp-call-reasoning-1k`](https://huggingface.co/datasets/Simon-Liu/github-mcp-call-reasoning-1k) 上做全參數監督式微調（full-parameter SFT），訓練成會先推理、再正確呼叫 GitHub MCP 工具（function calling）的繁體中文模型。

	## 微調前後成效（held-out 測試集，n=50）

	未微調基底 vs 微調後，在相同 held-out 樣本上的工具呼叫正確率：

	\| 指標 \| FT 前 \| FT 後 \| 提升 \|
	\|---\|---:\|---:\|---:\|
	\| 有工具呼叫率 \| 86.0% \| 100.0% \| +14.0% \|
	\| 函式名正確率 \| 86.0% \| 100.0% \| +14.0% \|
	\| 名稱+參數全對率 \| 74.0% \| 98.0% \| +24.0% \|

	> 有工具呼叫率＝是否吐出 `<tool_call>`；函式名正確率＝function name 正確；名稱+參數全對率＝name 與 arguments 皆正確（exact match，最嚴格）。

	## 訓練設定

	- 方法：全參數 SFT（非 LoRA）
	- 基底：`google/gemma-4-E4B-it`
	- 資料：`Simon-Liu/github-mcp-call-reasoning-1k`（~1k 筆 GitHub MCP function-calling，繁中推理）
	- epochs 3、effective batch 8、learning rate 1e-5、optimizer adafactor、max length 4096、bf16

	## 使用方式

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model = AutoModelForCausalLM.from_pretrained("Simon-Liu/gemma-4-e4b-github-mcp-sft-it", torch_dtype=torch.bfloat16, device_map="auto")
	tokenizer = AutoTokenizer.from_pretrained("Simon-Liu/gemma-4-e4b-github-mcp-sft-it")

	messages = [
	{"role": "system", "content": "<工具定義，Hermes 風格 JSON>"},
	{"role": "user", "content": "我現在 GitHub 登入的帳號是誰？"},
	]
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
	print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	## 授權

	衍生自 Google Gemma，使用前請遵守 [Gemma 使用條款](https://ai.google.dev/gemma/terms)。