Instructions to use Saad4web/FunctionGemma-Director-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Saad4web/FunctionGemma-Director-V1 with Transformers:

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

pipe = pipeline("text-generation", model="Saad4web/FunctionGemma-Director-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Saad4web/FunctionGemma-Director-V1")
model = AutoModelForCausalLM.from_pretrained("Saad4web/FunctionGemma-Director-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Saad4web/FunctionGemma-Director-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Saad4web/FunctionGemma-Director-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saad4web/FunctionGemma-Director-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Saad4web/FunctionGemma-Director-V1

SGLang

How to use Saad4web/FunctionGemma-Director-V1 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 "Saad4web/FunctionGemma-Director-V1" \
    --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": "Saad4web/FunctionGemma-Director-V1",
		"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 "Saad4web/FunctionGemma-Director-V1" \
        --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": "Saad4web/FunctionGemma-Director-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Saad4web/FunctionGemma-Director-V1 with Docker Model Runner:
```
docker model run hf.co/Saad4web/FunctionGemma-Director-V1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🎬 FunctionGemma-Director-V1

FunctionGemma-Director-V1 is a specialized lightweight AI model (270M parameters) designed to automate the production of viral short-form gaming videos (TikTok/Shorts/Reels).

It acts as a "Creative Director", converting a simple video title into a structured JSON editing plan, executing a "Trojan Horse" monetization strategy by seamlessly integrating CPA offers into content.

🚀 Key Features

Size: ~540MB (Runs smoothly on free Colab/CPU).
Strategy: Automatically places "High Retention Hooks" and injects "CPA Offers" at the most effective timestamps.
Output: Strict JSON format compatible with Python video automation engines (MoviePy).

💻 How to Use

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import json

# 1. Load the Model
model_id = "Saad4web/FunctionGemma-Director-V1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    device_map="auto", 
    torch_dtype=torch.float16 # Optimized for low memory
)

# 2. Define the Tools (The Model's Vocabulary)
tools_schema = [
    {"name": "add_video_clip", "parameters": {"file_path": "string", "duration": "number"}},
    {"name": "add_text_overlay", "parameters": {"text": "string", "color": "string"}},
]

# 3. Create the Prompt
video_title = "TOP 3|SCARIEST HORROR GAMES|*DONT WATCH ALONE*"
system_msg = f"You are a specialized video editor AI. Available tools: {json.dumps(tools_schema)}"
messages = [{"role": "user", "content": system_msg + f"\n\nCreate a viral video plan for: {video_title}"}]

# 4. Generate the Plan
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

outputs = model.generate(
    input_ids, 
    max_new_tokens=512, 
    do_sample=True, 
    temperature=0.1
)

# 5. Get the JSON
plan = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
print(plan)

#🛠️ Training Details
    Architecture: Fine-tuned google/functiongemma-270m-it.
    Dataset: Synthetic dataset generated via Knowledge Distillation (Teacher: GPT-4o/Gemini 2.0).
    Method: Full Fine-Tuning using LLaMA Factory.
    Created by [Saad4web]

Downloads last month: 3

Safetensors

Model size

0.3B params

Tensor type

F16

Model tree for Saad4web/FunctionGemma-Director-V1

Base model

google/functiongemma-270m-it

Finetuned

(409)

this model

Quantizations

1 model