G9v3-39A5B

⚠️ Preview Release

GitHub | Hugging Face

This is a preview release. G9v3-39A5B is under active development — expect continued updates with improved performance and additional capabilities in the near future.

Introduction

G9v3-39A5B is a Mixture-of-Experts (MoE) causal language model from the AI9Stars team, with 39B total parameters and 5B activated per token. The sparse design keeps inference cost close to a small dense model while retaining the capacity of a much larger one, making it a practical choice for local and self-hosted deployment.

It targets everyday assistant use, coding, tool-use workflows, and reasoning tasks, and supports both Think / No Think modes through the same checkpoint.

Model Information

• Type: Causal Language Model (Mixture-of-Experts)
• Total Parameters: ~39B
• Activated Parameters: ~5B per token
• Context Length: 131,072

Quickstart

vLLM

pip install "vllm>=0.21"
vllm serve ai9stars/G9v3-39A5B --port 8000

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai9stars/G9v3-39A5B",
    "messages": [{"role": "user", "content": "Who are you?"}],
    "max_tokens": 128,
    "temperature": 0.7
  }'

SGLang

pip install "sglang[srt]>=0.5.12"
python -m sglang.launch_server --model-path ai9stars/G9v3-39A5B --port 30000

curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai9stars/G9v3-39A5B",
    "messages": [{"role": "user", "content": "Who are you?"}],
    "max_tokens": 128,
    "temperature": 0.7
  }'

Transformers

pip install -U "transformers>=5.6" accelerate torch

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ai9stars/G9v3-39A5B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Who are you?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Recommended sampling parameters:

Mode	Recommended params	Enable
Think	temperature=1.0, top_p=0.95	enable_thinking=True
No Think	temperature=0.7, top_p=0.95	enable_thinking=False

Limitations and Responsible Use

G9v3-39A5B is a language model that generates content based on learned statistical patterns from training data. It may produce inaccurate, biased, or unsafe outputs, and generated content should be reviewed and verified before use in high-stakes settings. As a preview release, its behavior may change between versions. Users are responsible for evaluating outputs, applying appropriate safeguards, and complying with applicable laws, regulations, and platform policies.

License

This repository and the G9v3 model weights are released under the Apache-2.0 License.