Introduction
G9v3-3B is a dense 3B causal language model from the AI9Stars team, built for local deployment and resource-constrained scenarios. It targets everyday assistant use, coding, tool-use workflows, and reasoning tasks where a compact model is preferred.
Model Information
- Type: Causal Language Model
- Architecture: Standard
LlamaForCausalLM - Number of Parameters: ~3B
- Context Length: 131,072
Quickstart
vLLM
pip install "vllm>=0.21"
vllm serve ai9stars/G9v3-3B --port 8000
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "ai9stars/G9v3-3B",
"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/G9v3-3B --port 30000
curl http://localhost:30000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "ai9stars/G9v3-3B",
"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/G9v3-3B"
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=0.9, top_p=0.95 | enable_thinking=True |
| No Think | temperature=0.7, top_p=0.95 | enable_thinking=False |
Limitations and Responsible Use
G9v3-3B 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. 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.