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Cataloged from ai9stars/G9v3-3B
All models by ai9stars →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.
LlamaForCausalLMRecommended sampling parameters:
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.
This repository and the G9v3 model weights are released under the Apache-2.0 License.
pip install "vllm>=0.21"vllm serve ai9stars/G9v3-3B --port 8000curl 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 }'pip install "sglang[srt]>=0.5.12"python -m sglang.launch_server --model-path ai9stars/G9v3-3B --port 30000curl 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 }'pip install -U "transformers>=5.6" accelerate torchfrom 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))