Libraries Transformers How to use ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor") # Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor")
model = AutoModelForMultimodalLM.from_pretrained("ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor") Notebooks Google Colab Kaggle Local Apps Settings vLLM How to use ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Use Docker docker model run hf.co/ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor SGLang How to use ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor 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 "ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' 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 "ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Unsloth Studio How to use ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL) curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor to start chatting Install Unsloth Studio (Windows) irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor to start chatting Load model with FastModel pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor",
max_seq_length=2048,
) Docker Model Runner How to use ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor with Docker Model Runner:
docker model run hf.co/ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor 
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ntvcie/Gemma2bVinhntV01_16b_1epoch_afactor")