Text Generation
Transformers
Safetensors
gemma2
text-generation-inference
8-bit precision
bitsandbytes
Instructions to use nev/gemma-2-9b-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nev/gemma-2-9b-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nev/gemma-2-9b-8bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nev/gemma-2-9b-8bit") model = AutoModelForCausalLM.from_pretrained("nev/gemma-2-9b-8bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nev/gemma-2-9b-8bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nev/gemma-2-9b-8bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nev/gemma-2-9b-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nev/gemma-2-9b-8bit
- SGLang
How to use nev/gemma-2-9b-8bit 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 "nev/gemma-2-9b-8bit" \ --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": "nev/gemma-2-9b-8bit", "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 "nev/gemma-2-9b-8bit" \ --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": "nev/gemma-2-9b-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nev/gemma-2-9b-8bit with Docker Model Runner:
docker model run hf.co/nev/gemma-2-9b-8bit
Gemma 2 9B 8-bit
This is an 8-bit quantized version of Gemma 2 9B. The models belong to Google and are licensed under the Gemma Terms of Use and are only stored in quantized form here for convenience.
How to use
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
dtype = torch.float16
model = AutoModelForCausalLM.from_pretrained("nev/gemma-2-9b-8bit", torch_dtype=dtype, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("nev/gemma-2-9b-8bit")
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