Text Generation
Transformers
PyTorch
Safetensors
English
gemma2
text-generation-inference
unsloth
trl
Instructions to use EpistemeAI/Athena-codegemma-2-9b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EpistemeAI/Athena-codegemma-2-9b-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EpistemeAI/Athena-codegemma-2-9b-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Athena-codegemma-2-9b-v1") model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Athena-codegemma-2-9b-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EpistemeAI/Athena-codegemma-2-9b-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EpistemeAI/Athena-codegemma-2-9b-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EpistemeAI/Athena-codegemma-2-9b-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EpistemeAI/Athena-codegemma-2-9b-v1
- SGLang
How to use EpistemeAI/Athena-codegemma-2-9b-v1 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 "EpistemeAI/Athena-codegemma-2-9b-v1" \ --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": "EpistemeAI/Athena-codegemma-2-9b-v1", "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 "EpistemeAI/Athena-codegemma-2-9b-v1" \ --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": "EpistemeAI/Athena-codegemma-2-9b-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use EpistemeAI/Athena-codegemma-2-9b-v1 with Docker Model Runner:
docker model run hf.co/EpistemeAI/Athena-codegemma-2-9b-v1
Download tokenizer.model from EpistemeAI/Athena-codegemma-2-9b-v1: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/EpistemeAI/Athena-codegemma-2-9b-v1/resolve/refs%2Fpr%2F1/tokenizer.model
- Command line
-
hf download hf://EpistemeAI/Athena-codegemma-2-9b-v1@refs/pr/1/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/EpistemeAI/Athena-codegemma-2-9b-v1/resolve/refs%2Fpr%2F1/tokenizer.model
4.24 MB
- Xet hash:
- 4747d621061ff864406560d79c8c3e355c05f2e88ba30eba88c8d5881d3d220d
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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