Text Generation
Transformers
Safetensors
English
gemma
jepa
world-models
omnimodal
arc-challenge
mmlu
gsm8k
image-generation
video-generation
audio-generation
Mixture of Experts
sparse-moe
punica
dag-reasoning
compiler-safety
os-computer-use
casp15
structural-biology
custom_code
text-generation-inference
Instructions to use clevrpwn/gmma-jepa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clevrpwn/gmma-jepa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="clevrpwn/gmma-jepa", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("clevrpwn/gmma-jepa", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("clevrpwn/gmma-jepa", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use clevrpwn/gmma-jepa with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "clevrpwn/gmma-jepa" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clevrpwn/gmma-jepa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/clevrpwn/gmma-jepa
- SGLang
How to use clevrpwn/gmma-jepa 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 "clevrpwn/gmma-jepa" \ --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": "clevrpwn/gmma-jepa", "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 "clevrpwn/gmma-jepa" \ --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": "clevrpwn/gmma-jepa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use clevrpwn/gmma-jepa with Docker Model Runner:
docker model run hf.co/clevrpwn/gmma-jepa
File size: 613 Bytes
5e608bf 60cb29b 5e608bf 60cb29b 5e608bf 58edfdf 5e608bf 58edfdf 5e608bf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"architectures": [
"GemmaForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_gmma_jepa.GmmaJEPAConfig",
"AutoModelForCausalLM": "modeling_gmma_jepa.GmmaJEPAForCausalLM"
},
"model_type": "gemma",
"base_model": "google/gemma-2b",
"d_model": 1536,
"vocab_size": 256000,
"num_jepa_layers": 8,
"dag_reasoning_enabled": true,
"universal_compiler_safety": true,
"num_swarm_specialists": 23,
"quantization": "NVFP4_E2M1",
"torch_dtype": "bfloat16",
"transformers_version": "4.45.0",
"organization": "Danger Labs",
"authors": [
"Danger Labs",
"clevrpwn"
]
} |