Text Generation
Transformers
PyTorch
English
shram
research
sparse-attention
mixture-of-experts
custom_code
Instructions to use smithblack-0/SHRAM-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smithblack-0/SHRAM-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="smithblack-0/SHRAM-dev", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("smithblack-0/SHRAM-dev", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use smithblack-0/SHRAM-dev with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smithblack-0/SHRAM-dev" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "smithblack-0/SHRAM-dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/smithblack-0/SHRAM-dev
- SGLang
How to use smithblack-0/SHRAM-dev 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 "smithblack-0/SHRAM-dev" \ --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": "smithblack-0/SHRAM-dev", "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 "smithblack-0/SHRAM-dev" \ --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": "smithblack-0/SHRAM-dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use smithblack-0/SHRAM-dev with Docker Model Runner:
docker model run hf.co/smithblack-0/SHRAM-dev
| from .configuration import ShramConfig | |
| from .decoder_layer import DecoderLayer | |
| from .huggingface import ShramForCausalLM | |
| from .__attention__load_balance_loss import LoadBalanceLoss | |
| from .mlp import SwiGLUMLP | |
| from .model import ShramModel | |
| from .rope import RotaryEmbedding | |
| from .__attention__router import MoSRAHRouter | |
| from .__cache__mosrah_cache import MoSRAHCache | |
| __all__ = [ | |
| "DecoderLayer", | |
| "LoadBalanceLoss", | |
| "MoSRAHCache", | |
| "MoSRAHRouter", | |
| "ShramConfig", | |
| "ShramForCausalLM", | |
| "ShramModel", | |
| "RotaryEmbedding", | |
| "SwiGLUMLP", | |
| ] | |