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
File size: 563 Bytes
1670228 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | 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",
]
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