Instructions to use Maxtimer97/GLM2NSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maxtimer97/GLM2NSA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Maxtimer97/GLM2NSA", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Maxtimer97/GLM2NSA", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Maxtimer97/GLM2NSA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Maxtimer97/GLM2NSA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Maxtimer97/GLM2NSA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Maxtimer97/GLM2NSA
- SGLang
How to use Maxtimer97/GLM2NSA 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 "Maxtimer97/GLM2NSA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Maxtimer97/GLM2NSA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Maxtimer97/GLM2NSA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Maxtimer97/GLM2NSA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Maxtimer97/GLM2NSA with Docker Model Runner:
docker model run hf.co/Maxtimer97/GLM2NSA
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a2f57c7 | 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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import torch
def is_hopper_gpu():
if torch.cuda.is_available():
device_capability = torch.cuda.get_device_capability(0)
major, minor = device_capability
return major == 9
return False
def get_num_warps_stages(head_dim, block_size, is_hopper_gpu):
"""
Returns recommended num_warps and num_stages for a Sparse Attention kernel in Triton.
Args:
head_dim (int): Size of the head dimension.
block_size (int): Size of the block in the attention matrix.
is_hopper_gpu (bool): True if Hopper GPU, False if Ampere GPU.
Returns:
tuple: (num_warps, num_stages) recommended values.
"""
# Determine if head_dim and block_size exceed 64
head_large = head_dim > 64
block_large = block_size > 64
if is_hopper_gpu:
# Hopper GPU recommendations
if head_large and block_large:
num_warps = 8
num_stages = 3
elif head_large or block_large:
num_warps = 4
num_stages = 3
else:
num_warps = 2
num_stages = 2
else:
# Ampere GPU recommendations
if head_large and block_large:
num_warps = 8
num_stages = 3
elif head_large or block_large:
num_warps = 8
num_stages = 3
else:
num_warps = 2
num_stages = 2
return num_warps, num_stages
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