Instructions to use Maxtimer97/GLM2NSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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, device_map="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
```

Maxtimer97 commited on Sep 30, 2025

Commit

bdf9fdc

1 Parent(s): bf53a99

Safe cache check

Browse files

Files changed (1) hide show

modeling_chatglm.py +3 -2

modeling_chatglm.py CHANGED Viewed

@@ -717,8 +717,9 @@ class SelfAttention(torch.nn.Module):
         # adjust key and value for inference
         if kv_cache is not None:
             cache_k, cache_v = kv_cache
-            key_layer = torch.cat((cache_k, key_layer), dim=2)
-            value_layer = torch.cat((cache_v, value_layer), dim=2)
         if use_cache:
             if kv_cache is None:
                 kv_cache = torch.cat((key_layer.unsqueeze(0).unsqueeze(0), value_layer.unsqueeze(0).unsqueeze(0)),

         # adjust key and value for inference
         if kv_cache is not None:
             cache_k, cache_v = kv_cache
+            if cache_k is not None and cache_v is not None:
+                key_layer = torch.cat((cache_k, key_layer), dim=2)
+                value_layer = torch.cat((cache_v, value_layer), dim=2)
         if use_cache:
             if kv_cache is None:
                 kv_cache = torch.cat((key_layer.unsqueeze(0).unsqueeze(0), value_layer.unsqueeze(0).unsqueeze(0)),