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
```

GLM2NSA

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{
  "add_bias_linear": false,
  "add_qkv_bias": true,
  "apply_query_key_layer_scaling": true,
  "apply_residual_connection_post_layernorm": false,
  "architectures": [
    "ChatGLM2NSAForCausalLM"
  ],
  "attention_dropout": 0.0,
  "attention_softmax_in_fp32": true,
  "attn_implementation": "nsa",
  "auto_map": {
    "AutoConfig": "configuration_chatglm.ChatGLMConfig",
    "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
  },
  "bias_dropout_fusion": true,
  "block_size": 64,
  "classifier_dropout": null,
  "dtype": "bfloat16",
  "eos_token_id": [
    151329,
    151336,
    151338
  ],
  "ffn_hidden_size": 13696,
  "fp32_residual_connection": false,
  "hidden_dropout": 0.0,
  "hidden_size": 4096,
  "init_blocks": 1,
  "kernel_size": 64,
  "kernel_stride": 64,
  "kv_channels": 128,
  "layernorm_epsilon": 1.5625e-07,
  "local_blocks": 2,
  "model_type": "chatglm",
  "multi_query_attention": true,
  "multi_query_group_num": 2,
  "num_attention_heads": 32,
  "num_hidden_layers": 40,
  "num_layers": 40,
  "original_rope": true,
  "pad_token_id": 151329,
  "padded_vocab_size": 151552,
  "post_layer_norm": true,
  "rmsnorm": true,
  "rope_ratio": 500,
  "seq_length": 131072,
  "tie_word_embeddings": false,
  "topk": 16,
  "transformers_version": "4.56.1",
  "use_cache": true,
  "vocab_size": 151552,
  "window_size": 512
}