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, 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
```

GLM2NSA / config.json

Maxtimer97

Changed to correct architecture name for vllm

d97e95b verified 8 months ago

raw

history blame contribute delete

1.61 kB

	{
	"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
	}