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 / tokenizer_config.json

Maxtimer97

Upload tokenizer

2495dfe verified 8 months ago

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3.23 kB

	{
	"added_tokens_decoder": {
	"151329": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151330": {
	"content": "[MASK]",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151331": {
	"content": "[gMASK]",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151332": {
	"content": "[sMASK]",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151333": {
	"content": "<sop>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151334": {
	"content": "<eop>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151335": {
	"content": "<\|system\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151336": {
	"content": "<\|user\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151337": {
	"content": "<\|assistant\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151338": {
	"content": "<\|observation\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151339": {
	"content": "<\|begin_of_image\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151340": {
	"content": "<\|end_of_image\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151341": {
	"content": "<\|begin_of_video\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151342": {
	"content": "<\|end_of_video\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"additional_special_tokens": [
	"<\|endoftext\|>",
	"[MASK]",
	"[gMASK]",
	"[sMASK]",
	"<sop>",
	"<eop>",
	"<\|system\|>",
	"<\|user\|>",
	"<\|assistant\|>",
	"<\|observation\|>",
	"<\|begin_of_image\|>",
	"<\|end_of_image\|>",
	"<\|begin_of_video\|>",
	"<\|end_of_video\|>"
	],
	"auto_map": {
	"AutoTokenizer": [
	"tokenization_chatglm.ChatGLM4Tokenizer",
	null
	]
	},
	"clean_up_tokenization_spaces": false,
	"do_lower_case": false,
	"eos_token": "<\|endoftext\|>",
	"extra_special_tokens": {},
	"model_max_length": 128000,
	"pad_token": "<\|endoftext\|>",
	"padding_side": "left",
	"remove_space": false,
	"tokenizer_class": "ChatGLM4Tokenizer"
	}