Instructions to use unsloth/GLM-4-32B-0414 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/GLM-4-32B-0414 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="unsloth/GLM-4-32B-0414")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/GLM-4-32B-0414")
model = AutoModelForCausalLM.from_pretrained("unsloth/GLM-4-32B-0414", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use unsloth/GLM-4-32B-0414 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/GLM-4-32B-0414"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/GLM-4-32B-0414",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/GLM-4-32B-0414

SGLang

How to use unsloth/GLM-4-32B-0414 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 "unsloth/GLM-4-32B-0414" \
    --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": "unsloth/GLM-4-32B-0414",
		"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 "unsloth/GLM-4-32B-0414" \
        --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": "unsloth/GLM-4-32B-0414",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use unsloth/GLM-4-32B-0414 with Docker Model Runner:
```
docker model run hf.co/unsloth/GLM-4-32B-0414
```

GLM-4-32B-0414 / tokenizer_config.json

danielhanchen

Upload folder using huggingface_hub

66f4ea4 verified over 1 year ago

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4.29 kB

{
  "added_tokens_decoder": {
    "151329": {
      "content": "<|endoftext|>",
      "lstrip": false,
      "normalized": false,
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      "single_word": false,
      "special": true
    },
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      "special": true
    },
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    },
    "151334": {
      "content": "<eop>",
      "lstrip": false,
      "normalized": false,
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    },
    "151335": {
      "content": "<|system|>",
      "lstrip": false,
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    },
    "151336": {
      "content": "<|user|>",
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    "151337": {
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    "151338": {
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    "151339": {
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    "151340": {
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    "151341": {
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    "151342": {
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      "lstrip": false,
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      "special": true
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  "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|>"
  ],
  "bos_token": null,
  "clean_up_tokenization_spaces": false,
  "do_lower_case": false,
  "eos_token": "<|user|>",
  "extra_special_tokens": {},
  "model_input_names": [
    "input_ids",
    "attention_mask"
  ],
  "model_max_length": 32768,
  "pad_token": "[MASK]",
  "padding_side": "left",
  "remove_space": false,
  "tokenizer_class": "PreTrainedTokenizer",
  "unk_token": null,
  "chat_template": "[gMASK]<sop>\n{%- if tools -%}\n<|system|>\n# 可用工具\n{% for tool in tools %}\n    {%- set function = tool.function if tool.get(\"function\") else tool %}\n\n## {{ function.name }}\n\n{{ function | tojson(indent=4)|string }}\n在调用上述函数时，请使用 Json 格式表示调用的参数。\n{%- endfor %}\n{%- endif -%}\n\n{%- for msg in messages %}\n    {%- if msg.role == 'system' %}\n<|system|>\n{{ msg.content }}\n    {%- endif %}\n{%- endfor %}\n\n{%- for message in messages if message.role != 'system' %}\n    {%- set role = message['role'] %}\n    {%- set content = message['content'] %}\n    {%- set meta = message.get(\"metadata\", \"\") %}\n\n    {%- if role == 'user' %}\n<|user|>\n{{ content }}\n    {%- elif role == 'assistant' and not meta %}\n<|assistant|>\n{{ content }}\n    {%- elif role == 'assistant' and meta %}\n<|assistant|>{{ meta }}\n{{ content }}\n    {%- elif role == 'observation' %}\n<|observation|>\n{{ content }}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}<|assistant|>{% endif %}"
}

	{
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	"<sop>",
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	"<\|user\|>",
	"<\|assistant\|>",
	"<\|observation\|>",
	"<\|begin_of_image\|>",
	"<\|end_of_image\|>",
	"<\|begin_of_video\|>",
	"<\|end_of_video\|>"
	],
	"bos_token": null,
	"clean_up_tokenization_spaces": false,
	"do_lower_case": false,
	"eos_token": "<\|user\|>",
	"extra_special_tokens": {},
	"model_input_names": [
	"input_ids",
	"attention_mask"
	],
	"model_max_length": 32768,
	"pad_token": "[MASK]",
	"padding_side": "left",
	"remove_space": false,
	"tokenizer_class": "PreTrainedTokenizer",
	"unk_token": null,
	"chat_template": "[gMASK]<sop>\n{%- if tools -%}\n<\|system\|>\n# 可用工具\n{% for tool in tools %}\n {%- set function = tool.function if tool.get(\"function\") else tool %}\n\n## {{ function.name }}\n\n{{ function \| tojson(indent=4)\|string }}\n在调用上述函数时，请使用 Json 格式表示调用的参数。\n{%- endfor %}\n{%- endif -%}\n\n{%- for msg in messages %}\n {%- if msg.role == 'system' %}\n<\|system\|>\n{{ msg.content }}\n {%- endif %}\n{%- endfor %}\n\n{%- for message in messages if message.role != 'system' %}\n {%- set role = message['role'] %}\n {%- set content = message['content'] %}\n {%- set meta = message.get(\"metadata\", \"\") %}\n\n {%- if role == 'user' %}\n<\|user\|>\n{{ content }}\n {%- elif role == 'assistant' and not meta %}\n<\|assistant\|>\n{{ content }}\n {%- elif role == 'assistant' and meta %}\n<\|assistant\|>{{ meta }}\n{{ content }}\n {%- elif role == 'observation' %}\n<\|observation\|>\n{{ content }}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}<\|assistant\|>{% endif %}"
	}