Instructions to use PrimeIntellect/glm4-moe-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PrimeIntellect/glm4-moe-tiny with Transformers:

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

pipe = pipeline("text-generation", model="PrimeIntellect/glm4-moe-tiny")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PrimeIntellect/glm4-moe-tiny")
model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/glm4-moe-tiny")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PrimeIntellect/glm4-moe-tiny with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PrimeIntellect/glm4-moe-tiny"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PrimeIntellect/glm4-moe-tiny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PrimeIntellect/glm4-moe-tiny

SGLang

How to use PrimeIntellect/glm4-moe-tiny 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 "PrimeIntellect/glm4-moe-tiny" \
    --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": "PrimeIntellect/glm4-moe-tiny",
		"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 "PrimeIntellect/glm4-moe-tiny" \
        --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": "PrimeIntellect/glm4-moe-tiny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PrimeIntellect/glm4-moe-tiny with Docker Model Runner:
```
docker model run hf.co/PrimeIntellect/glm4-moe-tiny
```

glm4-moe-tiny

File size: 1,015 Bytes

8fc6463

[gMASK]<sop>
{%- if tools -%}
<|system|>
# 可用工具
{% for tool in tools %}
    {%- set function = tool.function if tool.get("function") else tool %}

## {{ function.name }}

{{ function | tojson(indent=4, ensure_ascii=False) }}
在调用上述函数时，请使用 Json 格式表示调用的参数。
{%- endfor %}
{%- endif -%}

{%- for msg in messages %}
    {%- if msg.role == 'system' %}
<|system|>
{{ msg.content }}
    {%- endif %}
{%- endfor %}

{%- for message in messages if message.role != 'system' %}
    {%- set role = message['role'] %}
    {%- set content = message['content'] %}
    {%- set meta = message.get("metadata", "") %}

    {%- if role == 'user' %}
<|user|>
{{ content }}
    {%- elif role == 'assistant' and not meta %}
<|assistant|>
{{ content }}
    {%- elif role == 'assistant' and meta %}
<|assistant|>{{ meta }}
{{ content }}
    {%- elif role == 'observation' %}
<|observation|>
{{ content }}
    {%- endif %}
{%- endfor %}
{% if add_generation_prompt %}<|assistant|>{% endif %}