Instructions to use QuantTrio/GLM-4.7-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/GLM-4.7-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="QuantTrio/GLM-4.7-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("QuantTrio/GLM-4.7-AWQ")
model = AutoModelForMultimodalLM.from_pretrained("QuantTrio/GLM-4.7-AWQ")
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 Settings

vLLM

How to use QuantTrio/GLM-4.7-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/GLM-4.7-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/GLM-4.7-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/GLM-4.7-AWQ

SGLang

How to use QuantTrio/GLM-4.7-AWQ 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 "QuantTrio/GLM-4.7-AWQ" \
    --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": "QuantTrio/GLM-4.7-AWQ",
		"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 "QuantTrio/GLM-4.7-AWQ" \
        --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": "QuantTrio/GLM-4.7-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/GLM-4.7-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/GLM-4.7-AWQ
```

Revert remplate

by s-yanev - opened 27 days ago

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

-27

Files changed (1) hide show

chat_template.jinja +2 -27

chat_template.jinja CHANGED Viewed

@@ -1,17 +1,14 @@
 [gMASK]<sop>
-{%- if tools and not (structured_output is defined and structured_output.enabled) -%}
 <|system|>
 # Tools
 You may call one or more functions to assist with the user query.
 You are provided with function signatures within <tools></tools> XML tags:
 <tools>
 {% for tool in tools %}
 {{ tool | tojson(ensure_ascii=False) }}
 {% endfor %}
 </tools>
 For each function call, output the function name and arguments within the following XML format:
 <tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}
 {%- macro visible_text(content) -%}
@@ -78,29 +75,7 @@ For each function call, output the function name and arguments within the follow
 <tool_response>{{ tr.output if tr.output is defined else tr }}</tool_response>{% endfor -%}
 {% endif -%}
 {%- elif m.role == 'system' -%}
-<|system|>
-{%- if structured_output is defined and structured_output.enabled and loop.index0 == 0 -%}
-# Structured Output
-You are a structured output engine.
-Output ONLY valid JSON. Follow the JSON schema exactly.
-Do not output:
-- markdown
-- prose
-- explanations
-- XML
-- comments
-- chain-of-thought
-JSON Schema:
-{{ structured_output.schema | tojson(indent=2) }}
-{{ visible_text(m.content) }}
-{%- else -%}
-{{ visible_text(m.content) }}
-{%- endif -%}
 {%- endif -%}
 {%- endfor -%}
 {%- if add_generation_prompt -%}

 [gMASK]<sop>
+{%- if tools -%}
 <|system|>
 # Tools
 You may call one or more functions to assist with the user query.
 You are provided with function signatures within <tools></tools> XML tags:
 <tools>
 {% for tool in tools %}
 {{ tool | tojson(ensure_ascii=False) }}
 {% endfor %}
 </tools>
 For each function call, output the function name and arguments within the following XML format:
 <tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}
 {%- macro visible_text(content) -%}
 <tool_response>{{ tr.output if tr.output is defined else tr }}</tool_response>{% endfor -%}
 {% endif -%}
 {%- elif m.role == 'system' -%}
+<|system|>{{ visible_text(m.content) }}
 {%- endif -%}
 {%- endfor -%}
 {%- if add_generation_prompt -%}