Instructions to use Jon-Nielsen/GLM-4.7-REAP-30-W4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jon-Nielsen/GLM-4.7-REAP-30-W4A16 with Transformers:

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

pipe = pipeline("text-generation", model="Jon-Nielsen/GLM-4.7-REAP-30-W4A16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jon-Nielsen/GLM-4.7-REAP-30-W4A16")
model = AutoModelForCausalLM.from_pretrained("Jon-Nielsen/GLM-4.7-REAP-30-W4A16", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jon-Nielsen/GLM-4.7-REAP-30-W4A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jon-Nielsen/GLM-4.7-REAP-30-W4A16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jon-Nielsen/GLM-4.7-REAP-30-W4A16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jon-Nielsen/GLM-4.7-REAP-30-W4A16

SGLang

How to use Jon-Nielsen/GLM-4.7-REAP-30-W4A16 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 "Jon-Nielsen/GLM-4.7-REAP-30-W4A16" \
    --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": "Jon-Nielsen/GLM-4.7-REAP-30-W4A16",
		"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 "Jon-Nielsen/GLM-4.7-REAP-30-W4A16" \
        --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": "Jon-Nielsen/GLM-4.7-REAP-30-W4A16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jon-Nielsen/GLM-4.7-REAP-30-W4A16 with Docker Model Runner:
```
docker model run hf.co/Jon-Nielsen/GLM-4.7-REAP-30-W4A16
```

GLM-4.7-REAP-30-W4A16 / quantization_config.json

Jon-Nielsen

Upload W4G128 AutoRound GPTQ quantization of 0xSero/GLM-4.7-REAP-30

0ceb4ae verified 8 months ago

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	{
	"bits": 4,
	"group_size": 128,
	"sym": true,
	"data_type": "int",
	"low_gpu_mem_usage": true,
	"autoround_version": "0.9.4",
	"lm_head": false,
	"provider": "auto-round",
	"quant_method": "gptq",
	"desc_act": false,
	"true_sequential": false,
	"damp_percent": 0.01
	}