Instructions to use RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code with Transformers:

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

pipe = pipeline("text-generation", model="RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code")
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("RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code")
model = AutoModelForCausalLM.from_pretrained("RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code", 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 RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code

SGLang

How to use RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code 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 "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code" \
    --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": "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code",
		"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 "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code" \
        --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": "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code with Docker Model Runner:
```
docker model run hf.co/RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP-Code
```

GLM-4.7-Flash-24B-A3B-KREAP-Code / README.md

Commit History

Sep 28

06:58
Make the 24B KREAP-Code card easier to assess⁠

buugiiiiii

Verified signature
c25338b
06:39
Align control comparisons with the KREAP-Code identity⁠

buugiiiiii

Verified signature
31137aa
06:34
Align the code-control card with the KREAP collection identity⁠

buugiiiiii

Verified signature
1330abb

Jul 29

01:51
Add validated B20 LiveCodeBench results to the model card

buugiiiiii

Verified signature
33e1c73

Jul 27

22:07
Clarify the evidence boundary of the experimental release⁠

buugiiiiii

Verified signature
8da53a3
04:06
Add verified model card, benchmark artifacts, and survivor manifest

buugiiiiii

Verified signature
efdaa23
04:02
Add verified model card, benchmark artifacts, and survivor manifest

buugiiiiii

Verified signature
0e452cc
03:39
Add verified model card, benchmark artifacts, and survivor manifest

buugiiiiii

Verified signature
b53331c
03:29
Add verified model card, benchmark artifacts, and survivor manifest

buugiiiiii

Verified signature
5786af8

Commit History

Sep 28

Make the 24B KREAP-Code card easier to assess⁠

Align control comparisons with the KREAP-Code identity⁠

Align the code-control card with the KREAP collection identity⁠

Jul 29

Add validated B20 LiveCodeBench results to the model card

Jul 27

Clarify the evidence boundary of the experimental release⁠

Add verified model card, benchmark artifacts, and survivor manifest

Add verified model card, benchmark artifacts, and survivor manifest

Add verified model card, benchmark artifacts, and survivor manifest

Add verified model card, benchmark artifacts, and survivor manifest