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
```

buugiiiiii commited on Sep 28

Commit

31137aa

verified ·

1 Parent(s): 1330abb

Align control comparisons with the KREAP-Code identity

Browse files

Use KREAP-Code consistently in benchmark and comparison labels

Constraint: Preserve the standard REAP control disclosure
Confidence: high
Scope-risk: narrow
Tested: Targeted editor replacements
Not-tested: Inference behavior

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -49,7 +49,7 @@ model.
 ## Benchmark results
-| benchmark | base | KREAP 12.5% | KREAP force-fit 25% | KREAP force-fit 50% | **REAP-Code 25% (this checkpoint)** |
 |---|---:|---:|---:|---:|---:|
 | **Korean (held-out harness)** |  |  |  |  |  |
 | KMMLU | 49.4 | 48.8 | 42.4 | 23.6 | 35.0 |
@@ -180,7 +180,7 @@ criteria do not apply because Korean hard protection was intentionally omitted.
 - This is not a controlled single-variable comparison: the two checkpoints differ in both
   calibration data and survivor-selection rules. It cannot isolate the causal effect of hard
   protection.
-- Machine-readable results include direct KREAP-48E versus REAP-Code-48E McNemar tests for 11
   non-Korean binary/pass-fail tasks. Direct paired tests for the Korean benchmark set, aggregate
   bootstrap confidence intervals, multiple-comparison correction, and repeated calibration seeds
   are absent.

 ## Benchmark results
+| benchmark | base | KREAP 12.5% | KREAP force-fit 25% | KREAP force-fit 50% | **KREAP-Code 25% (this checkpoint)** |
 |---|---:|---:|---:|---:|---:|
 | **Korean (held-out harness)** |  |  |  |  |  |
 | KMMLU | 49.4 | 48.8 | 42.4 | 23.6 | 35.0 |
 - This is not a controlled single-variable comparison: the two checkpoints differ in both
   calibration data and survivor-selection rules. It cannot isolate the causal effect of hard
   protection.
+- Machine-readable results include direct KREAP-48E versus KREAP-Code-48E McNemar tests for 11
   non-Korean binary/pass-fail tasks. Direct paired tests for the Korean benchmark set, aggregate
   bootstrap confidence intervals, multiple-comparison correction, and repeated calibration seeds
   are absent.