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

File size: 1,019 Bytes

b53331c

{
  "status": "PASS",
  "stage": "B13 independent REAP-Code-S1 manifest validation",
  "calibration_eligible": true,
  "release_eligible": false,
  "model_path": "/workspace/models/glm-4.7-flash",
  "model_revision": "7dd20894a642a0aa287e9827cb1a1f7f91386b67",
  "manifest_path": "/workspace/kguard-reap/data/glm-calibration-reap-code-s1-8192.jsonl",
  "manifest_sha256": "3f2c46de59e42213b9d4c614cd9fdaca3d53b5ee141cf13c13680aee71cc326b",
  "samples": 8192,
  "source_repo": "theblackcat102/evol-codealpaca-v1",
  "source_revision": "c75242318519b5470635e84064baed7c78594020",
  "source_split": "train",
  "contamination_gate": "exact_normalized_primary_user_prompt",
  "evaluation_prompt_exact_overlap_count": 0,
  "stored_token_count_mismatches": 0,
  "stored_token_hash_mismatches": 0,
  "token_length_statistics": {
    "minimum": 34,
    "median": 460.0,
    "maximum": 4013,
    "mean": 533.012451171875,
    "total": 4366438
  },
  "elapsed_seconds": 6.643751670999336,
  "failure_count": 0,
  "failures": []
}