Instructions to use FrenzyMath/Reaper-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrenzyMath/Reaper-Base with Transformers:

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

pipe = pipeline("text-generation", model="FrenzyMath/Reaper-Base")
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("FrenzyMath/Reaper-Base")
model = AutoModelForCausalLM.from_pretrained("FrenzyMath/Reaper-Base", 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 FrenzyMath/Reaper-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FrenzyMath/Reaper-Base

SGLang

How to use FrenzyMath/Reaper-Base 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 "FrenzyMath/Reaper-Base" \
    --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": "FrenzyMath/Reaper-Base",
		"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 "FrenzyMath/Reaper-Base" \
        --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": "FrenzyMath/Reaper-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FrenzyMath/Reaper-Base with Docker Model Runner:
```
docker model run hf.co/FrenzyMath/Reaper-Base
```

Reaper-Base

Reaper-Base is the base checkpoint released with the Reaper model family. It uses the Qwen3 causal language model architecture with approximately 1.72B parameters and BF16 weights.

This checkpoint is part of the Reaper model collection.

Model details

Architecture: Qwen3ForCausalLM
Parameters: 1,720,574,976
Weight format: safetensors
Context length in the model configuration: 131,072 tokens
Vocabulary size: 151,936

Model family

Checkpoint	Stage
FrenzyMath/Reaper-Base	Base checkpoint
FrenzyMath/Reaper-SFT	Supervised fine-tuning
FrenzyMath/Reaper-1	Final policy-value checkpoint

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "FrenzyMath/Reaper-Base"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)

Use a recent version of transformers with Qwen3 support.

Intended use and limitations

This release is intended for research and for initializing later checkpoints in the Reaper family. Users should independently evaluate the model for their target domain. Training-data details, benchmark results, and a license declaration are not included in the current release.