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
```

XuanzhiRen commited on 2 days ago

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+---
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-1.7B-Base
+tags:
+- qwen3
+- reaper
+- causal-lm
+---
+# 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]](https://huggingface.co/collections/FrenzyMath/reaper-6ad03ad88957749d6114b8f6).
+## 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](https://huggingface.co/FrenzyMath/Reaper-Base) | Base checkpoint |
+| [FrenzyMath/Reaper-SFT](https://huggingface.co/FrenzyMath/Reaper-SFT) | Supervised fine-tuning |
+| [FrenzyMath/Reaper-1](https://huggingface.co/FrenzyMath/Reaper-1) | Final policy-value checkpoint |
+## Usage
+```python
+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.