Instructions to use FrenzyMath/Reaper-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrenzyMath/Reaper-1 with Transformers:

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

pipe = pipeline("text-generation", model="FrenzyMath/Reaper-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FrenzyMath/Reaper-1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FrenzyMath/Reaper-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrenzyMath/Reaper-1"
# 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-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FrenzyMath/Reaper-1

SGLang

How to use FrenzyMath/Reaper-1 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-1" \
    --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-1",
		"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-1" \
        --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-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FrenzyMath/Reaper-1 with Docker Model Runner:
```
docker model run hf.co/FrenzyMath/Reaper-1
```

Reaper-1 / README.md

XuanzhiRen

Fix collection link in model card

b94c879 verified 2 days ago

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	---
	library_name: transformers
	pipeline_tag: text-generation
	base_model: FrenzyMath/Reaper-SFT
	base_model_relation: finetune
	tags:
	- qwen3
	- reaper
	- policy-value
	- dual-head
	---

	# Reaper-1

	Reaper-1 is the final policy-value checkpoint in the Reaper model family. The repository stores a Qwen3 1.7B policy model together with a separate categorical value head.

	This checkpoint is part of the [Reaper model collection](https://huggingface.co/collections/FrenzyMath/reaper-6ad03ad88957749d6114b8f6).

	## Checkpoint structure

	- `policy_model/`: `Qwen3ForCausalLM` policy checkpoint
	- `value_head.pt`: state dictionary for a linear value head with 32 output bins
	- `dual_head_meta.json`: policy/value loss weights and value-head configuration
	- Tokenizer and chat-template files are stored at the repository root

	The included metadata specifies `policy_loss_weight=1.0`, `value_loss_weight=0.1`, and `value_dropout=0.1`.

	## 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 \|

	## Load the policy model

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo_id = "FrenzyMath/Reaper-1"

	tokenizer = AutoTokenizer.from_pretrained(repo_id)
	policy_model = AutoModelForCausalLM.from_pretrained(
	repo_id,
	subfolder="policy_model",
	torch_dtype="auto",
	device_map="auto",
	)
	```

	## Load the value head

	```python
	import json
	import torch
	from huggingface_hub import hf_hub_download

	meta_path = hf_hub_download(repo_id, "dual_head_meta.json")
	head_path = hf_hub_download(repo_id, "value_head.pt")

	with open(meta_path, encoding="utf-8") as handle:
	metadata = json.load(handle)

	value_head = torch.nn.Linear(
	policy_model.config.hidden_size,
	metadata["num_value_bins"],
	)
	value_head.load_state_dict(
	torch.load(head_path, map_location="cpu", weights_only=True)
	)
	value_head.eval()
	```

	The policy and value head are stored separately. Integrating value predictions with policy hidden states depends on the calling application.

	## Intended use and limitations

	This checkpoint is intended for research with policy-value modeling. Users should independently evaluate it for their target domain. Training-data details, benchmark results, and a license declaration are not included in the current release.