Instructions to use the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320 with Transformers:

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

pipe = pipeline("text-generation", model="the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320")
model = AutoModelForCausalLM.from_pretrained("the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320

SGLang

How to use the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320 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 "the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320" \
    --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": "the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320",
		"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 "the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320" \
        --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": "the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320 with Docker Model Runner:
```
docker model run hf.co/the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320
```

SPIRAL Qwen3-8B Multi-Agent Model

This model was trained using the SPIRAL (Self-Play Iterative Reinforcement learning for Adaptation and Learning) framework.

Model Details

Base Model: Qwen/Qwen3-8B-Base
Training Framework: SPIRAL
Checkpoint: step_00320
Model Size: 8B parameters
Training Date: 2025-08-31

Training Configuration

The model was trained with self-play on multiple environments:

KuhnPoker-v1
TicTacToe-v0
SimpleNegotiation-v1

Training Parameters

{
  "learning_rate": "1e-6",
  "train_batch_size": 128,
  "num_ppo_epochs": 2,
  "temperature": 1.0,
  "max_model_len": 16384,
  "environments": [
    "KuhnPoker-v1",
    "TicTacToe-v0",
    "SimpleNegotiation-v1"
  ],
  "base_model": "Qwen/Qwen3-8B-Base",
  "framework": "SPIRAL"
}

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320")
model = AutoModelForCausalLM.from_pretrained(
    "the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

License

This model is licensed under the Apache License 2.0.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00320

Base model

Qwen/Qwen3-8B-Base

Finetuned

(439)

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