SPADE-Qwen3-8B-Games
SPADE checkpoint for the games setting, trained from Qwen/Qwen3-8B.
SPADE trains a single model in two roles: an Environment Designer that writes executable environments, and a Reasoning Agent that solves them. The Designer is rewarded for producing environments at the frontier of what the Agent can currently solve, so the curriculum keeps pace with the policy instead of being fixed in advance. See the paper for details.
| Base model | Qwen/Qwen3-8B |
| Setting | games |
| Context length | 32,768 |
Quickstart
We advise you to use the latest version of transformers.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "spade-rl/SPADE-Qwen3-8B-Games"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
prompt = "Give me a short introduction to large language model."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=16384)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
print(tokenizer.decode(output_ids, skip_special_tokens=True))
Deployment
For deployment, you can use sglang>=0.4.6.post1 or vllm>=0.8.5 to create an
OpenAI-compatible API endpoint:
- SGLang:
python -m sglang.launch_server --model-path spade-rl/SPADE-Qwen3-8B-Games --context-length 32768 - vLLM:
vllm serve spade-rl/SPADE-Qwen3-8B-Games --max-model-len 32768
Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value.
Best practices
We recommend temperature=0.6, top_p=0.95, top_k=20, min_p=0, following
the sampling guidance on the base model card.
Related artifacts
- Grounding corpora: games | tool use
- All SPADE models, data and generated environments: huggingface.co/spade-rl