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SPADE-Qwen3-8B-Games/README.md
ModelHub XC cd6a817c8d 初始化项目,由ModelHub XC社区提供模型
Model: spade-rl/SPADE-Qwen3-8B-Games
Source: Original Platform
2026-10-19 18:24:19 +08:00

2.6 KiB

license, base_model, library_name, pipeline_tag, tags
license base_model library_name pipeline_tag tags
apache-2.0 Qwen/Qwen3-8B transformers text-generation
spade
self-play
reinforcement-learning
environment-generation
games

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.