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Model: spade-rl/SPADE-Qwen3-4B-Games
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- spade
+- self-play
+- reinforcement-learning
+- environment-generation
+- games
+---
+
+# SPADE-Qwen3-4B-Games
+
+SPADE checkpoint for the **games** setting, trained from [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507).
+
+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](https://arxiv.org/abs/2608.19197) for details.
+
+| | |
+|---|---|
+| Base model | [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) |
+| Setting | games |
+| Context length | 262,144 |
+
+## Quickstart
+
+We advise you to use the latest version of `transformers`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "spade-rl/SPADE-Qwen3-4B-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:
+  ```shell
+  python -m sglang.launch_server --model-path spade-rl/SPADE-Qwen3-4B-Games --context-length 262144
+  ```
+- vLLM:
+  ```shell
+  vllm serve spade-rl/SPADE-Qwen3-4B-Games --max-model-len 262144
+  ```
+
+Note: If you encounter out-of-memory (OOM) issues, consider reducing the context
+length to a shorter value, such as 32,768.
+
+## Best practices
+
+We recommend `temperature=0.7`, `top_p=0.8`, `top_k=20`, `min_p=0`, following
+the sampling guidance on the [base model card](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507).
+
+## Related artifacts
+
+- Grounding corpora: [games](https://huggingface.co/datasets/spade-rl/spade-grounding-corpus-games-15k) | [tool use](https://huggingface.co/datasets/spade-rl/spade-grounding-corpus-tooluse-15k)
+- All SPADE models, data and generated environments: [huggingface.co/spade-rl](https://huggingface.co/spade-rl)