Instructions to use chnln/Qwen3.5-2B-playpen-playornotplay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chnln/Qwen3.5-2B-playpen-playornotplay with Transformers:

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

pipe = pipeline("text-generation", model="chnln/Qwen3.5-2B-playpen-playornotplay")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chnln/Qwen3.5-2B-playpen-playornotplay")
model = AutoModelForCausalLM.from_pretrained("chnln/Qwen3.5-2B-playpen-playornotplay", device_map="auto")
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 chnln/Qwen3.5-2B-playpen-playornotplay with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chnln/Qwen3.5-2B-playpen-playornotplay"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chnln/Qwen3.5-2B-playpen-playornotplay",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chnln/Qwen3.5-2B-playpen-playornotplay

SGLang

How to use chnln/Qwen3.5-2B-playpen-playornotplay 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 "chnln/Qwen3.5-2B-playpen-playornotplay" \
    --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": "chnln/Qwen3.5-2B-playpen-playornotplay",
		"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 "chnln/Qwen3.5-2B-playpen-playornotplay" \
        --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": "chnln/Qwen3.5-2B-playpen-playornotplay",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chnln/Qwen3.5-2B-playpen-playornotplay with Docker Model Runner:
```
docker model run hf.co/chnln/Qwen3.5-2B-playpen-playornotplay
```

Qwen3.5-2B Playpen Agent — team `playornotplay`

Submission for the LM Playschool Challenge / Playpen shared task (EMNLP 2026 Workshop) by team playornotplay.

This repository contains full merged weights: a fine-tune of Qwen/Qwen3.5-2B for interactive dialogue-game play, trained with parameter-efficient methods on colab-potsdam/playpen-data (train split only). The scaled LoRA adapter used to produce these merged weights is preserved under lora-adapter/ for provenance.

Weights are stored in fp32 deliberately. The fine-tune delta is small relative to bf16's resolution at these weight magnitudes; materializing the merge in bf16 measurably degrades static-benchmark behavior (statscore 45.06 → 43.15 in our tests). Load with the default torch_dtype="auto" and do not downcast the weights.

Public validation results (dataset revision 557d8caf):

Model	Clemscore	Statscore	Δ Clem	Δ Stat
Official `Qwen/Qwen3.5-2B` baseline	13.05	44.02	—	—
This model	50.43	44.90	+37.38	+0.88

Clemscore is a paired same-host mean of two runs (50.82 / 50.03). The model is positive on both public axes versus the official base, and its clemscore exceeds the official 9B (41.92) and 4B (39.70) baselines. Independent verification of this repository's merged weights, loaded exactly as the public evaluator does (plain transformers, torch_dtype="auto"): clem 48.70 / stat 45.06 — both within suite variance of the headline runs, statscore still positive vs the official base. (The adapter path under lora-adapter/ verifies at clem 51.22 / stat 45.36.)

How to load

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "chnln/Qwen3.5-2B-playpen-playornotplay",
    trust_remote_code=True,  # weights are fp32 on purpose — do not downcast (see above)
)
tokenizer = AutoTokenizer.from_pretrained("chnln/Qwen3.5-2B-playpen-playornotplay")

All evaluations used the bundled chat template with enable_thinking=False.

Evaluation protocol

Interactive suite: playpen eval <model> --suite clem; static suite: playpen eval <model> --suite static
Public validation basis: colab-potsdam/playpen-data revision 557d8cafd1bc4557bc22803a6d4502ef53fb664c
model.safetensors merged from base + adapter; adapter SHA-256: fbcfa37fd1d30e45b85ae4b8e44b59bb1d1792b187b280f487629f12026c0d2e

Training

Trained by team playornotplay on the Playpen interaction/instance data (train split; no validation or private test data used). Summary training card:

Methodology: multi-stage LoRA fine-tuning (r=16, α=32, all-linear) — broad SFT on successful game episodes, then game-targeted SFT, then preference optimization (DPO) stages targeting diagnosed decision-level failure modes, followed by a training-free LoRA-delta scaling step (s=0.85) that recovers static-benchmark performance while preserving interactive gains.
Data usage breakdown: all data derives from the official colab-potsdam/playpen-data train split. SFT stages: success-only interaction transcripts (per-game balanced, ≤700 episodes/game; a weak-game subset filtered by source model). DPO stages: self-constructed preference pairs — ~3.8k synthetic turn-local pairs targeting mechanical failure modes, plus on-policy pairs collected by branching the current model during game play. No external datasets, no validation/test data.
Key hyperparameters: LoRA r=16 / α=32 / dropout 0.05; SFT LR 5e-5, max_len 2048; DPO LR 5e-6, β=0.1–0.2, max_len 1024; final delta scale s=0.85.
Compute budget: ~5 A100-hours total for the shipped lineage (single GPU, measured per job from scheduler logs).
Design decisions for reproducibility: dataset revision pinned and recorded per run (557d8caf basis); evaluation-harness commit pinned; per-run metadata (code SHA, environment, dataset revision); artifact SHA-256 checksums verified train-host → upload → Hub; merged weights stored fp32 (see warning above) and re-verified end-to-end from a clean Hub pull.

The full training card — per-stage configurations, data-construction procedures, per-stage results, and the complete failure-mode analysis — will be released with the workshop paper.

Limitations

Specialized for Playpen dialogue games; not intended as a general-purpose chat model.
Public clem validation is small (~~42 instances) and noise-dominated (~~3 pts run-to-run); statscore (430 instances) is more stable.
Scores depend on the dataset revision used by the public evaluator.

Downloads last month: 48

Safetensors

Model size

2B params

Tensor type

F32

Model tree for chnln/Qwen3.5-2B-playpen-playornotplay

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

(348)

this model

Dataset used to train chnln/Qwen3.5-2B-playpen-playornotplay

Evaluation results

clemscore on playpen-data (public validation, rev 557d8caf)
validation set self-reported

50.430
statscore on playpen-data (public validation, rev 557d8caf)
validation set self-reported

44.900

Qwen3.5-2B Playpen Agent — team playornotplay