Instructions to use varadsrivastava/lm-playschool-qwen3.5-2b-iter4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use varadsrivastava/lm-playschool-qwen3.5-2b-iter4 with Transformers:

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

pipe = pipeline("text-generation", model="varadsrivastava/lm-playschool-qwen3.5-2b-iter4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("varadsrivastava/lm-playschool-qwen3.5-2b-iter4")
model = AutoModelForCausalLM.from_pretrained("varadsrivastava/lm-playschool-qwen3.5-2b-iter4", 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 varadsrivastava/lm-playschool-qwen3.5-2b-iter4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "varadsrivastava/lm-playschool-qwen3.5-2b-iter4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "varadsrivastava/lm-playschool-qwen3.5-2b-iter4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/varadsrivastava/lm-playschool-qwen3.5-2b-iter4

SGLang

How to use varadsrivastava/lm-playschool-qwen3.5-2b-iter4 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 "varadsrivastava/lm-playschool-qwen3.5-2b-iter4" \
    --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": "varadsrivastava/lm-playschool-qwen3.5-2b-iter4",
		"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 "varadsrivastava/lm-playschool-qwen3.5-2b-iter4" \
        --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": "varadsrivastava/lm-playschool-qwen3.5-2b-iter4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use varadsrivastava/lm-playschool-qwen3.5-2b-iter4 with Docker Model Runner:
```
docker model run hf.co/varadsrivastava/lm-playschool-qwen3.5-2b-iter4
```

R4 — Corrective feedback (DPO)

Part of a five-regime developmental sweep of post-training methods for dialogue-game competence (LM Playschool Challenge 2026, team DAIR).

A second DPO round on the merged R2 model using 201 first-move preference pairs from two rollout passes (greedy and t=0.7): 107 on-policy pairs from instances where the two passes disagreed in outcome (chosen = the model's own success, rejected = its own failure on the identical instance) and 94 hybrid pairs from instances failed in both passes (chosen = a stronger model's success on that instance — a recast). Same hyperparameters as R2.

Effect: 67.39 -> 67.64, the nominal best of the family, though the margin is within run-to-run variability. The on-policy pairs come from the model's competence frontier (instances of variable outcome).

All numbers are clemscore / statscore on the playpen validation split, measured in a single frozen environment (Python 3.11, clemcore pinned via playpen, clembench pinned requirements) with two upstream fixes applied: a division-by-zero guard in the privateshared Game Master and the punkt_tab NLTK resource for the IFEval scorer. Earlier revisions of this card reported numbers from an unpinned environment; see the paper for the environment-sensitivity analysis.

Checkpoint family (LM Playschool challenge, team DAIR)

Regime	Repo	clem	stat
R1 imitation (SFT)	`lm-playschool-qwen3.5-2b-sft`	55.61	43.87
R2 outcome contrast (DPO)	`lm-playschool-qwen3.5-2b-sft-dpo`	67.39	44.72
R3 self-imitation (SFT)	`lm-playschool-qwen3.5-2b-iter3`	61.06	44.01
R4 corrective feedback (DPO)	`lm-playschool-qwen3.5-2b-iter4`	67.64	44.31
R5 GRPO (control)	`lm-playschool-qwen3.5-2b-grpo-base-s42`	62.43	44.19
R5 GRPO + RND	`lm-playschool-qwen3.5-2b-grpo-rnd-s42`	67.44	43.53

Base model: Qwen3.5-2B (13.63 / 44.22 in the same environment). Paper: Raising a Small Language Model: From Imitation to Curiosity in Dialogue Games (LM Playschool Challenge 2026).