Instructions to use littlelearner/littlelearner-1.3b-grpo-math-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use littlelearner/littlelearner-1.3b-grpo-math-expert with Transformers:

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

pipe = pipeline("text-generation", model="littlelearner/littlelearner-1.3b-grpo-math-expert")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("littlelearner/littlelearner-1.3b-grpo-math-expert")
model = AutoModelForCausalLM.from_pretrained("littlelearner/littlelearner-1.3b-grpo-math-expert", 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 littlelearner/littlelearner-1.3b-grpo-math-expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "littlelearner/littlelearner-1.3b-grpo-math-expert"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "littlelearner/littlelearner-1.3b-grpo-math-expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/littlelearner/littlelearner-1.3b-grpo-math-expert

SGLang

How to use littlelearner/littlelearner-1.3b-grpo-math-expert 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 "littlelearner/littlelearner-1.3b-grpo-math-expert" \
    --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": "littlelearner/littlelearner-1.3b-grpo-math-expert",
		"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 "littlelearner/littlelearner-1.3b-grpo-math-expert" \
        --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": "littlelearner/littlelearner-1.3b-grpo-math-expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use littlelearner/littlelearner-1.3b-grpo-math-expert with Docker Model Runner:
```
docker model run hf.co/littlelearner/littlelearner-1.3b-grpo-math-expert
```

littlelearner-1.3b-grpo-math-expert

1.36B K-5-bounded chat model post-trained with GRPO on top of SFT.

Part of the LittleLearner scale-up study (pedagogically-controlled knowledge exposure): Qwen3 dense LMs trained on a corpus filtered to U.S. K-5 material (bounded) vs an unfiltered FineWeb-Edu corpus (unbounded), to measure what an interpretable knowledge boundary costs and grants.

Note: This checkpoint was post-trained with GRPO on mathematical reasoning tasks to probe achievable performance on MathCAMPS. As a result, its behavior is specialized toward mathematical reasoning and may not preserve general-purpose chat capabilities; responses may also exhibit a tendency toward math-oriented reasoning or output.

Model

Architecture: Qwen3 dense (Qwen3ForCausalLM).
Size: 1.358B params, hidden 2048, 26 layers, 16 query / 8 KV heads, FFN 5632. Context: 4096.
Tokenizer: custom 64k byte-level BPE with per-digit splitting (ChatML special tokens).
Pretraining: 88B tokens on K-5 LittleCurriculum (FineWeb-Edu filtered to U.S. grades K-5). WSD schedule, sharded Muon, MXFP8, Megatron-Core on 8xB200.
SFT: supervised fine-tuned on K-5 chat data (lr 1e-5, 3 epochs) from a cooloff-SFT-primed base.
RL (GRPO): segmented policy re-banding on a strictly K-5 verifiable-answer pool (Gemini-generated K-5 word problems + K-5-filtered GSM8K): 4 segments at rollout/training temperature 1.0, then 2 more at temperature 2.0 (the bounded-corpus, 1.3B-scale unlock temperature).

Evaluation

MathCAMPS:

K-5 pass@64 61.2 / pass@1 40.1
beyond-K-5 pass@64 17.1 / pass@1 6.5

Usage

# transformers (chat)
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "manueldeprada/littlelearner-1.3b-bounded-grpo"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="cuda")
msgs = [{"role": "user", "content": "Liam has 3 apples and buys 4 more. How many apples does he have?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

# vLLM
from vllm import LLM
repo = "manueldeprada/littlelearner-1.3b-bounded-grpo"
llm = LLM(repo)
msgs = [{"role": "user", "content": "Liam has 3 apples and buys 4 more. How many apples does he have?"}]
print(llm.chat(msgs)[0].outputs[0].text)

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Safetensors

Model size

1B params

Tensor type

BF16