Instructions to use ypwhere/LQK-BabyLM-Strict with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ypwhere/LQK-BabyLM-Strict with Transformers:

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

pipe = pipeline("text-generation", model="ypwhere/LQK-BabyLM-Strict", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ypwhere/LQK-BabyLM-Strict", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ypwhere/LQK-BabyLM-Strict with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ypwhere/LQK-BabyLM-Strict"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ypwhere/LQK-BabyLM-Strict",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ypwhere/LQK-BabyLM-Strict

SGLang

How to use ypwhere/LQK-BabyLM-Strict 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 "ypwhere/LQK-BabyLM-Strict" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ypwhere/LQK-BabyLM-Strict",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ypwhere/LQK-BabyLM-Strict" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ypwhere/LQK-BabyLM-Strict",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ypwhere/LQK-BabyLM-Strict with Docker Model Runner:
```
docker model run hf.co/ypwhere/LQK-BabyLM-Strict
```

LQK — BabyLM 2026 Strict

LQK is a 116M-parameter causal model built on a Llama-1-style decoder with Qwen3-Next-style gated attention and Kimi Attention Residuals (AttnRes). This seed-1337 model was trained for 10 epochs on the official BabyLM 2026 Strict data with its exact 16k byte-level BPE tokenizer.

Load with AutoModelForCausalLM.from_pretrained("ypwhere/LQK-BabyLM-Strict", trust_remote_code=True). The repository provides main plus the 28 required chck_*M checkpoint revisions. Training-data terms remain governed by the BabyLM release; the included nanoGPT-derived model code is provided for research reproducibility.

Downloads last month: 19

Safetensors

Model size

0.1B params

Tensor type

F16

ypwhere
/

LQK-BabyLM-Strict

LQK — BabyLM 2026 Strict

Dataset used to train ypwhere/LQK-BabyLM-Strict