Instructions to use NeTSlab/gpt2_parfind_nl_zh_equal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeTSlab/gpt2_parfind_nl_zh_equal with Transformers:

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

pipe = pipeline("text-generation", model="NeTSlab/gpt2_parfind_nl_zh_equal")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeTSlab/gpt2_parfind_nl_zh_equal")
model = AutoModelForCausalLM.from_pretrained("NeTSlab/gpt2_parfind_nl_zh_equal")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NeTSlab/gpt2_parfind_nl_zh_equal with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeTSlab/gpt2_parfind_nl_zh_equal"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeTSlab/gpt2_parfind_nl_zh_equal",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NeTSlab/gpt2_parfind_nl_zh_equal

SGLang

How to use NeTSlab/gpt2_parfind_nl_zh_equal 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 "NeTSlab/gpt2_parfind_nl_zh_equal" \
    --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": "NeTSlab/gpt2_parfind_nl_zh_equal",
		"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 "NeTSlab/gpt2_parfind_nl_zh_equal" \
        --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": "NeTSlab/gpt2_parfind_nl_zh_equal",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NeTSlab/gpt2_parfind_nl_zh_equal with Docker Model Runner:
```
docker model run hf.co/NeTSlab/gpt2_parfind_nl_zh_equal
```

gpt2_parfind_nl_zh_equal / README.md

achille-fusco

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	---
	language:
	- nl
	- zh
	tags:
	- causal-lm
	- language-model
	- babylm
	- babylm-2026
	- multilingual
	- gpt2
	- paradigmfinder
	pipeline_tag: text-generation
	library_name: transformers
	---

	# gpt2_parfind_nl_zh_equal

	Bilingual GPT-2 model packaged for the BabyLM 2026 multilingual evaluation track.

	Source experiment: `/home/achille.fusco/pr_baby_lm/BabyLM_2026_ENH/04-experiments/model_gpt2_ParFindFast_nld_zho_BD_budget16k_zhchildes_v1.0`

	Hugging Face target repo: `NeTSlab/gpt2_parfind_nl_zh_equal`

	Current status on July 6, 2026: `resume_needed`

	Notes:
	- `main` is intended to point to the final 1000M checkpoint (`epoch_9`).
	- The custom ParadigmFinder tokenizer is bundled with the model files.
	- Loading from HF requires `trust_remote_code=True`.