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

Libraries

How to use NeTSlab/hawk_parfind_en_nl_zh_equal with Transformers:

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

pipe = pipeline("text-generation", model="NeTSlab/hawk_parfind_en_nl_zh_equal", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("NeTSlab/hawk_parfind_en_nl_zh_equal", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NeTSlab/hawk_parfind_en_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/hawk_parfind_en_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/hawk_parfind_en_nl_zh_equal",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NeTSlab/hawk_parfind_en_nl_zh_equal

SGLang

How to use NeTSlab/hawk_parfind_en_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/hawk_parfind_en_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/hawk_parfind_en_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/hawk_parfind_en_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/hawk_parfind_en_nl_zh_equal",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

hawk_parfind_en_nl_zh_equal

Multilingual Hawk causal language model for the BabyLM 2026 multilingual setting, paired with its own custom ParadigmFinder tokenizer trained on English, Dutch, and Chinese.

This package is staged for direct Hugging Face upload. Because both the model and tokenizer rely on custom runtime code, loading requires trust_remote_code=True.

Model

architecture: HawkForCausalLM
model type: hawk_rglru
max positions: 1024
exported model vocab size: 44091
default main checkpoint: chck_600M

Tokenizer

class: EnhancedParadigmTokenizerWrapper
paradigm family: ParadigmFinder
multilingual budgeting: soft
budget per language: 16384
languages: eng, nld, zho
space-free lexicon language: zho

This tokenizer includes the custom runtime files needed for HF loading:

tokenizer.py
boundary_discovery.py
preprocessing.py
paradigm_utils.py
paradigms.json
multilingual_meta.json
modeling_hawk.py

Revisions

The following training checkpoints are prepared as self-contained HF revisions:

chck_1M
chck_2M
chck_3M
chck_4M
chck_5M
chck_6M
chck_7M
chck_8M
chck_9M
chck_10M
chck_20M
chck_30M
chck_40M
chck_50M
chck_60M
chck_70M
chck_80M
chck_90M
chck_100M
chck_200M
chck_300M
chck_400M
chck_500M
chck_600M

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "NeTSlab/hawk_parfind_en_nl_zh_equal"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)

prompt = "The child looked at"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=30)
print(tokenizer.decode(output[0]))

If your Hugging Face cache is not writable, set:

export HF_HOME=/tmp/hf_cache

Notes

This is a BabyLM-scale research model, not a production general-purpose LM.
The tokenizer and model both use custom remote code.
The package preserves each checkpoint's own tokenizer.py and modeling_hawk.py so revision uploads stay aligned with the trained artifacts.

Downloads last month: -

Safetensors

Model size

0.1B params

Tensor type

F32