Instructions to use Recor2d/GPT2-ChineseDevBench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Recor2d/GPT2-ChineseDevBench with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Recor2d/GPT2-ChineseDevBench")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Recor2d/GPT2-ChineseDevBench") model = AutoModelForCausalLM.from_pretrained("Recor2d/GPT2-ChineseDevBench", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Recor2d/GPT2-ChineseDevBench with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Recor2d/GPT2-ChineseDevBench" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Recor2d/GPT2-ChineseDevBench", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Recor2d/GPT2-ChineseDevBench
- SGLang
How to use Recor2d/GPT2-ChineseDevBench 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 "Recor2d/GPT2-ChineseDevBench" \ --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": "Recor2d/GPT2-ChineseDevBench", "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 "Recor2d/GPT2-ChineseDevBench" \ --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": "Recor2d/GPT2-ChineseDevBench", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Recor2d/GPT2-ChineseDevBench with Docker Model Runner:
docker model run hf.co/Recor2d/GPT2-ChineseDevBench
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# Chinese BabyLM Evaluation Pipeline Config
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# Models to evaluate.
# Each entry needs:
# path โ HuggingFace repo ID or local directory path
# backend โ one of: causal, mlm, mntp, enc_dec_mask, enc_dec_prefix
models:
- path: /home/haihu2/scratch/models/Qwen3-0.6B
backend: causal
# - path: /path/to/local/model
# backend: mlm
# Tasks to run. Comment out any group or individual task to skip it.
tasks:
# NLU Track โ zero-shot minimal pairs
zero_shot:
- zhoblimp
- hanzi_structure
- hanzi_pinyin
# Cog Track โ fMRI brain encoding
cogbench:
- word_fmri
- fmri
# Fine-tuning Track โ CLUE tasks
finetune:
- afqmc
- ocnli
- tnews
- cluewsc2020
# Directories
eval_dir: evaluation_data # where prepare_chinese_data.py puts data
results_dir: results # where eval results are written
# Save items containing UNK tokens for hanzi track tasks
save_item_with_unk: true
# Fine-tuning hyperparameters
# Global defaults are applied first; per-task overrides are merged on top.
finetune_hparams:
lr: 3.0e-5
batch_size: 32
max_epochs: 10
sequence_length: 128
seed: 42
task_overrides:
afqmc:
lr: 3.0e-5
batch_size: 32
max_epochs: 10
ocnli:
lr: 3.0e-5
batch_size: 32
max_epochs: 10
tnews:
lr: 3.0e-5
batch_size: 32
max_epochs: 10
cluewsc2020:
lr: 3.0e-5
batch_size: 32
max_epochs: 30
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