Text Generation
Transformers
Safetensors
pinyin_code
causal-lm
trust-remote-code
sentencepiece
custom_code
Instructions to use timorobrecht/full_chinese_gpu3.2-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timorobrecht/full_chinese_gpu3.2-dpo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="timorobrecht/full_chinese_gpu3.2-dpo", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("timorobrecht/full_chinese_gpu3.2-dpo", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use timorobrecht/full_chinese_gpu3.2-dpo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "timorobrecht/full_chinese_gpu3.2-dpo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "timorobrecht/full_chinese_gpu3.2-dpo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/timorobrecht/full_chinese_gpu3.2-dpo
- SGLang
How to use timorobrecht/full_chinese_gpu3.2-dpo 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 "timorobrecht/full_chinese_gpu3.2-dpo" \ --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": "timorobrecht/full_chinese_gpu3.2-dpo", "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 "timorobrecht/full_chinese_gpu3.2-dpo" \ --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": "timorobrecht/full_chinese_gpu3.2-dpo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use timorobrecht/full_chinese_gpu3.2-dpo with Docker Model Runner:
docker model run hf.co/timorobrecht/full_chinese_gpu3.2-dpo
| # βββββββββββββββββββββββββββββββββββββββββββββ | |
| # Chinese BabyLM Evaluation Pipeline Config | |
| # βββββββββββββββββββββββββββββββββββββββββββββ | |
| # Models to evaluate. | |
| # Each entry needs: | |
| # path β HuggingFace repo ID or local directory path | |
| # backend β one of: causal, mlm, mntp, enc_dec_mask, enc_dec_prefix | |
| models: | |
| - path: timorobrecht/full_chinese_gpu3.2-dpo | |
| 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 β zero-shot minimal pairs | |
| zero_shot: | |
| - zhoblimp | |
| - hanzi_structure | |
| - hanzi_pinyin | |
| # Cog Track β fMRI brain encoding | |
| cogbench: | |
| - word_fmri | |
| - fmri | |
| # Fine-tuning Track β 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 | |