Instructions to use timorobrecht/full_chinese_gpu3.2-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

full_chinese_gpu3.2-dpo / config.json

timorobrecht

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	{
	"architectures": [
	"PinyinCodeForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_pinyin_code.PinyinCodeConfig",
	"AutoModel": "modeling_pinyin_code.PinyinCodeModel",
	"AutoModelForCausalLM": "modeling_pinyin_code.PinyinCodeForCausalLM",
	"AutoTokenizer": [
	"tokenization_pinyin_code.EncodedMandarinTokenizer",
	null
	]
	},
	"block_size": 512,
	"bos_token_id": 2,
	"dropout": 0.1,
	"dtype": "float32",
	"eos_token_id": 3,
	"evaluation_backend": "causal",
	"hidden_size": 512,
	"is_decoder": true,
	"max_position_embeddings": 512,
	"model_type": "pinyin_code",
	"n_embd": 512,
	"n_head": 8,
	"n_layer": 8,
	"num_attention_heads": 8,
	"num_hidden_layers": 8,
	"pad_token_id": 0,
	"patch_pathlib_utf8_open": true,
	"transformers_version": "5.10.2",
	"unk_token_id": 1,
	"use_cache": false,
	"vocab_size": 16000
	}