Text Generation
Transformers
ONNX
Safetensors
Chinese
t5
text2text-generation
text-generation-inference
custom_code
Instructions to use charent/ChatLM-mini-Chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use charent/ChatLM-mini-Chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="charent/ChatLM-mini-Chinese", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("charent/ChatLM-mini-Chinese", trust_remote_code=True) model = AutoModelForSeq2SeqLM.from_pretrained("charent/ChatLM-mini-Chinese", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use charent/ChatLM-mini-Chinese with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "charent/ChatLM-mini-Chinese" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "charent/ChatLM-mini-Chinese", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/charent/ChatLM-mini-Chinese
- SGLang
How to use charent/ChatLM-mini-Chinese 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 "charent/ChatLM-mini-Chinese" \ --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": "charent/ChatLM-mini-Chinese", "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 "charent/ChatLM-mini-Chinese" \ --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": "charent/ChatLM-mini-Chinese", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use charent/ChatLM-mini-Chinese with Docker Model Runner:
docker model run hf.co/charent/ChatLM-mini-Chinese
| { | |
| "_name_or_path": "./model_save/dpo/", | |
| "architectures": [ | |
| "TextToTextModel" | |
| ], | |
| "auto_map": { | |
| "AutoModelForSeq2SeqLM": "modeling_chat_model.TextToTextModel" | |
| }, | |
| "classifier_dropout": 0.0, | |
| "d_ff": 3072, | |
| "d_kv": 64, | |
| "d_model": 768, | |
| "decoder_start_token_id": 0, | |
| "dense_act_fn": "relu", | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 1, | |
| "feed_forward_proj": "relu", | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "is_gated_act": false, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "t5", | |
| "num_decoder_layers": 10, | |
| "num_heads": 12, | |
| "num_layers": 10, | |
| "pad_token_id": 0, | |
| "relative_attention_max_distance": 128, | |
| "relative_attention_num_buckets": 32, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.36.2", | |
| "use_cache": true, | |
| "vocab_size": 29298 | |
| } | |