Instructions to use TCMLLM/Lingdan-13B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TCMLLM/Lingdan-13B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TCMLLM/Lingdan-13B-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TCMLLM/Lingdan-13B-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from TCMLLM/Lingdan-13B-Base: direct link, hf CLI and curl.
- Browser
- Download file 866 Bytes
-
https://huggingface.co/TCMLLM/Lingdan-13B-Base/resolve/main/config.json
- Command line
-
hf download hf://TCMLLM/Lingdan-13B-Base/config.json
-
curl -L -o config.json https://huggingface.co/TCMLLM/Lingdan-13B-Base/resolve/main/config.json
866 Bytes
| { | |
| "_from_model_config": true, | |
| "_name_or_path": "/fs1/home/tasly/model/Baichuan2-13B-Base", | |
| "architectures": [ | |
| "BaichuanForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_baichuan.BaichuanConfig", | |
| "AutoModel": "modeling_baichuan.BaichuanForCausalLM", | |
| "AutoModelForCausalLM": "modeling_baichuan.BaichuanForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 5120, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 13696, | |
| "model_max_length": 4096, | |
| "model_type": "baichuan", | |
| "num_attention_heads": 40, | |
| "num_hidden_layers": 40, | |
| "pad_token_id": 0, | |
| "rms_norm_eps": 1e-06, | |
| "tie_word_embeddings": false, | |
| "tokenizer_class": "BaichuanTokenizer", | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.33.3", | |
| "use_cache": true, | |
| "vocab_size": 125696, | |
| "z_loss_weight": 0 | |
| } | |