Instructions to use Jayfeather1024/rm_30k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jayfeather1024/rm_30k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, LlamaModelForScore tokenizer = AutoTokenizer.from_pretrained("Jayfeather1024/rm_30k") model = LlamaModelForScore.from_pretrained("Jayfeather1024/rm_30k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Jayfeather1024/rm_30k: direct link, hf CLI and curl.
- Browser
- Download file 855 Bytes
-
https://huggingface.co/Jayfeather1024/rm_30k/resolve/main/config.json
- Command line
-
hf download hf://Jayfeather1024/rm_30k/config.json
-
curl -L -o config.json https://huggingface.co/Jayfeather1024/rm_30k/resolve/main/config.json
855 Bytes
| { | |
| "_name_or_path": "output/sft", | |
| "architectures": [ | |
| "LlamaModelForScore" | |
| ], | |
| "bias": false, | |
| "bos_token_id": 1, | |
| "do_normalize": false, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 2048, | |
| "max_sequence_length": 2048, | |
| "mean": [ | |
| 7.4375 | |
| ], | |
| "model_type": "llama", | |
| "momentum": 0.9, | |
| "normalizer_type": "ExponentialMovingAverage", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "pad_token_id": 32000, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "score_dim": 1, | |
| "score_type": "reward", | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.31.0", | |
| "use_cache": true, | |
| "var": [ | |
| 82.5 | |
| ], | |
| "vocab_size": 32001 | |
| } | |