Instructions to use lancehar/last_linear_seed5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lancehar/last_linear_seed5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lancehar/last_linear_seed5", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("lancehar/last_linear_seed5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "tlc4418/pythia_70m_sft", | |
| "architectures": [ | |
| "GPTNeoXRewardModel" | |
| ], | |
| "bos_token_id": 0, | |
| "classifier_dropout": 0.1, | |
| "eos_token_id": 0, | |
| "hidden_act": "gelu", | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 2048, | |
| "mean": 2.484375, | |
| "model_type": "gpt_neox_reward_model", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 6, | |
| "pooling": "last", | |
| "reward_head_activation": "gelu", | |
| "reward_head_dropout": 0.0, | |
| "reward_head_hidden_dim": null, | |
| "reward_head_type": "linear", | |
| "rotary_emb_base": 10000, | |
| "rotary_pct": 0.25, | |
| "std": 1.53125, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.29.2", | |
| "use_cache": true, | |
| "use_parallel_residual": true, | |
| "vocab_size": 50288, | |
| "auto_map": { | |
| "AutoConfig": "gpt_neox_reward_model.GPTNeoXRewardModelConfig", | |
| "AutoModelForSequenceClassification": "gpt_neox_reward_model.GPTNeoXRewardModel" | |
| } | |
| } | |