Text Classification
Transformers
PyTorch
English
Chinese
internlm2
feature-extraction
Reward
RL
RFT
Reward Model
custom_code
Instructions to use internlm/POLAR-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/POLAR-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="internlm/POLAR-7B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/POLAR-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/cpfs01/shared/alillm_hs/zouyicheng/rm_pretrain/rm/RM_PT_internlm2_5_7b_DATA_510m_single_mix_Node_57_LR_1_45e_5_STEP_223684_hf", | |
| "architectures": [ | |
| "InternLM2ForRewardModel" | |
| ], | |
| "attn_implementation": "flash_attention_2", | |
| "auto_map": { | |
| "AutoConfig": "configuration_internlm2.InternLM2Config", | |
| "AutoModel": "modeling_internlm2.InternLM2ForRewardModel" | |
| }, | |
| "bias": false, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 14336, | |
| "max_position_embeddings": 262144, | |
| "model_type": "internlm2", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 2, | |
| "pretraining_tp": 1, | |
| "reward_token_id": 92527, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "factor": 2.0, | |
| "type": "dynamic" | |
| }, | |
| "rope_theta": 50000000, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.49.0", | |
| "use_cache": true, | |
| "vocab_size": 92544 | |
| } |