Text Classification
Transformers
Safetensors
internlm2
text-generation
hallucination-detection
custom_code
Instructions to use opencompass/anah-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use opencompass/anah-20b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="opencompass/anah-20b", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("opencompass/anah-20b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "InternLM2ForCausalLM" | |
| ], | |
| "attn_implementation": "eager", | |
| "auto_map": { | |
| "AutoConfig": "configuration_internlm2.InternLM2Config", | |
| "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM", | |
| "AutoModel": "modeling_internlm2.InternLM2ForCausalLM" | |
| }, | |
| "bias": false, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 6144, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 16384, | |
| "max_position_embeddings": 32768, | |
| "model_type": "internlm2", | |
| "num_attention_heads": 48, | |
| "num_hidden_layers": 48, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 2, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "factor": 2.0, | |
| "type": "dynamic" | |
| }, | |
| "rope_theta": 1000000, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.39.3", | |
| "use_cache": true, | |
| "vocab_size": 92544 | |
| } |