Instructions to use CofeAI/Tele-FLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CofeAI/Tele-FLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CofeAI/Tele-FLM", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CofeAI/Tele-FLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "TeleFLMModel", "TeleFLMForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_teleflm.TeleFLMConfig", | |
| "AutoModel": "modeling_teleflm.TeleFLMModel", | |
| "AutoModelForCausalLM": "modeling_teleflm.TeleFLMForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 8192, | |
| "initializer_range": 0.02, | |
| "input_mult": 1.0, | |
| "intermediate_size": 21824, | |
| "max_position_embeddings": 4096, | |
| "model_type": "TeleFLM", | |
| "mup_scale_factor": 16.0, | |
| "num_attention_heads": 64, | |
| "num_hidden_layers": 64, | |
| "num_key_value_heads": 64, | |
| "output_mult": 0.5, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.40.0", | |
| "use_cache": true, | |
| "use_mup": true, | |
| "vocab_size": 80384 | |
| } | |