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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 940 Bytes
8b28dae cbaeefd 79e7844 cbaeefd 8b28dae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"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
}
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