Instructions to use VERSIL91/e9b6697f-b0d4-4434-8cf6-757d3202d201 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use VERSIL91/e9b6697f-b0d4-4434-8cf6-757d3202d201 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "VERSIL91/e9b6697f-b0d4-4434-8cf6-757d3202d201") - Notebooks
- Google Colab
- Kaggle
File size: 802 Bytes
07ec1fb | 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 | {
"_attn_implementation_autoset": true,
"_name_or_path": "trl-internal-testing/tiny-random-LlamaForCausalLM",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 0,
"eos_token_id": 2,
"head_dim": 4,
"hidden_act": "silu",
"hidden_size": 16,
"initializer_range": 0.02,
"intermediate_size": 64,
"max_position_embeddings": 2048,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"num_key_value_heads": 4,
"pad_token_id": -1,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000.0,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.46.0",
"use_cache": false,
"vocab_size": 32000
}
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