Views
No views yet
1from transformers import LlamaTokenizer, LlamaForCausalLM
2
3model_name = 'TheBloke/Llama-2-13B-fp16'
4
5model = LlamaForCausalLM.from_pretrained(model_name).half()
6tokenizer = LlamaTokenizer.from_pretrained(model_name)
7
8# Add padding token
9tokenizer.add_tokens(['<PAD>'])
10tokenizer.pad_token = '<PAD>'
11
12# Resizing the model
13model.resize_token_embeddings(len(tokenizer))
14
15padded_model_name = 'Llama-2-13B-fp16-padded'
16
17# Save
18tokenizer.save_pretrained(padded_model_name)
19model.save_pretrained(padded_model_name)
20| Training parameteres | |
|---|---|
| LoRA scale | 2 |
| Epochs | 0.75 |
| Learning Rate | 2e-5 |
| Warmup Steps | 100 |
| Loss | 1.07 |