◆ get Embedding def get_embeddings(result, llm_tokenizer, model, args): fw = open(args.output, 'w', encoding='utf-8') for qa in result: q = qa[0] a = qa[1] # 对输出文本进行 tokenize 和编码 tokens = llm_tokenizer.encode_plus(a, add_special_tokens=True, padding='max_length'...
model, tokenizer = get_llama2_model_tokenizer(model_dir) else: raise ValueError(f'model_type: {args.model_type}') # if args.gradient_checkpoint: # baichuan-13b does not implement the `get_input_embeddings` function if args.model_type == 'baichuan-13b': def get_input_embeddings(self): ...
embedding_size=model.get_input_embeddings().weight.shape[0] 获取embedding_size = 65000,接下来调用do_train函数部分 iftraining_args.do_train:ifdata_args.streaming:...else:column_names=list(raw_datasets["train"].features)...text_column_name="text"if"text"incolumn_nameselsecolumn_names[0] 这里...
旋转嵌入编码（Rotary Positional Embeddings，RoPE）：RoPE可以兼顾相对位置和绝对位置的信息以提高模型的泛化能力 2.2 Llama 2训练亮点 除了上文提到的训练数据的增加，Llama 2在训练过程方面也有两个亮点值得我们关注。第一，上下文长度的扩大提升了模型的理解能力；第二，分组查询注意力机制提高了模型的推理速度。上下...
python MinimumExample/Example_ONNX_LlamaV2.py --onnx_file 7B_FT_float16/ONNX/LlamaV2_7B_FT_float16.onnx --embedding_file 7B_FT_float16/embeddings.pth --tokenizer_path tokenizer.model --prompt "What is the lightest element?" 1. ...
Don’t forget to resize the token embeddings of Llama 2 after you added the pad token to its vocabulary. I explained how to do it in this article: Fine-tune Llama 2 on Your Computer with QLoRa and TRL On Guanaco and with the correct padding kaitchup.substack.comConclusion...
2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 自定义 Prompts 基于使用的Index,llamaIndex 会使用默认的 prompt 模板进行构建 Index(插入 or 创建), 也可以自定义link。 自定义 Embeddings
Borrowing from the GPT-Neo-X project, LLaMA features rotary positional embeddings (RoPE) at each layer of the network. As reported in the appendix of the LLaMA 2 paper, the primary architectural differences from the original model are increased context length and grouped-query attention (GQA). ...
{'dim': 4096, 'multiple_of': 256, 'n_heads': 32, 'n_layers': 32, 'norm_eps': 1e-06, 'vocab_size': 32000}n_parts = 1Processing part 0Processing variable: tok_embeddings.weight with shape: torch.Size([32000, 4096]) and type: torch.float16Processing variable: norm.weight with ...
self.word_embeddings = tensor_parallel.VocabParallelEmbedding( File "test/ascendspeed/core/tensor_parallel/layers.py", line 220, ininit initialize_affine_weight_gpu(self.weight, init_method, partition_dim=0, stride=1) File "test/ascendspeed/core/tensor_parallel/layers.py", line 112, ininitiali...