args = TrainingArguments( output_dir="/content/drive/MyDrive/Machine Learning/model_llama2_finetuned", num_train_epochs=3, per_device_train_batch_size=1, gradient_accumulation_steps=2, gradient_checkpointing=True, logging_steps=10, save_strategy="epoch", learning_rate=2e-4, bf16=True, tf...
而Llama 2不仅仅是在训练数据量的层面相比上一代Llama 1增加了40%，而且在数据来源和丰富性上也有了...
LLaMA-2-chat 几乎是开源模型中唯一做了 RLHF 的模型。这个东西太贵了，Meta 真是大功一件！根据下图...
采用Colab上的 T4 GPU,由于仅具有有限的 16 GB VRAM,这几乎足够存储 Llama 2–7b 的权重,这意味着无法进行完全微调,我们需要使用像 LoRA 或 QLoRA 这样的参数高效微调技术。 在这里,我们使用 QLoRA 技术以 4 位精度微调模型并优化 VRAM 使用。为此,我们直接使用 Hugging Face 生态系统中的 LLM 库:transformers...
Loading vocab file/app/LinkSoul/Chinese-Llama-2-7b/tokenizer.modelparams:n_vocab:32000n_embd:4096n_mult:256n_head:32n_layer:32Writing vocab...[1/291]Writing tensor tok_embeddings.weight|size32000x4096|typeUnquantizedDataType(name='F32')[2/291]Writing tensor norm.weight|size4096|typeUnquanti...
Check the current disk usage: Before resizing the file system, it is a good practice to check the current disk usage to ensure it reflects the increased boot volume size. You can use thedfcommand for this purpose. Verify that the available space matches your new boot volume size (300GB)....
Batch Size: 1; Measure on 1 socket; PyTorch nightly build0711; Intel® Extensions for PyTorch* llm_feature_branch; Model: Llama 2 7B and Llama 2 13B, Dataset LAMBADA; Token Length: 32/128/1024/2016 (in), 32 (out); Beam Width 4; Precision: BF16 and INT8; Test by Intel on 7/...
Llama 2 Uncensored7B3.8GBollama run llama2-uncensored LLaVA7B4.5GBollama run llava Solar10.7B6.1GBollama run solar Note You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models. ...
You'll notice that the 110M model is equivalent to GPT-1 in size. Alternatively, this is also the smallest model in the GPT-2 series (GPT-2 small), except the max context length is only 1024 instead of 2048. The only notable changes from GPT-1/2 architecture is that Llama uses RoPE...
考虑到RAM被限制为16GB, 8位GGML版本是合适的,因为它只需要9.6GB的内存而原始的非量化16位模型需要约15gb的内存 8位格式也提供了与16位相当的响应质量,而其他更小的量化格式(即4位和5位)是可用的,但它们是以准确性和响应质量为代价的。 构建步骤指导 ...