update scripts

README.md

@@ -537,9 +537,10 @@ docker exec -it llamafactory bash
 
 <details><summary>Details about volume</summary>
 
-- hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
+- `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
-- data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
+- `ms_cache`: Similar to Hugging Face cache but for ModelScope users.
-- output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
+- `data`: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
+- `output`: Set export dir to this location so that the merged result can be accessed directly on the host machine.
 
 </details>
 

README_zh.md

@@ -537,9 +537,10 @@ docker exec -it llamafactory bash
 
 <details><summary>数据卷详情</summary>
 
-- hf_cache：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
+- `hf_cache`：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
-- data：宿主机中存放数据集的文件夹路径。
+- `ms_cache`：类似 Hugging Face 缓存文件夹，为 ModelScope 用户提供。
-- output：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
+- `data`：宿主机中存放数据集的文件夹路径。
+- `output`：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
 
 </details>
 

examples/extras/pissa/init.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+
+python scripts/pissa_init.py \
+    --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \
+    --output_dir models/llama3-8b-pissa

scripts/cal_lr.py

@@ -43,7 +43,7 @@ def calculate_lr(
     dataset_dir: str = "data",
     template: str = "default",
     cutoff_len: int = 1024,  # i.e. maximum input length during training
-    is_mistral: bool = False,  # mistral model uses a smaller learning rate,
+    is_mistral_or_gemma: bool = False,  # mistral and gemma models opt for a smaller learning rate,
     packing: bool = False,
 ):
     r"""
@@ -84,7 +84,7 @@ def calculate_lr(
     valid_ratio = valid_tokens / total_tokens
     batch_valid_len = batch_max_len * valid_ratio
     lr = BASE_LR * math.sqrt(batch_valid_len / BASE_BS)  # lr ~ sqrt(batch_size)
-    lr = lr / 6.0 if is_mistral else lr
+    lr = lr / 6.0 if is_mistral_or_gemma else lr
     print(
         "Optimal learning rate is {:.2e} for valid ratio% {:.2f} and effective batch size {:.2f}".format(
             lr, valid_ratio * 100, batch_valid_len

scripts/llama_pro.py

@@ -19,7 +19,7 @@
 import json
 import os
 from collections import OrderedDict
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import fire
 import torch
@@ -47,8 +47,8 @@ def block_expansion(
     model_name_or_path: str,
     output_dir: str,
     num_expand: int,
-    shard_size: Optional[str] = "2GB",
+    shard_size: str = "2GB",
-    save_safetensors: Optional[bool] = False,
+    save_safetensors: bool = True,
 ):
     r"""
     Performs block expansion for LLaMA, Mistral, Qwen1.5 or Yi models.

scripts/llamafy_baichuan2.py

@@ -16,7 +16,7 @@
 import json
 import os
 from collections import OrderedDict
-from typing import Any, Dict, Optional
+from typing import Any, Dict
 
 import fire
 import torch
@@ -86,7 +86,10 @@ def save_config(input_dir: str, output_dir: str):
 
 
 def llamafy_baichuan2(
-    input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
+    input_dir: str,
+    output_dir: str,
+    shard_size: str = "2GB",
+    save_safetensors: bool = True,
 ):
     r"""
     Converts the Baichuan2-7B model in the same format as LLaMA2-7B.

scripts/llamafy_qwen.py

@@ -16,7 +16,7 @@
 import json
 import os
 from collections import OrderedDict
-from typing import Any, Dict, Optional
+from typing import Any, Dict
 
 import fire
 import torch
@@ -139,7 +139,10 @@ def save_config(input_dir: str, output_dir: str, torch_dtype: str):
 
 
 def llamafy_qwen(
-    input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
+    input_dir: str,
+    output_dir: str,
+    shard_size: str = "2GB",
+    save_safetensors: bool = False,
 ):
     r"""
     Converts the Qwen models in the same format as LLaMA2.

scripts/pissa_init.py

@@ -31,7 +31,7 @@ if TYPE_CHECKING:
 def quantize_pissa(
     model_name_or_path: str,
     output_dir: str,
-    pissa_iter: int = 4,
+    pissa_iter: int = 16,
     lora_alpha: int = None,
     lora_rank: int = 16,
     lora_dropout: float = 0,