[assets] update wechat (#8962)

README.md

@@ -10,7 +10,6 @@
 
 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
 [![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
-[![GitCode](https://gitcode.com/zhengyaowei/LLaMA-Factory/star/badge.svg)](https://gitcode.com/zhengyaowei/LLaMA-Factory)
 
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
 [![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
@@ -101,7 +100,7 @@ Choose your path:
 
 ## Blogs
 
-- [Fine-tune GPT-OSS for Role-Playing using LLaMA-Factory](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptoss/?utm_source=LLaMA-Factory) (Chinese)
+- [Fine-tune GPT-OSS for Role-Playing using LLaMA-Factory](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptroleplay/?utm_source=LLaMA-Factory) (Chinese)
 - [Fine-tune Llama3.1-70B for Medical Diagnosis using LLaMA-Factory](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory) (Chinese)
 - [A One-Stop Code-Free Model Reinforcement Learning and Deployment Platform based on LLaMA-Factory and EasyR1](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/) (Chinese)
 - [How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/) (English)
@@ -266,7 +265,7 @@ Choose your path:
 | [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon              |
 | [Falcon-H1](https://huggingface.co/tiiuae)                        | 0.5B/1.5B/3B/7B/34B              | falcon_h1           |
 | [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma/gemma2        |
-| [Gemma 3/Gemma 3n](https://huggingface.co/google)                 | 1B/4B/6B/8B/12B/27B              | gemma3/gemma3n      |
+| [Gemma 3/Gemma 3n](https://huggingface.co/google)                 | 270M/1B/4B/6B/8B/12B/27B         | gemma3/gemma3n      |
 | [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/zai-org)         | 9B/32B                           | glm4/glmz1          |
 | [GLM-4.1V](https://huggingface.co/zai-org)                        | 9B                               | glm4v               |
 | [GLM-4.5/GLM-4.5V](https://huggingface.co/zai-org)*               | 106B/355B                        | glm4_moe/glm4v_moe  |

README_zh.md

@@ -10,7 +10,6 @@
 
 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
 [![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
-[![GitCode](https://gitcode.com/zhengyaowei/LLaMA-Factory/star/badge.svg)](https://gitcode.com/zhengyaowei/LLaMA-Factory)
 
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
 [![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
@@ -103,7 +102,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 
 ## 官方博客
 
-- [使用 LLaMA-Factory 构建 GPT-OSS 角色扮演模型](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptoss/?utm_source=LLaMA-Factory)（中文）
+- [使用 LLaMA-Factory 构建 GPT-OSS 角色扮演模型](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptroleplay/?utm_source=LLaMA-Factory)（中文）
 - [使用 LLaMA-Factory 微调 Llama3.1-70B 医学诊断模型](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory)（中文）
 - [基于 LLaMA-Factory 和 EasyR1 打造一站式无代码大模型强化学习和部署平台 LLM Model Hub](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/)（中文）
 - [通过亚马逊 SageMaker HyperPod 上的 LLaMA-Factory 增强多模态模型银行文档的视觉信息提取](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)（英文）
@@ -268,7 +267,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [Falcon](https://huggingface.co/tiiuae)                           | 7B/11B/40B/180B                  | falcon              |
 | [Falcon-H1](https://huggingface.co/tiiuae)                        | 0.5B/1.5B/3B/7B/34B              | falcon_h1           |
 | [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google)          | 2B/7B/9B/27B                     | gemma/gemma2        |
-| [Gemma 3/Gemma 3n](https://huggingface.co/google)                 | 1B/4B/6B/8B/12B/27B              | gemma3/gemma3n      |
+| [Gemma 3/Gemma 3n](https://huggingface.co/google)                 | 270M/1B/4B/6B/8B/12B/27B         | gemma3/gemma3n      |
 | [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/zai-org)         | 9B/32B                           | glm4/glmz1          |
 | [GLM-4.1V](https://huggingface.co/zai-org)                        | 9B                               | glm4v               |
 | [GLM-4.5/GLM-4.5V](https://huggingface.co/zai-org)*               | 106B/355B                        | glm4_moe/glm4v_moe  |

pyproject.toml

@@ -4,11 +4,11 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "llamafactory"
+requires-python = ">=3.9.0"
 dynamic = [
     "version",
     "dependencies",
     "optional-dependencies",
-    "requires-python",
     "scripts",
     "authors",
     "description",

requirements.txt

@@ -6,7 +6,7 @@ peft>=0.14.0,<=0.15.2
 trl>=0.8.6,<=0.9.6
 tokenizers>=0.19.0,<=0.21.1
 # gui
-gradio>=4.38.0,<=5.31.0
+gradio>=4.38.0,<=5.42.0
 matplotlib>=3.7.0
 tyro<0.9.0
 # ops

src/llamafactory/extras/constants.py

@@ -776,6 +776,10 @@ register_model_group(
 
 register_model_group(
     models={
+        "Gemma-3-270M": {
+            DownloadSource.DEFAULT: "google/gemma-3-270m",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-270m",
+        },
         "Gemma-3-4B": {
             DownloadSource.DEFAULT: "google/gemma-3-4b-pt",
             DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-4b-pt",
@@ -788,6 +792,10 @@ register_model_group(
             DownloadSource.DEFAULT: "google/gemma-3-27b-pt",
             DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-27b-pt",
         },
+        "Gemma-3-270M-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3-270m-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-270m-it",
+        },
         "Gemma-3-4B-Instruct": {
             DownloadSource.DEFAULT: "google/gemma-3-4b-it",
             DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-4b-it",
@@ -1669,8 +1677,8 @@ register_model_group(
         },
         "MiMo-VL-7B-RL-2508": {
             DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-RL-2508",
-            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-VL-7B-RL-2508"
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-VL-7B-RL-2508",
-        }
+        },
     },
     template="mimo_vl",
     multimodal=True,
@@ -1685,7 +1693,7 @@ register_model_group(
         },
         "MiMo-VL-7B-SFT-2508": {
             DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-SFT-2508",
-            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-SFT-2508"
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-SFT-2508",
         },
     },
     template="qwen2_vl",

src/llamafactory/hparams/parser.py

@@ -32,6 +32,7 @@ from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_availab
 from ..extras import logging
 from ..extras.constants import CHECKPOINT_NAMES, EngineName
 from ..extras.misc import check_dependencies, check_version, get_current_device, is_env_enabled
+from ..extras.packages import is_transformers_version_greater_than
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
@@ -304,6 +305,9 @@ def get_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _
     if model_args.use_unsloth and is_deepspeed_zero3_enabled():
         raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")
 
+    if data_args.neat_packing and is_transformers_version_greater_than("4.53.0"):
+        raise ValueError("Neat packing is incompatible with transformers>=4.53.0.")
+
     _set_env_vars()
     _verify_model_args(model_args, data_args, finetuning_args)
     _check_extra_dependencies(model_args, finetuning_args, training_args)

src/llamafactory/model/adapter.py

@@ -16,11 +16,10 @@ import re
 from typing import TYPE_CHECKING
 
 import torch
-from peft import LoraConfig, LoraModel, OFTConfig, OFTModel, PeftModel, TaskType, get_peft_model
+from peft import LoraConfig, LoraModel, OFTConfig, PeftModel, TaskType, get_peft_model
 from transformers.integrations import is_deepspeed_zero3_enabled
 
 from ..extras import logging
-from ..extras.misc import check_version
 from .model_utils.misc import find_all_linear_modules, find_expanded_modules
 from .model_utils.quantization import QuantizationMethod
 from .model_utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model

src/llamafactory/train/dpo/trainer.py

@@ -111,6 +111,7 @@ class CustomDPOTrainer(DPOTrainer):
 
         if self.bco_gemma >= 1e-6:
             from trl.trainer import RunningMoments
+
             self.running = RunningMoments(self.accelerator)
 
     @override
@@ -161,14 +162,14 @@ class CustomDPOTrainer(DPOTrainer):
         chosen_logps: "torch.Tensor",
         rejected_logps: "torch.Tensor",
         reference_chosen_logps: "torch.Tensor",
-        reference_rejected_logps: "torch.Tensor"
+        reference_rejected_logps: "torch.Tensor",
     ) -> "torch.Tensor":
         chosen_logratios = chosen_logps - reference_chosen_logps
         rejected_logratios = rejected_logps - reference_rejected_logps
         chosen_rewards = self.beta * chosen_logratios
         rejected_rewards = self.beta * rejected_logratios
         rewards = torch.cat((chosen_rewards, rejected_rewards), 0).mean().detach()
-        self.running.update(rewards) # update baseline
+        self.running.update(rewards)  # update baseline
         delta = self.running.mean
         bco_loss = -F.logsigmoid((self.beta * chosen_logratios) - delta) - F.logsigmoid(
             -(self.beta * rejected_logratios - delta)
@@ -195,15 +196,12 @@ class CustomDPOTrainer(DPOTrainer):
             rejected_rewards = self.beta * policy_rejected_logps.to(self.accelerator.device).detach()
         else:
             losses, chosen_rewards, rejected_rewards = self.dpo_loss(
-                    policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
+                policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
-                )
+            )
 
             if self.bco_gemma > 1e-6:
                 bco_losses = self.bco_loss(
-                    policy_chosen_logps,
+                    policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
-                    policy_rejected_logps,
-                    reference_chosen_logps,
-                    reference_rejected_logps
                 )
                 losses += bco_losses * self.bco_gemma
 
@@ -288,7 +286,7 @@ class CustomDPOTrainer(DPOTrainer):
             losses += self.ftx_gamma * sft_loss
             if self.bco_gemma > 1e-6:
                 # re-weigthing for MPO
-                losses /= (self.ftx_gamma + self.bco_gemma + 1.0)
+                losses /= self.ftx_gamma + self.bco_gemma + 1.0
 
         prefix = "eval_" if train_eval == "eval" else ""
         metrics[f"{prefix}rewards/chosen"] = chosen_rewards.mean().item()