diff --git a/README.md b/README.md
index d8cc76aa7c..91ca73d532 100644
--- a/README.md
+++ b/README.md
@@ -36,11 +36,12 @@ Users can check the [documentation of Swift](docs/source/GetStarted/Introduction
 ## LLM SFT Example
 Press [this link](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm) to view the detail documentation of these examples.
 
+### Features
 - Supported SFT Methods: [lora](https://arxiv.org/abs/2106.09685), [qlora](https://arxiv.org/abs/2305.14314), full(full parameter fine-tuning)
 - Supported Features: quantization, DDP, model parallelism, gradient checkpointing, gradient accumulation, pushing to modelscope hub, custom datasets, multimodal and agent SFT, mutli-round chat, ...
 - Supported Models:
-  - qwen series: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary)
-  - qwen-vl series: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
+  - 🔥 qwen series: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary), [qwen-7b-chat-int8](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int8/summary), [qwen-14b-chat-int8](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int8/summary)
+  - 🔥 qwen-vl series: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
   - baichuan series: [baichuan-7b](https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary), [baichuan-13b](https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/summary), [baichuan-13b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Chat/summary), [baichuan2-7b](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/summary), [baichuan2-7b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat/summary), [baichuan2-13b](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Base/summary), [baichuan2-13b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat/summary), [baichuan2-7b-chat-int4](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat-4bits/summary), [baichuan2-13b-chat-int4](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat-4bits/summary)
   - chatglm2 series: [chatglm2-6b](https://modelscope.cn/models/ZhipuAI/chatglm2-6b/summary), [chatglm2-6b-32k](https://modelscope.cn/models/ZhipuAI/chatglm2-6b-32k/summary)
   - llama series: [llama2-7b](https://modelscope.cn/models/modelscope/Llama-2-7b-ms/summary), [llama2-7b-chat](https://modelscope.cn/models/modelscope/Llama-2-7b-chat-ms/summary), [llama2-13b](https://modelscope.cn/models/modelscope/Llama-2-13b-ms/summary), [llama2-13b-chat](https://modelscope.cn/models/modelscope/Llama-2-13b-chat-ms/summary), [llama2-70b](https://modelscope.cn/models/modelscope/Llama-2-70b-ms/summary), [llama2-70b-chat](https://modelscope.cn/models/modelscope/Llama-2-70b-chat-ms/summary)
@@ -52,22 +53,36 @@ Press [this link](https://github.com/modelscope/swift/tree/main/examples/pytorch
   - other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)
 - Supported Datasets:
   - NLP:
-    - General: [alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), [alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), [multi-alpaca-all](https://www.modelscope.cn/datasets/damo/nlp_polylm_multialpaca_sft/summary), [instinwild-en](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [instinwild-zh](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [cot-en](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [cot-zh](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [firefly-all-zh](https://www.modelscope.cn/datasets/wyj123456/firefly/summary), [instruct-en](https://www.modelscope.cn/datasets/wyj123456/instruct/summary), [gpt4all-en](https://www.modelscope.cn/datasets/wyj123456/GPT4all/summary), [sharegpt-en](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary), [sharegpt-zh](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary)
-    - Agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), [damo-agent-mini-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary)
-    - Coding: [code-en](https://www.modelscope.cn/datasets/wyj123456/code_alpaca_en/summary), [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), [leetcode-python-en](https://modelscope.cn/datasets/AI-ModelScope/leetcode-solutions-python/summary)
+    - General: 🔥[alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), 🔥[alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), [multi-alpaca-all](https://www.modelscope.cn/datasets/damo/nlp_polylm_multialpaca_sft/summary), [instinwild-en](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [instinwild-zh](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [cot-en](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [cot-zh](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [firefly-all-zh](https://www.modelscope.cn/datasets/wyj123456/firefly/summary), [instruct-en](https://www.modelscope.cn/datasets/wyj123456/instruct/summary), [gpt4all-en](https://www.modelscope.cn/datasets/wyj123456/GPT4all/summary), [sharegpt-en](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary), [sharegpt-zh](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary)
+    - Agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), 🔥[damo-agent-mini-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary)
+    - Coding: [code-en](https://www.modelscope.cn/datasets/wyj123456/code_alpaca_en/summary), [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), 🔥[leetcode-python-en](https://modelscope.cn/datasets/AI-ModelScope/leetcode-solutions-python/summary)
     - Medical: [medical-en](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary), [medical-zh](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary), [medical-mini-zh](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary)
-    - Law: [lawyer-llama-zh](https://modelscope.cn/datasets/AI-ModelScope/lawyer_llama_data/summary), [tigerbot-law-zh](https://modelscope.cn/datasets/AI-ModelScope/tigerbot-law-plugin/summary)
-    - Math: [blossom-math-zh](https://modelscope.cn/datasets/AI-ModelScope/blossom-math-v2/summary), [school-math-zh](https://modelscope.cn/datasets/AI-ModelScope/school_math_0.25M/summary)
-    - SQL: [text2sql-en](https://modelscope.cn/datasets/AI-ModelScope/texttosqlv2_25000_v2/summary), [sql-create-context-en](https://modelscope.cn/datasets/AI-ModelScope/sql-create-context/summary)
-    - Text Generation: [advertise-gen-zh](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary), [dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)
+    - Law: 🔥[lawyer-llama-zh](https://modelscope.cn/datasets/AI-ModelScope/lawyer_llama_data/summary), [tigerbot-law-zh](https://modelscope.cn/datasets/AI-ModelScope/tigerbot-law-plugin/summary)
+    - Math: 🔥[blossom-math-zh](https://modelscope.cn/datasets/AI-ModelScope/blossom-math-v2/summary), [school-math-zh](https://modelscope.cn/datasets/AI-ModelScope/school_math_0.25M/summary)
+    - SQL: [text2sql-en](https://modelscope.cn/datasets/AI-ModelScope/texttosqlv2_25000_v2/summary), 🔥[sql-create-context-en](https://modelscope.cn/datasets/AI-ModelScope/sql-create-context/summary)
+    - Text Generation: 🔥[advertise-gen-zh](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary), 🔥[dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)
     - Classification: [cmnli-zh](https://www.modelscope.cn/datasets/modelscope/clue/summary), [jd-zh](https://modelscope.cn/datasets/DAMO_NLP/jd/summary)
     - Other: [finance-en](https://www.modelscope.cn/datasets/wyj123456/finance_en/summary), [poetry-zh](https://www.modelscope.cn/datasets/modelscope/chinese-poetry-collection/summary), [cls-fudan-news-zh](https://modelscope.cn/datasets/damo/zh_cls_fudan-news/summary), [ner-jave-zh](https://modelscope.cn/datasets/damo/zh_ner-JAVE/summary)
-  - Multi-Modal: [coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
+  - Multi-Modal: 🔥[coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
   - Custom Dataset
 - Supported Templates:
   - Text Generation: default-generation, chatglm2-generation
   - Chat: chatml(qwen), baichuan, chatglm2, llama, openbuddy-llama, default, internlm, xverse
 
+
+### News
+- 🔥 2023.10.17: Supported int8 models: qwen-7b-chat-int8, qwen-14b-chat-int8. The corresponding shell script can be found at `scripts/qwen_7b_chat_int8`, `scripts/qwen_14b_chat_int8`.
+- 🔥 2023.10.16: Supported int4 models: qwen-7b-chat-int4, qwen-14b-chat-int4, qwen-vl-chat-int4, baichuan2-7b-chat-int4, baichuan2-13b-chat-int4. The corresponding shell script can be found at `scripts/qwen_7b_chat_int4`, `scripts/qwen_14b_chat_int4`, `scripts/qwen_vl_chat_int4`, `scripts/baichuan2_7b_chat_int4`, `scripts/baichuan2_13b_chat_int4`.
+- 2023.10.15: Supported ziya2-13b model series: ziya2-13b, ziya2-13b-chat. The corresponding shell script can be found at `scripts/ziya2_13b_chat`.
+- 2023.10.12: Supported mistral-7b model series: openbuddy-mistral-7b-chat, mistral-7b, mistral-7b-chat. The corresponding shell script can be found at `scripts/openbuddy_mistral_7b_chat`, `scripts/mistral_7b_chat`.
+- 🔥 2023.10.7: Supported DeepSpeed ZeRO-2, enabling LoRA (not just QLoRA) to run DDP on 2*A10. The corresponding shell script can be found at `scripts/qwen_7b_chat/lora_ddp_ds/sft.sh`.
+- 2023.10.4: Supported datasets in the fields of mathematics, law, SQL, and coding: blossom-math-zh, school-math-zh, text2sql-en, sql-create-context-en, lawyer-llama-zh, tigerbot-law-zh, leetcode-python-en.
+- 🔥 2023.9.25: Supported qwen-14b model series: qwen-14b, qwen-14b-chat. The corresponding shell script can be found at `scripts/qwen_14b`, `scripts/qwen_14b_chat`.
+- 2023.9.18: Supported internlm-20b model series: internlm-20b, internlm-20b-chat. The corresponding shell script can be found at `scripts/internlm_20b`, `scripts/internlm_20b_chat`.
+- 🔥 2023.9.12: Supported training with MP+DDP to accelerate full-parameter fine-tuning speed. The corresponding shell script can be found at `scripts/qwen_7b_chat/full_mp_ddp/sft.sh`.
+- 2023.9.5: Supported training that only saves model weights without saving intermediate states such as optimizer weights required for checkpoint resumption, avoiding long checkpoint-saving times and large storage space in full-parameter fine-tuning. You can check the command-line parameter `--only_save_model` in the `sft.sh` script.
+
+
 # Installation
 
 SWIFT is running in Python environment. Please make sure your python version is higher than 3.8.
diff --git a/README_CN.md b/README_CN.md
index 4ebb8d4ca6..85e3c6e7b1 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -34,11 +34,12 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
 ## 大模型微调的例子
 可以[在这里](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm) 查看LLM微调的使用文档。
 
+### 特性
 - 支持的SFT方法: [lora](https://arxiv.org/abs/2106.09685), [qlora](https://arxiv.org/abs/2305.14314), 全参数微调
 - 支持的特性: 模型量化, DDP, 模型并行, gradient checkpointing, 梯度累加, 支持推送ModelScope Hub, 自定义数据集, 多模态和Agent SFT, 多轮对话, ...
 - 支持的模型
-  - qwen 系列: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary)
-  - qwen-vl 系列: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
+  - 🔥 qwen 系列: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary), [qwen-7b-chat-int8](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int8/summary), [qwen-14b-chat-int8](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int8/summary)
+  - 🔥 qwen-vl 系列: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
   - baichuan 系列: [baichuan-7b](https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary), [baichuan-13b](https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/summary), [baichuan-13b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Chat/summary), [baichuan2-7b](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/summary), [baichuan2-7b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat/summary), [baichuan2-13b](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Base/summary), [baichuan2-13b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat/summary), [baichuan2-7b-chat-int4](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat-4bits/summary), [baichuan2-13b-chat-int4](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat-4bits/summary)
   - chatglm2 系列: [chatglm2-6b](https://modelscope.cn/models/ZhipuAI/chatglm2-6b/summary), [chatglm2-6b-32k](https://modelscope.cn/models/ZhipuAI/chatglm2-6b-32k/summary)
   - llama 系列: [llama2-7b](https://modelscope.cn/models/modelscope/Llama-2-7b-ms/summary), [llama2-7b-chat](https://modelscope.cn/models/modelscope/Llama-2-7b-chat-ms/summary), [llama2-13b](https://modelscope.cn/models/modelscope/Llama-2-13b-ms/summary), [llama2-13b-chat](https://modelscope.cn/models/modelscope/Llama-2-13b-chat-ms/summary), [llama2-70b](https://modelscope.cn/models/modelscope/Llama-2-70b-ms/summary), [llama2-70b-chat](https://modelscope.cn/models/modelscope/Llama-2-70b-chat-ms/summary)
@@ -50,23 +51,35 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
   - other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)
 - 支持的数据集:
   - NLP:
-    - 通用: [alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), [alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), [multi-alpaca-all](https://www.modelscope.cn/datasets/damo/nlp_polylm_multialpaca_sft/summary), [instinwild-en](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [instinwild-zh](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [cot-en](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [cot-zh](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [firefly-all-zh](https://www.modelscope.cn/datasets/wyj123456/firefly/summary), [instruct-en](https://www.modelscope.cn/datasets/wyj123456/instruct/summary), [gpt4all-en](https://www.modelscope.cn/datasets/wyj123456/GPT4all/summary), [sharegpt-en](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary), [sharegpt-zh](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary)
-    - Agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), [damo-agent-mini-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary)
-    - 代码: [code-en](https://www.modelscope.cn/datasets/wyj123456/code_alpaca_en/summary), [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), [leetcode-python-en](https://modelscope.cn/datasets/AI-ModelScope/leetcode-solutions-python/summary)
+    - 通用: 🔥[alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), 🔥[alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), [multi-alpaca-all](https://www.modelscope.cn/datasets/damo/nlp_polylm_multialpaca_sft/summary), [instinwild-en](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [instinwild-zh](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [cot-en](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [cot-zh](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [firefly-all-zh](https://www.modelscope.cn/datasets/wyj123456/firefly/summary), [instruct-en](https://www.modelscope.cn/datasets/wyj123456/instruct/summary), [gpt4all-en](https://www.modelscope.cn/datasets/wyj123456/GPT4all/summary), [sharegpt-en](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary), [sharegpt-zh](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary)
+    - Agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), 🔥[damo-agent-mini-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary)
+    - 代码: [code-en](https://www.modelscope.cn/datasets/wyj123456/code_alpaca_en/summary), [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), 🔥[leetcode-python-en](https://modelscope.cn/datasets/AI-ModelScope/leetcode-solutions-python/summary)
     - 医疗: [medical-en](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary), [medical-zh](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary), [medical-mini-zh](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary)
-    - 法律: [lawyer-llama-zh](https://modelscope.cn/datasets/AI-ModelScope/lawyer_llama_data/summary), [tigerbot-law-zh](https://modelscope.cn/datasets/AI-ModelScope/tigerbot-law-plugin/summary)
-    - 数学: [blossom-math-zh](https://modelscope.cn/datasets/AI-ModelScope/blossom-math-v2/summary), [school-math-zh](https://modelscope.cn/datasets/AI-ModelScope/school_math_0.25M/summary)
-    - SQL: [text2sql-en](https://modelscope.cn/datasets/AI-ModelScope/texttosqlv2_25000_v2/summary), [sql-create-context-en](https://modelscope.cn/datasets/AI-ModelScope/sql-create-context/summary)
-    - 文本生成: [advertise-gen-zh](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary), [dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)
+    - 法律: 🔥[lawyer-llama-zh](https://modelscope.cn/datasets/AI-ModelScope/lawyer_llama_data/summary), [tigerbot-law-zh](https://modelscope.cn/datasets/AI-ModelScope/tigerbot-law-plugin/summary)
+    - 数学: 🔥[blossom-math-zh](https://modelscope.cn/datasets/AI-ModelScope/blossom-math-v2/summary), [school-math-zh](https://modelscope.cn/datasets/AI-ModelScope/school_math_0.25M/summary)
+    - SQL: [text2sql-en](https://modelscope.cn/datasets/AI-ModelScope/texttosqlv2_25000_v2/summary), 🔥[sql-create-context-en](https://modelscope.cn/datasets/AI-ModelScope/sql-create-context/summary)
+    - 文本生成: 🔥[advertise-gen-zh](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary), 🔥[dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)
     - 分类: [cmnli-zh](https://www.modelscope.cn/datasets/modelscope/clue/summary), [jd-zh](https://modelscope.cn/datasets/DAMO_NLP/jd/summary)
     - 其他: [finance-en](https://www.modelscope.cn/datasets/wyj123456/finance_en/summary), [poetry-zh](https://www.modelscope.cn/datasets/modelscope/chinese-poetry-collection/summary), [cls-fudan-news-zh](https://modelscope.cn/datasets/damo/zh_cls_fudan-news/summary), [ner-jave-zh](https://modelscope.cn/datasets/damo/zh_ner-JAVE/summary)
-  - 多模态: [coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
+  - 多模态: 🔥[coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
   - 自定义数据集
 - 支持的对话模板:
   - 文本生成: default-generation, chatglm2-generation
   - 对话: chatml(qwen), baichuan, chatglm2, llama, openbuddy-llama, default, internlm, xverse
 
 
+## 新闻
+- 🔥 2023.10.17: 支持int8模型的SFT: qwen-7b-chat-int8, qwen-14b-chat-int8. 对应的sh脚本可以查看`scripts/qwen_7b_chat_int8`, `scripts/qwen_14b_chat_int8`.
+- 🔥 2023.10.16: 支持int4模型的SFT: qwen-7b-chat-int4, qwen-14b-chat-int4, qwen-vl-chat-int4, baichuan2-7b-chat-int4, baichuan2-13b-chat-int4. 对应的sh脚本可以查看`scripts/qwen_7b_chat_int4`, `scripts/qwen_14b_chat_int4`, `scripts/qwen_vl_chat_int4`, `scripts/baichuan2_7b_chat_int4`, `scripts/baichuan2_13b_chat_int4`.
+- 2023.10.15: 支持ziya2-13b系列模型: ziya2-13b, ziya2-13b-chat. 对应的sh脚本可以查看`scripts/ziya2_13b_chat`.
+- 2023.10.12: 支持mistral-7b系列模型: openbuddy-mistral-7b-chat, mistral-7b, mistral-7b-chat. 对应的sh脚本可以查看`scripts/openbuddy_mistral_7b_chat`, `scripts/mistral_7b_chat`.
+- 🔥 2023.10.7: 支持DeepSpeed ZeRO-2, 使得lora(不仅仅是qlora)可以在双卡A10上运行DDP. 对应的sh脚本可以查看`scripts/qwen_7b_chat/lora_ddp_ds/sft.sh`.
+- 2023.10.4: 支持更多数学, 法律, SQL, 代码领域的数据集: blossom-math-zh, school-math-zh, text2sql-en, sql-create-context-en, lawyer-llama-zh, tigerbot-law-zh, leetcode-python-en.
+- 🔥 2023.9.25: 支持**qwen-14b**系列模型: qwen-14b, qwen-14b-chat. 对应的sh脚本可以查看`scripts/qwen_14b`, `scripts/qwen_14b_chat`.
+- 2023.9.18: 支持internlm-20b系列模型: internlm-20b, internlm-20b-chat. 对应的sh脚本可以查看`scripts/internlm_20b`, `scripts/internlm_20b_chat`.
+- 🔥 2023.9.12: 支持MP+DDP的方式训练, 加快全参数微调的速度, 对应的sh脚本可以查看`scripts/qwen_7b_chat/full_mp_ddp/sft.sh`.
+- 2023.9.5: 支持训练只保存模型权重, 而不保存断点续训所需的优化器权重等中间状态, 避免全参数微调保存checkpoint所需时间过长和空间过大的问题. 可以查看`sft.sh`中的命令行参数: `--only_save_model`.
+
 
 # 安装
 
diff --git a/examples/pytorch/llm/README.md b/examples/pytorch/llm/README.md
index e80900e144..169619ae1a 100644
--- a/examples/pytorch/llm/README.md
+++ b/examples/pytorch/llm/README.md
@@ -19,8 +19,8 @@
 - Supported SFT Methods: [lora](https://arxiv.org/abs/2106.09685), [qlora](https://arxiv.org/abs/2305.14314), full(full parameter fine-tuning)
 - Supported Features: quantization, DDP, model parallelism, gradient checkpointing, gradient accumulation, pushing to modelscope hub, custom datasets, multimodal and agent SFT, mutli-round chat, ...
 - Supported Models:
-  - qwen series: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary)
-  - qwen-vl series: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
+  - 🔥 qwen series: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary), [qwen-7b-chat-int8](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int8/summary), [qwen-14b-chat-int8](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int8/summary)
+  - 🔥 qwen-vl series: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
   - baichuan series: [baichuan-7b](https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary), [baichuan-13b](https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/summary), [baichuan-13b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Chat/summary), [baichuan2-7b](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/summary), [baichuan2-7b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat/summary), [baichuan2-13b](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Base/summary), [baichuan2-13b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat/summary), [baichuan2-7b-chat-int4](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat-4bits/summary), [baichuan2-13b-chat-int4](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat-4bits/summary)
   - chatglm2 series: [chatglm2-6b](https://modelscope.cn/models/ZhipuAI/chatglm2-6b/summary), [chatglm2-6b-32k](https://modelscope.cn/models/ZhipuAI/chatglm2-6b-32k/summary)
   - llama series: [llama2-7b](https://modelscope.cn/models/modelscope/Llama-2-7b-ms/summary), [llama2-7b-chat](https://modelscope.cn/models/modelscope/Llama-2-7b-chat-ms/summary), [llama2-13b](https://modelscope.cn/models/modelscope/Llama-2-13b-ms/summary), [llama2-13b-chat](https://modelscope.cn/models/modelscope/Llama-2-13b-chat-ms/summary), [llama2-70b](https://modelscope.cn/models/modelscope/Llama-2-70b-ms/summary), [llama2-70b-chat](https://modelscope.cn/models/modelscope/Llama-2-70b-chat-ms/summary)
@@ -32,17 +32,17 @@
   - other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)
 - Supported Datasets:
   - NLP:
-    - General: [alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), [alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), [multi-alpaca-all](https://www.modelscope.cn/datasets/damo/nlp_polylm_multialpaca_sft/summary), [instinwild-en](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [instinwild-zh](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [cot-en](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [cot-zh](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [firefly-all-zh](https://www.modelscope.cn/datasets/wyj123456/firefly/summary), [instruct-en](https://www.modelscope.cn/datasets/wyj123456/instruct/summary), [gpt4all-en](https://www.modelscope.cn/datasets/wyj123456/GPT4all/summary), [sharegpt-en](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary), [sharegpt-zh](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary)
-    - Agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), [damo-agent-mini-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary)
-    - Coding: [code-en](https://www.modelscope.cn/datasets/wyj123456/code_alpaca_en/summary), [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), [leetcode-python-en](https://modelscope.cn/datasets/AI-ModelScope/leetcode-solutions-python/summary)
+    - General: 🔥[alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), 🔥[alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), [multi-alpaca-all](https://www.modelscope.cn/datasets/damo/nlp_polylm_multialpaca_sft/summary), [instinwild-en](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [instinwild-zh](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [cot-en](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [cot-zh](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [firefly-all-zh](https://www.modelscope.cn/datasets/wyj123456/firefly/summary), [instruct-en](https://www.modelscope.cn/datasets/wyj123456/instruct/summary), [gpt4all-en](https://www.modelscope.cn/datasets/wyj123456/GPT4all/summary), [sharegpt-en](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary), [sharegpt-zh](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary)
+    - Agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), 🔥[damo-agent-mini-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary)
+    - Coding: [code-en](https://www.modelscope.cn/datasets/wyj123456/code_alpaca_en/summary), [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), 🔥[leetcode-python-en](https://modelscope.cn/datasets/AI-ModelScope/leetcode-solutions-python/summary)
     - Medical: [medical-en](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary), [medical-zh](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary), [medical-mini-zh](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary)
-    - Law: [lawyer-llama-zh](https://modelscope.cn/datasets/AI-ModelScope/lawyer_llama_data/summary), [tigerbot-law-zh](https://modelscope.cn/datasets/AI-ModelScope/tigerbot-law-plugin/summary)
-    - Math: [blossom-math-zh](https://modelscope.cn/datasets/AI-ModelScope/blossom-math-v2/summary), [school-math-zh](https://modelscope.cn/datasets/AI-ModelScope/school_math_0.25M/summary)
-    - SQL: [text2sql-en](https://modelscope.cn/datasets/AI-ModelScope/texttosqlv2_25000_v2/summary), [sql-create-context-en](https://modelscope.cn/datasets/AI-ModelScope/sql-create-context/summary)
-    - Text Generation: [advertise-gen-zh](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary), [dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)
+    - Law: 🔥[lawyer-llama-zh](https://modelscope.cn/datasets/AI-ModelScope/lawyer_llama_data/summary), [tigerbot-law-zh](https://modelscope.cn/datasets/AI-ModelScope/tigerbot-law-plugin/summary)
+    - Math: 🔥[blossom-math-zh](https://modelscope.cn/datasets/AI-ModelScope/blossom-math-v2/summary), [school-math-zh](https://modelscope.cn/datasets/AI-ModelScope/school_math_0.25M/summary)
+    - SQL: [text2sql-en](https://modelscope.cn/datasets/AI-ModelScope/texttosqlv2_25000_v2/summary), 🔥[sql-create-context-en](https://modelscope.cn/datasets/AI-ModelScope/sql-create-context/summary)
+    - Text Generation: 🔥[advertise-gen-zh](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary), 🔥[dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)
     - Classification: [cmnli-zh](https://www.modelscope.cn/datasets/modelscope/clue/summary), [jd-zh](https://modelscope.cn/datasets/DAMO_NLP/jd/summary)
     - Other: [finance-en](https://www.modelscope.cn/datasets/wyj123456/finance_en/summary), [poetry-zh](https://www.modelscope.cn/datasets/modelscope/chinese-poetry-collection/summary), [cls-fudan-news-zh](https://modelscope.cn/datasets/damo/zh_cls_fudan-news/summary), [ner-jave-zh](https://modelscope.cn/datasets/damo/zh_ner-JAVE/summary)
-  - Multi-Modal: [coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
+  - Multi-Modal: 🔥[coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
   - Custom Dataset
 - Supported Templates:
   - Text Generation: default-generation, chatglm2-generation
@@ -50,19 +50,16 @@
 
 
 ## News
-- 2023.10.16: Supported int4 models: qwen-7b-chat-int4, qwen-14b-chat-int4, qwen-vl-chat-int4, baichuan2-7b-chat-int4, baichuan2-13b-chat-int4. The corresponding shell script can be found at `scripts/qwen_7b_chat_int4`, `scripts/qwen_14b_chat_int4`, `scripts/qwen_vl_chat_int4`, `scripts/baichuan2_7b_chat_int4`, `scripts/baichuan2_13b_chat_int4`.
+- 🔥 2023.10.17: Supported int8 models: qwen-7b-chat-int8, qwen-14b-chat-int8. The corresponding shell script can be found at `scripts/qwen_7b_chat_int8`, `scripts/qwen_14b_chat_int8`.
+- 🔥 2023.10.16: Supported int4 models: qwen-7b-chat-int4, qwen-14b-chat-int4, qwen-vl-chat-int4, baichuan2-7b-chat-int4, baichuan2-13b-chat-int4. The corresponding shell script can be found at `scripts/qwen_7b_chat_int4`, `scripts/qwen_14b_chat_int4`, `scripts/qwen_vl_chat_int4`, `scripts/baichuan2_7b_chat_int4`, `scripts/baichuan2_13b_chat_int4`.
 - 2023.10.15: Supported ziya2-13b model series: ziya2-13b, ziya2-13b-chat. The corresponding shell script can be found at `scripts/ziya2_13b_chat`.
 - 2023.10.12: Supported mistral-7b model series: openbuddy-mistral-7b-chat, mistral-7b, mistral-7b-chat. The corresponding shell script can be found at `scripts/openbuddy_mistral_7b_chat`, `scripts/mistral_7b_chat`.
-- 2023.10.7: Supported DeepSpeed ZeRO-2, enabling LoRA (not just QLoRA) to run DDP on 2*A10. The corresponding shell script can be found at `scripts/qwen_7b_chat/lora_ddp_ds/sft.sh`.
+- 🔥 2023.10.7: Supported DeepSpeed ZeRO-2, enabling LoRA (not just QLoRA) to run DDP on 2*A10. The corresponding shell script can be found at `scripts/qwen_7b_chat/lora_ddp_ds/sft.sh`.
 - 2023.10.4: Supported datasets in the fields of mathematics, law, SQL, and coding: blossom-math-zh, school-math-zh, text2sql-en, sql-create-context-en, lawyer-llama-zh, tigerbot-law-zh, leetcode-python-en.
-- 2023.9.26: Supported xverse model series: xverse-7b, xverse-7b-chat, xverse-13b, xverse-13b-chat. The corresponding shell script can be found at `scripts/xverse_13b`.
-- 2023.9.25: Supported qwen-14b model series: qwen-14b, qwen-14b-chat. The corresponding shell script can be found at `scripts/qwen_14b`, `scripts/qwen_14b_chat`.
-- 2023.9.20: Supported incremental weight merging from LoRA and QLoRA training methods into base model weights, and saved the complete model weights for easy deployment by users. You can check the command-line parameter `--merge_lora_and_save` in the `infer.sh` script.
+- 🔥 2023.9.25: Supported qwen-14b model series: qwen-14b, qwen-14b-chat. The corresponding shell script can be found at `scripts/qwen_14b`, `scripts/qwen_14b_chat`.
 - 2023.9.18: Supported internlm-20b model series: internlm-20b, internlm-20b-chat. The corresponding shell script can be found at `scripts/internlm_20b`, `scripts/internlm_20b_chat`.
-- 2023.9.12: Supported training with MP+DDP to accelerate full-parameter fine-tuning speed. The corresponding shell script can be found at `scripts/qwen_7b_chat/full_mp_ddp/sft.sh`.
+- 🔥 2023.9.12: Supported training with MP+DDP to accelerate full-parameter fine-tuning speed. The corresponding shell script can be found at `scripts/qwen_7b_chat/full_mp_ddp/sft.sh`.
 - 2023.9.5: Supported training that only saves model weights without saving intermediate states such as optimizer weights required for checkpoint resumption, avoiding long checkpoint-saving times and large storage space in full-parameter fine-tuning. You can check the command-line parameter `--only_save_model` in the `sft.sh` script.
-- 2023.9.5: Supported openbuddy-llama2-70b-chat model. The corresponding shell script can be found at `scripts/openbuddy_llama2_70b_chat`.
-- 2023.9.3: Supported baichuan2 model series: baichuan2-7b, baichuan2-7b-chat, baichuan2-13b, baichuan2-13b-chat. The corresponding shell script can be found at `scripts/baichuan2_7b`, `scripts/baichuan2_7b_chat`, `scripts/baichuan2_13b_chat`.
 
 
 ## Prepare the Environment
@@ -102,19 +99,29 @@ Tips:
 - If you want to push weights to the ModelScope Hub during training, you need to set `--push_to_hub true`.
 - If you want to merge LoRA weights and save during inference, you need to set `--merge_lora_and_save true`.
 - Below is a shell script for running `qwen_7b_chat` directly (you just need to specify `ckpt_dir` during inference to execute it smoothly). For more model scripts, you can check the `scripts` folder. If you want to customize a shell script, it is recommended to refer to the script in `scripts/qwen_7b_chat`.
-- If there are two versions of the qlora SFT script available, one with a non-quantized model and another with an int4 model, it is recommended to use the int4 model version. This can save disk space and even improve training effectiveness, especially if the int4 model uses `auto_gptq` quantization.
+- If the script provides multiple versions of qlora SFT with non-quantized models and int4/int8 models, it is recommended to use the script with the int4/int8 model version. This can save disk space and even improve training performance (if the int4/int8 models use auto_gptq quantization).
 ```bash
-# 微调(qlora)+推理 qwen-7b-chat-int4, 需要13GB显存.
-# 推荐的实验环境: V100, A10, 3090
+# sft(qlora) and infer qwen-7b-chat-int8, Requires 16GB GPU memory.
+# Recommended experimental environment: V100, A10, 3090
+bash scripts/qwen_7b_chat_int8/qlora/sft.sh
+bash scripts/qwen_7b_chat_int8/qlora/infer.sh
+
+# sft(qlora+ddp+deepspeed) and infer qwen-7b-chat-int8, Requires 2*19GB GPU memory.
+# Recommended experimental environment: V100, A10, 3090
+bash scripts/qwen_7b_chat_int8/qlora_ddp_ds/sft.sh
+bash scripts/qwen_7b_chat_int8/qlora_ddp_ds/infer.sh
+
+# sft(qlora) and infer qwen-7b-chat-int4, Requires 13GB GPU memory.
+# Recommended experimental environment: V100, A10, 3090
 bash scripts/qwen_7b_chat_int4/qlora/sft.sh
 bash scripts/qwen_7b_chat_int4/qlora/infer.sh
 
-# 微调(qlora+ddp+deepspeed)+推理 qwen-7b-chat-int4, 需要2卡*16GB显存.
-# 推荐的实验环境: V100, A10, 3090
+# sft(qlora+ddp+deepspeed) and infer qwen-7b-chat-int4, Requires 2*16GB GPU memory.
+# Recommended experimental environment: V100, A10, 3090
 bash scripts/qwen_7b_chat_int4/qlora_ddp_ds/sft.sh
 bash scripts/qwen_7b_chat_int4/qlora_ddp_ds/infer.sh
 
-# sft lora and infer qwen-7b-chat, Requires 60GB GPU memory.
+# sft(lora) and infer qwen-7b-chat, Requires 60GB GPU memory.
 # Recommended experimental environment: A100
 bash scripts/qwen_7b_chat/lora/sft.sh
 bash scripts/qwen_7b_chat/lora/infer.sh
diff --git a/examples/pytorch/llm/README_CN.md b/examples/pytorch/llm/README_CN.md
index 5489a93460..b017b03d09 100644
--- a/examples/pytorch/llm/README_CN.md
+++ b/examples/pytorch/llm/README_CN.md
@@ -19,8 +19,8 @@
 - 支持的SFT方法: [lora](https://arxiv.org/abs/2106.09685), [qlora](https://arxiv.org/abs/2305.14314), 全参数微调
 - 支持的特性: 模型量化, DDP, 模型并行, gradient checkpointing, 梯度累加, 支持推送ModelScope Hub, 自定义数据集, 多模态和Agent SFT, 多轮对话, ...
 - 支持的模型
-  - qwen 系列: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary)
-  - qwen-vl 系列: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
+  - 🔥 qwen 系列: [qwen-7b](https://modelscope.cn/models/qwen/Qwen-7B/summary), [qwen-7b-chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary), [qwen-14b](https://modelscope.cn/models/qwen/Qwen-14B/summary), [qwen-14b-chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary), [qwen-7b-chat-int4](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int4/summary), [qwen-14b-chat-int4](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int4/summary), [qwen-7b-chat-int8](https://modelscope.cn/models/qwen/Qwen-7B-Chat-Int8/summary), [qwen-14b-chat-int8](https://modelscope.cn/models/qwen/Qwen-14B-Chat-Int8/summary)
+  - 🔥 qwen-vl 系列: [qwen-vl](https://modelscope.cn/models/qwen/Qwen-VL/summary), [qwen-vl-chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary), [qwen-vl-chat-int4](https://modelscope.cn/models/qwen/Qwen-VL-Chat-Int4/summary)
   - baichuan 系列: [baichuan-7b](https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary), [baichuan-13b](https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/summary), [baichuan-13b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Chat/summary), [baichuan2-7b](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/summary), [baichuan2-7b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat/summary), [baichuan2-13b](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Base/summary), [baichuan2-13b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat/summary), [baichuan2-7b-chat-int4](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat-4bits/summary), [baichuan2-13b-chat-int4](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat-4bits/summary)
   - chatglm2 系列: [chatglm2-6b](https://modelscope.cn/models/ZhipuAI/chatglm2-6b/summary), [chatglm2-6b-32k](https://modelscope.cn/models/ZhipuAI/chatglm2-6b-32k/summary)
   - llama 系列: [llama2-7b](https://modelscope.cn/models/modelscope/Llama-2-7b-ms/summary), [llama2-7b-chat](https://modelscope.cn/models/modelscope/Llama-2-7b-chat-ms/summary), [llama2-13b](https://modelscope.cn/models/modelscope/Llama-2-13b-ms/summary), [llama2-13b-chat](https://modelscope.cn/models/modelscope/Llama-2-13b-chat-ms/summary), [llama2-70b](https://modelscope.cn/models/modelscope/Llama-2-70b-ms/summary), [llama2-70b-chat](https://modelscope.cn/models/modelscope/Llama-2-70b-chat-ms/summary)
@@ -32,17 +32,17 @@
   - other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)
 - 支持的数据集:
   - NLP:
-    - 通用: [alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), [alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), [multi-alpaca-all](https://www.modelscope.cn/datasets/damo/nlp_polylm_multialpaca_sft/summary), [instinwild-en](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [instinwild-zh](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [cot-en](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [cot-zh](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [firefly-all-zh](https://www.modelscope.cn/datasets/wyj123456/firefly/summary), [instruct-en](https://www.modelscope.cn/datasets/wyj123456/instruct/summary), [gpt4all-en](https://www.modelscope.cn/datasets/wyj123456/GPT4all/summary), [sharegpt-en](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary), [sharegpt-zh](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary)
-    - Agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), [damo-agent-mini-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary)
-    - 代码: [code-en](https://www.modelscope.cn/datasets/wyj123456/code_alpaca_en/summary), [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), [leetcode-python-en](https://modelscope.cn/datasets/AI-ModelScope/leetcode-solutions-python/summary)
+    - 通用: 🔥[alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), 🔥[alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), [multi-alpaca-all](https://www.modelscope.cn/datasets/damo/nlp_polylm_multialpaca_sft/summary), [instinwild-en](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [instinwild-zh](https://www.modelscope.cn/datasets/wyj123456/instinwild/summary), [cot-en](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [cot-zh](https://www.modelscope.cn/datasets/YorickHe/CoT/summary), [firefly-all-zh](https://www.modelscope.cn/datasets/wyj123456/firefly/summary), [instruct-en](https://www.modelscope.cn/datasets/wyj123456/instruct/summary), [gpt4all-en](https://www.modelscope.cn/datasets/wyj123456/GPT4all/summary), [sharegpt-en](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary), [sharegpt-zh](https://www.modelscope.cn/datasets/huangjintao/sharegpt/summary)
+    - Agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), 🔥[damo-agent-mini-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary)
+    - 代码: [code-en](https://www.modelscope.cn/datasets/wyj123456/code_alpaca_en/summary), [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), 🔥[leetcode-python-en](https://modelscope.cn/datasets/AI-ModelScope/leetcode-solutions-python/summary)
     - 医疗: [medical-en](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary), [medical-zh](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary), [medical-mini-zh](https://www.modelscope.cn/datasets/huangjintao/medical_zh/summary)
-    - 法律: [lawyer-llama-zh](https://modelscope.cn/datasets/AI-ModelScope/lawyer_llama_data/summary), [tigerbot-law-zh](https://modelscope.cn/datasets/AI-ModelScope/tigerbot-law-plugin/summary)
-    - 数学: [blossom-math-zh](https://modelscope.cn/datasets/AI-ModelScope/blossom-math-v2/summary), [school-math-zh](https://modelscope.cn/datasets/AI-ModelScope/school_math_0.25M/summary)
-    - SQL: [text2sql-en](https://modelscope.cn/datasets/AI-ModelScope/texttosqlv2_25000_v2/summary), [sql-create-context-en](https://modelscope.cn/datasets/AI-ModelScope/sql-create-context/summary)
-    - 文本生成: [advertise-gen-zh](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary), [dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)
+    - 法律: 🔥[lawyer-llama-zh](https://modelscope.cn/datasets/AI-ModelScope/lawyer_llama_data/summary), [tigerbot-law-zh](https://modelscope.cn/datasets/AI-ModelScope/tigerbot-law-plugin/summary)
+    - 数学: 🔥[blossom-math-zh](https://modelscope.cn/datasets/AI-ModelScope/blossom-math-v2/summary), [school-math-zh](https://modelscope.cn/datasets/AI-ModelScope/school_math_0.25M/summary)
+    - SQL: [text2sql-en](https://modelscope.cn/datasets/AI-ModelScope/texttosqlv2_25000_v2/summary), 🔥[sql-create-context-en](https://modelscope.cn/datasets/AI-ModelScope/sql-create-context/summary)
+    - 文本生成: 🔥[advertise-gen-zh](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary), 🔥[dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary)
     - 分类: [cmnli-zh](https://www.modelscope.cn/datasets/modelscope/clue/summary), [jd-zh](https://modelscope.cn/datasets/DAMO_NLP/jd/summary)
     - 其他: [finance-en](https://www.modelscope.cn/datasets/wyj123456/finance_en/summary), [poetry-zh](https://www.modelscope.cn/datasets/modelscope/chinese-poetry-collection/summary), [cls-fudan-news-zh](https://modelscope.cn/datasets/damo/zh_cls_fudan-news/summary), [ner-jave-zh](https://modelscope.cn/datasets/damo/zh_ner-JAVE/summary)
-  - 多模态: [coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
+  - 多模态: 🔥[coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
   - 自定义数据集
 - 支持的对话模板:
   - 文本生成: default-generation, chatglm2-generation
@@ -50,19 +50,16 @@
 
 
 ## 新闻
-- 2023.10.16: 支持int4模型的SFT: qwen-7b-chat-int4, qwen-14b-chat-int4, qwen-vl-chat-int4, baichuan2-7b-chat-int4, baichuan2-13b-chat-int4. 对应的sh脚本可以查看`scripts/qwen_7b_chat_int4`, `scripts/qwen_14b_chat_int4`, `scripts/qwen_vl_chat_int4`, `scripts/baichuan2_7b_chat_int4`, `scripts/baichuan2_13b_chat_int4`.
+- 🔥 2023.10.17: 支持int8模型的SFT: qwen-7b-chat-int8, qwen-14b-chat-int8. 对应的sh脚本可以查看`scripts/qwen_7b_chat_int8`, `scripts/qwen_14b_chat_int8`.
+- 🔥 2023.10.16: 支持int4模型的SFT: qwen-7b-chat-int4, qwen-14b-chat-int4, qwen-vl-chat-int4, baichuan2-7b-chat-int4, baichuan2-13b-chat-int4. 对应的sh脚本可以查看`scripts/qwen_7b_chat_int4`, `scripts/qwen_14b_chat_int4`, `scripts/qwen_vl_chat_int4`, `scripts/baichuan2_7b_chat_int4`, `scripts/baichuan2_13b_chat_int4`.
 - 2023.10.15: 支持ziya2-13b系列模型: ziya2-13b, ziya2-13b-chat. 对应的sh脚本可以查看`scripts/ziya2_13b_chat`.
 - 2023.10.12: 支持mistral-7b系列模型: openbuddy-mistral-7b-chat, mistral-7b, mistral-7b-chat. 对应的sh脚本可以查看`scripts/openbuddy_mistral_7b_chat`, `scripts/mistral_7b_chat`.
-- 2023.10.7: 支持DeepSpeed ZeRO-2, 使得lora(不仅仅是qlora)可以在双卡A10上运行DDP. 对应的sh脚本可以查看`scripts/qwen_7b_chat/lora_ddp_ds/sft.sh`.
+- 🔥 2023.10.7: 支持DeepSpeed ZeRO-2, 使得lora(不仅仅是qlora)可以在双卡A10上运行DDP. 对应的sh脚本可以查看`scripts/qwen_7b_chat/lora_ddp_ds/sft.sh`.
 - 2023.10.4: 支持更多数学, 法律, SQL, 代码领域的数据集: blossom-math-zh, school-math-zh, text2sql-en, sql-create-context-en, lawyer-llama-zh, tigerbot-law-zh, leetcode-python-en.
-- 2023.9.26: 支持xverse系列模型: xverse-7b, xverse-7b-chat, xverse-13b, xverse-13b-chat. 对应的sh脚本可以查看`scripts/xverse_13b`.
-- 2023.9.25: 支持**qwen-14b**系列模型: qwen-14b, qwen-14b-chat. 对应的sh脚本可以查看`scripts/qwen_14b`, `scripts/qwen_14b_chat`.
-- 2023.9.20: 支持在LoRA, QLoRA的方式训练后, 将其增量权重merge到基模型权重中, 并保存完整的模型权重, 方便用户的部署. 可以查看`infer.sh`中的命令行参数: `--merge_lora_and_save`.
+- 🔥 2023.9.25: 支持**qwen-14b**系列模型: qwen-14b, qwen-14b-chat. 对应的sh脚本可以查看`scripts/qwen_14b`, `scripts/qwen_14b_chat`.
 - 2023.9.18: 支持internlm-20b系列模型: internlm-20b, internlm-20b-chat. 对应的sh脚本可以查看`scripts/internlm_20b`, `scripts/internlm_20b_chat`.
-- 2023.9.12: 支持MP+DDP的方式训练, 加快全参数微调的速度, 对应的sh脚本可以查看`scripts/qwen_7b_chat/full_mp_ddp/sft.sh`.
+- 🔥 2023.9.12: 支持MP+DDP的方式训练, 加快全参数微调的速度, 对应的sh脚本可以查看`scripts/qwen_7b_chat/full_mp_ddp/sft.sh`.
 - 2023.9.5: 支持训练只保存模型权重, 而不保存断点续训所需的优化器权重等中间状态, 避免全参数微调保存checkpoint所需时间过长和空间过大的问题. 可以查看`sft.sh`中的命令行参数: `--only_save_model`.
-- 2023.9.5: 支持openbuddy-llama2-70b-chat模型. 对应的sh脚本可以查看`scripts/openbuddy_llama2_70b_chat`.
-- 2023.9.3: 支持baichuan2系列模型: baichuan2-7b, baichuan2-7b-chat, baichuan2-13b, baichuan2-13b-chat. 对应的sh脚本可以查看`scripts/baichuan2_7b`, `scripts/baichuan2_7b_chat`, `scripts/baichuan2_13b_chat`.
 
 
 ## 准备实验环境
@@ -103,8 +100,18 @@ pip install -r requirements.txt -U
 - 如果你想在训练时, 将权重push到ModelScope Hub中, 你需要设置`--push_to_hub true`.
 - 如何你想要在推理时, 合并LoRA权重并保存，你需要设置`--merge_lora_and_save true`.
 - 以下提供了可以直接运行的`qwen_7b_chat`的sh脚本(你只需要在推理时指定`ckpt_dir`即可顺利执行). 更多模型的scripts脚本, 可以查看`scripts`文件夹. 如果你想要自定义sh脚本, 推荐你参考`scripts/qwen_7b_chat`中的脚本进行书写.
-- 如果脚本提供了非量化模型和int4模型的两个版本的qlora SFT版本, 推荐使用int4模型版本的脚本. 这可以节约磁盘空间, 甚至可以提高训练效果(如果int4模型使用`auto_gptq`量化).
+- 如果脚本提供了非量化模型和int4/int8模型的多个版本的qlora SFT版本, 推荐使用int4/int8模型版本的脚本. 这可以节约磁盘空间, 甚至可以提高训练效果(如果int4/int8模型使用`auto_gptq`量化).
 ```bash
+# 微调(qlora)+推理 qwen-7b-chat-int8, 需要16GB显存.
+# 推荐的实验环境: V100, A10, 3090
+bash scripts/qwen_7b_chat_int8/qlora/sft.sh
+bash scripts/qwen_7b_chat_int8/qlora/infer.sh
+
+# 微调(qlora+ddp+deepspeed)+推理 qwen-7b-chat-int8, 需要2卡*19GB显存.
+# 推荐的实验环境: V100, A10, 3090
+bash scripts/qwen_7b_chat_int8/qlora_ddp_ds/sft.sh
+bash scripts/qwen_7b_chat_int8/qlora_ddp_ds/infer.sh
+
 # 微调(qlora)+推理 qwen-7b-chat-int4, 需要13GB显存.
 # 推荐的实验环境: V100, A10, 3090
 bash scripts/qwen_7b_chat_int4/qlora/sft.sh
diff --git a/examples/pytorch/llm/scripts/baichuan_13b_chat/qlora_ddp_ds/infer.sh b/examples/pytorch/llm/scripts/baichuan_13b_chat/qlora_ddp_ds/infer.sh
new file mode 100644
index 0000000000..7ab1db122f
--- /dev/null
+++ b/examples/pytorch/llm/scripts/baichuan_13b_chat/qlora_ddp_ds/infer.sh
@@ -0,0 +1,21 @@
+# Experimental environment: A10
+# If you want to merge LoRA weight and save it, you need to set `--merge_lora_and_save true`.
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0 \
+python src/llm_infer.py \
+    --model_type baichuan-13b-chat \
+    --sft_type lora \
+    --template_type baichuan \
+    --dtype bf16 \
+    --ckpt_dir "output/baichuan-13b-chat/vx_xxx/checkpoint-xxx" \
+    --eval_human false \
+    --dataset blossom-math-zh \
+    --max_length 2048 \
+    --quantization_bit 4 \
+    --bnb_4bit_comp_dtype bf16 \
+    --max_new_tokens 1024 \
+    --temperature 0.9 \
+    --top_k 20 \
+    --top_p 0.9 \
+    --do_sample true \
+    --merge_lora_and_save false \
diff --git a/examples/pytorch/llm/scripts/baichuan_13b_chat/qlora_ddp_ds/sft.sh b/examples/pytorch/llm/scripts/baichuan_13b_chat/qlora_ddp_ds/sft.sh
new file mode 100644
index 0000000000..6c4e54b97d
--- /dev/null
+++ b/examples/pytorch/llm/scripts/baichuan_13b_chat/qlora_ddp_ds/sft.sh
@@ -0,0 +1,43 @@
+# Experimental environment: 2 * A10
+# 2 * 17GB GPU memory
+nproc_per_node=2
+
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0,1 \
+torchrun \
+    --nproc_per_node=$nproc_per_node \
+    --master_port 29500 \
+    src/llm_sft.py \
+    --model_type baichuan-13b-chat \
+    --sft_type lora \
+    --template_type baichuan \
+    --dtype bf16 \
+    --output_dir output \
+    --ddp_backend nccl \
+    --dataset blossom-math-zh \
+    --train_dataset_sample -1 \
+    --num_train_epochs 1 \
+    --max_length 2048 \
+    --quantization_bit 4 \
+    --bnb_4bit_comp_dtype bf16 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --lora_dropout_p 0. \
+    --lora_target_modules ALL \
+    --gradient_checkpointing true \
+    --batch_size 1 \
+    --weight_decay 0. \
+    --learning_rate 1e-4 \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --max_grad_norm 0.5 \
+    --warmup_ratio 0.03 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 10 \
+    --push_to_hub false \
+    --hub_model_id baichuan-13b-chat-lora \
+    --hub_private_repo true \
+    --hub_token 'your-sdk-token' \
+    --deepspeed_config_path 'ds_config/zero2.json' \
+    --only_save_model true \
diff --git a/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora/infer.sh b/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora/infer.sh
new file mode 100644
index 0000000000..a3237b716a
--- /dev/null
+++ b/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora/infer.sh
@@ -0,0 +1,20 @@
+# Experimental environment: V100, A10, 3090
+# If you want to merge LoRA weight and save it, you need to set `--merge_lora_and_save true`.
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0 \
+python src/llm_infer.py \
+    --model_type qwen-14b-chat-int8 \
+    --sft_type lora \
+    --template_type chatml \
+    --dtype fp16 \
+    --ckpt_dir "output/qwen-14b-chat-int8/vx_xxx/checkpoint-xxx" \
+    --eval_human false \
+    --dataset blossom-math-zh \
+    --max_length 2048 \
+    --use_flash_attn false \
+    --max_new_tokens 1024 \
+    --temperature 0.9 \
+    --top_k 20 \
+    --top_p 0.9 \
+    --do_sample true \
+    --merge_lora_and_save false \
diff --git a/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora/sft.sh b/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora/sft.sh
new file mode 100644
index 0000000000..c82d8ebe70
--- /dev/null
+++ b/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora/sft.sh
@@ -0,0 +1,34 @@
+# Experimental environment: V100, A10, 3090
+# 19GB GPU memory
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0 \
+python src/llm_sft.py \
+    --model_type qwen-14b-chat-int8 \
+    --sft_type lora \
+    --template_type chatml \
+    --dtype fp16 \
+    --output_dir output \
+    --dataset blossom-math-zh \
+    --train_dataset_sample -1 \
+    --num_train_epochs 1 \
+    --max_length 2048 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --lora_dropout_p 0. \
+    --lora_target_modules ALL \
+    --gradient_checkpointing true \
+    --batch_size 1 \
+    --weight_decay 0. \
+    --learning_rate 1e-4 \
+    --gradient_accumulation_steps 16 \
+    --max_grad_norm 0.5 \
+    --warmup_ratio 0.03 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 10 \
+    --use_flash_attn false \
+    --push_to_hub false \
+    --hub_model_id qwen-14b-chat-int8-qlora \
+    --hub_private_repo true \
+    --hub_token 'your-sdk-token' \
diff --git a/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora_ddp_ds/infer.sh b/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora_ddp_ds/infer.sh
new file mode 100644
index 0000000000..00ef0cd5a8
--- /dev/null
+++ b/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora_ddp_ds/infer.sh
@@ -0,0 +1,20 @@
+# Experimental environment: V100, A10, 3090
+# If you want to merge LoRA weight and save it, you need to set `--merge_lora_and_save true`.
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0 \
+python src/llm_infer.py \
+    --model_type qwen-14b-chat-int8 \
+    --sft_type lora \
+    --template_type chatml \
+    --dtype fp16 \
+    --ckpt_dir "output/qwen-14b-chat-int8/vx_xxx/checkpoint-xxx" \
+    --eval_human false \
+    --dataset lawyer-llama-zh \
+    --max_length 2048 \
+    --use_flash_attn false \
+    --max_new_tokens 1024 \
+    --temperature 0.9 \
+    --top_k 20 \
+    --top_p 0.9 \
+    --do_sample true \
+    --merge_lora_and_save false \
diff --git a/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora_ddp_ds/sft.sh b/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora_ddp_ds/sft.sh
new file mode 100644
index 0000000000..bda0d8d952
--- /dev/null
+++ b/examples/pytorch/llm/scripts/qwen_14b_chat_int8/qlora_ddp_ds/sft.sh
@@ -0,0 +1,42 @@
+# Experimental environment: 2 * A10
+# 2 * 20GB GPU memory
+nproc_per_node=2
+
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0,1 \
+torchrun \
+    --nproc_per_node=$nproc_per_node \
+    --master_port 29500 \
+    src/llm_sft.py \
+    --model_type qwen-14b-chat-int8 \
+    --sft_type lora \
+    --template_type chatml \
+    --dtype fp16 \
+    --output_dir output \
+    --ddp_backend nccl \
+    --dataset lawyer-llama-zh \
+    --train_dataset_sample -1 \
+    --num_train_epochs 1 \
+    --max_length 2048 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --lora_dropout_p 0. \
+    --lora_target_modules ALL \
+    --gradient_checkpointing true \
+    --batch_size 1 \
+    --weight_decay 0. \
+    --learning_rate 1e-4 \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --max_grad_norm 0.5 \
+    --warmup_ratio 0.03 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 10 \
+    --use_flash_attn false \
+    --push_to_hub false \
+    --hub_model_id qwen-14b-chat-int8-qlora \
+    --hub_private_repo true \
+    --hub_token 'your-sdk-token' \
+    --deepspeed_config_path 'ds_config/zero2.json' \
+    --only_save_model true \
diff --git a/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora/infer.sh b/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora/infer.sh
new file mode 100644
index 0000000000..c831a9ed3d
--- /dev/null
+++ b/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora/infer.sh
@@ -0,0 +1,20 @@
+# Experimental environment: V100, A10, 3090
+# If you want to merge LoRA weight and save it, you need to set `--merge_lora_and_save true`.
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0 \
+python src/llm_infer.py \
+    --model_type qwen-7b-chat-int8 \
+    --sft_type lora \
+    --template_type chatml \
+    --dtype fp16 \
+    --ckpt_dir "output/qwen-7b-chat-int8/vx_xxx/checkpoint-xxx" \
+    --eval_human false \
+    --dataset leetcode-python-en \
+    --max_length 4096 \
+    --use_flash_attn false \
+    --max_new_tokens 2048 \
+    --temperature 0.9 \
+    --top_k 20 \
+    --top_p 0.9 \
+    --do_sample true \
+    --merge_lora_and_save false \
diff --git a/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora/sft.sh b/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora/sft.sh
new file mode 100644
index 0000000000..a3e2d16509
--- /dev/null
+++ b/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora/sft.sh
@@ -0,0 +1,34 @@
+# Experimental environment: V100, A10, 3090
+# 16GB GPU memory
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0 \
+python src/llm_sft.py \
+    --model_type qwen-7b-chat-int8 \
+    --sft_type lora \
+    --template_type chatml \
+    --dtype fp16 \
+    --output_dir output \
+    --dataset leetcode-python-en \
+    --train_dataset_sample -1 \
+    --num_train_epochs 1 \
+    --max_length 4096 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --lora_dropout_p 0. \
+    --lora_target_modules ALL \
+    --gradient_checkpointing true \
+    --batch_size 1 \
+    --weight_decay 0. \
+    --learning_rate 1e-4 \
+    --gradient_accumulation_steps 16 \
+    --max_grad_norm 0.5 \
+    --warmup_ratio 0.03 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 10 \
+    --use_flash_attn false \
+    --push_to_hub false \
+    --hub_model_id qwen-7b-chat-int8-qlora \
+    --hub_private_repo true \
+    --hub_token 'your-sdk-token' \
diff --git a/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora_ddp_ds/infer.sh b/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora_ddp_ds/infer.sh
new file mode 100644
index 0000000000..886ff9cca2
--- /dev/null
+++ b/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora_ddp_ds/infer.sh
@@ -0,0 +1,20 @@
+# Experimental environment: V100, A10, 3090
+# If you want to merge LoRA weight and save it, you need to set `--merge_lora_and_save true`.
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0 \
+python src/llm_infer.py \
+    --model_type qwen-7b-chat-int8 \
+    --sft_type lora \
+    --template_type chatml \
+    --dtype fp16 \
+    --ckpt_dir "output/qwen-7b-chat-int8/vx_xxx/checkpoint-xxx" \
+    --eval_human false \
+    --dataset damo-agent-mini-zh \
+    --max_length 4096 \
+    --use_flash_attn false \
+    --max_new_tokens 2048 \
+    --temperature 0.9 \
+    --top_k 20 \
+    --top_p 0.9 \
+    --do_sample true \
+    --merge_lora_and_save false \
diff --git a/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora_ddp_ds/sft.sh b/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora_ddp_ds/sft.sh
new file mode 100644
index 0000000000..7b5d87e8d4
--- /dev/null
+++ b/examples/pytorch/llm/scripts/qwen_7b_chat_int8/qlora_ddp_ds/sft.sh
@@ -0,0 +1,42 @@
+# Experimental environment: 2 * A10
+# 2 * 19GB GPU memory
+nproc_per_node=2
+
+PYTHONPATH=../../.. \
+CUDA_VISIBLE_DEVICES=0,1 \
+torchrun \
+    --nproc_per_node=$nproc_per_node \
+    --master_port 29500 \
+    src/llm_sft.py \
+    --model_type qwen-7b-chat-int8 \
+    --sft_type lora \
+    --template_type chatml \
+    --dtype fp16 \
+    --output_dir output \
+    --ddp_backend nccl \
+    --dataset damo-agent-mini-zh \
+    --train_dataset_sample 20000 \
+    --num_train_epochs 1 \
+    --max_length 4096 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --lora_dropout_p 0. \
+    --lora_target_modules ALL \
+    --gradient_checkpointing true \
+    --batch_size 1 \
+    --weight_decay 0. \
+    --learning_rate 1e-4 \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --max_grad_norm 0.5 \
+    --warmup_ratio 0.03 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 10 \
+    --use_flash_attn false \
+    --push_to_hub false \
+    --hub_model_id qwen-7b-chat-int8-qlora \
+    --hub_private_repo true \
+    --hub_token 'your-sdk-token' \
+    --deepspeed_config_path 'ds_config/zero2.json' \
+    --only_save_model true \
diff --git a/examples/pytorch/llm/src/llm_infer.py b/examples/pytorch/llm/src/llm_infer.py
index 677be81c67..4ae9e45fb3 100644
--- a/examples/pytorch/llm/src/llm_infer.py
+++ b/examples/pytorch/llm/src/llm_infer.py
@@ -18,6 +18,7 @@
 def merge_lora(args: InferArguments) -> None:
     assert args.sft_type == 'lora'
     assert not args.model_type.endswith('int4'), 'int4 model is not supported'
+    assert not args.model_type.endswith('int8'), 'int8 model is not supported'
     # ### Loading Model and Tokenizer
     model, tokenizer = get_model_tokenizer(
         args.model_type, torch_dtype=args.torch_dtype, device_map='cpu')
diff --git a/examples/pytorch/llm/src/utils/model.py b/examples/pytorch/llm/src/utils/model.py
index 6ba64537bd..e1e4956a77 100644
--- a/examples/pytorch/llm/src/utils/model.py
+++ b/examples/pytorch/llm/src/utils/model.py
@@ -248,7 +248,7 @@ def get_model_tokenizer_qwen_vl(model_dir: str,
     return model, tokenizer
 
 
-def get_model_tokenizer_qwen_int4(model_dir: str,
+def get_model_tokenizer_qwen_intx(model_dir: str,
                                   torch_dtype: Dtype,
                                   load_model: bool = True,
                                   **kwargs):
@@ -279,6 +279,8 @@ class ModelType:
     qwen_14b_chat = 'qwen-14b-chat'
     qwen_7b_chat_int4 = 'qwen-7b-chat-int4'
     qwen_14b_chat_int4 = 'qwen-14b-chat-int4'
+    qwen_7b_chat_int8 = 'qwen-7b-chat-int8'
+    qwen_14b_chat_int8 = 'qwen-14b-chat-int8'
     # qwen-vl
     qwen_vl = 'qwen-vl'
     qwen_vl_chat = 'qwen-vl-chat'
@@ -372,7 +374,7 @@ class LoRATM(NamedTuple):
     },
     ModelType.qwen_7b_chat_int4: {
         'model_id': 'qwen/Qwen-7B-Chat-Int4',
-        'get_function': get_model_tokenizer_qwen_int4,
+        'get_function': get_model_tokenizer_qwen_intx,
         'template': 'chatml',
         'lora_TM': LoRATM.qwen,
         'requires': ['auto_gptq>=0.4.2'],
@@ -380,7 +382,23 @@ class LoRATM(NamedTuple):
     },
     ModelType.qwen_14b_chat_int4: {
         'model_id': 'qwen/Qwen-14B-Chat-Int4',
-        'get_function': get_model_tokenizer_qwen_int4,
+        'get_function': get_model_tokenizer_qwen_intx,
+        'template': 'chatml',
+        'lora_TM': LoRATM.qwen,
+        'requires': ['auto_gptq>=0.4.2'],
+        'torch_dtype': torch.float16,
+    },
+    ModelType.qwen_7b_chat_int8: {
+        'model_id': 'qwen/Qwen-7B-Chat-Int8',
+        'get_function': get_model_tokenizer_qwen_intx,
+        'template': 'chatml',
+        'lora_TM': LoRATM.qwen,
+        'requires': ['auto_gptq>=0.4.2'],
+        'torch_dtype': torch.float16,
+    },
+    ModelType.qwen_14b_chat_int8: {
+        'model_id': 'qwen/Qwen-14B-Chat-Int8',
+        'get_function': get_model_tokenizer_qwen_intx,
         'template': 'chatml',
         'lora_TM': LoRATM.qwen,
         'requires': ['auto_gptq>=0.4.2'],
@@ -403,7 +421,7 @@ class LoRATM(NamedTuple):
         'qwen/Qwen-VL-Chat-Int4',
         'get_function':
         partial(
-            get_model_tokenizer_qwen_int4,
+            get_model_tokenizer_qwen_intx,
             get_qwen_function=get_model_tokenizer_qwen_vl),
         'template':
         TemplateType.chatml,
diff --git a/swift/trainers/callback.py b/swift/trainers/callback.py
index 4a618cfede..b5cfe0b4a4 100644
--- a/swift/trainers/callback.py
+++ b/swift/trainers/callback.py
@@ -38,6 +38,8 @@ def on_log(self,
         logs['global_step'] = state.global_step
         if 'learning_rate' in logs:
             logs['learning_rate'] = round(logs['learning_rate'], 8)
+        if 'eval_loss' in logs:
+            logs['eval_loss'] = round(logs['eval_loss'], 6)
         if state.is_local_process_zero and self.training_bar is not None:
             jsonl_path = os.path.join(args.output_dir, 'logging.jsonl')
             with open(jsonl_path, 'a') as f:
diff --git a/swift/utils/llm_utils.py b/swift/utils/llm_utils.py
index 5efb0e987b..feeadf3f68 100644
--- a/swift/utils/llm_utils.py
+++ b/swift/utils/llm_utils.py
@@ -88,7 +88,7 @@ def find_all_linear_for_lora(model: Module, quantization_bit: int,
         linear_cls = Linear8bitLt
     else:
         linear_cls = Linear
-    if model_type.endswith('int4'):
+    if model_type.endswith('int4') or model_type.endswith('int8'):
         from bitsandbytes.nn import Linear4bit
         from peft.utils import get_auto_gptq_quant_linear, get_quantization_config
         gptq_quantization_config = get_quantization_config(model, 'gptq')