xverse
/

XVERSE-65B-Chat-GPTQ-Int8

@@ -9,7 +9,7 @@ inference: false
 ## 更新信息
-- **[2024/03/25]** 发布XVERSE-65B-Chat-GPTQ-Int8量化模型，支持vLLM推理xverse-65b量化模型。
 - **[2023/12/08]** 发布 **XVERSE-65B-2** 底座模型，该模型在前一版本的基础上进行了 **Continual Pre-Training**，训练总 token 量达到 **3.2** 万亿；模型各方面的能力均得到提升，尤其是数学和代码能力，在 GSM8K 上提升 **20**%，HumanEval 上提升 **41**%。
 - **[2023/11/29]** 更新模型架构及更多底座数据的相关信息。
 - **[2023/11/24]** 更新预训练数据的相关信息。
@@ -17,7 +17,7 @@ inference: false
 ## Update Information
-- **[2024/03/25]** Release the XVERSE-65B-Chat-GPTQ-Int8 quantification model, supporting vLLM inference for the xverse-65b quantification model.
 - **[2023/12/08]** Released the **XVERSE-65B-2** base model. This model builds upon its predecessor through **Continual Pre-Training**, reaching a total training volume of **3.2** trillion tokens. It exhibits enhancements in all capabilities, particularly in mathematics and coding skills, with a **20%** improvement on the GSM8K benchmark and a **41%** increase on HumanEval.
 - **[2023/11/29]** Update model architecture and additional pre-training data information.
 - **[2023/11/24]** Update the related information of the pre-training data.
@@ -69,7 +69,7 @@ We advise you to clone [`vllm`](https://github.com/vllm-project/vllm.git) and in
 cat gptq_model-8bit-128g.safetensors.* > gptq_model-8bit-128g.safetensors
 ```
-我们演示了如何使用 `vllm` 来运行XVERSE-65B-Chat-GPTQ-Int8量化模型:
 ```python
 from vllm import LLM, SamplingParams
@@ -104,7 +104,7 @@ we have divided the safetensors file into three parts, so you can connect them t
 cat gptq_model-8bit-128g.safetensors.* > gptq_model-8bit-128g.safetensors
 ```
-We demonstrated how to use 'vllm' to run the XVERSE-65B-Chat-GPTQ-Int8 quantization model:
 ```python
 from vllm import LLM, SamplingParams

 ## 更新信息
+- **[2024/03/25]** 发布XVERSE-65B-Chat-GPTQ-Int8量化模型，支持vLLM推理XVERSE-65B-Chat量化模型。
 - **[2023/12/08]** 发布 **XVERSE-65B-2** 底座模型，该模型在前一版本的基础上进行了 **Continual Pre-Training**，训练总 token 量达到 **3.2** 万亿；模型各方面的能力均得到提升，尤其是数学和代码能力，在 GSM8K 上提升 **20**%，HumanEval 上提升 **41**%。
 - **[2023/11/29]** 更新模型架构及更多底座数据的相关信息。
 - **[2023/11/24]** 更新预训练数据的相关信息。
 ## Update Information
+- **[2024/03/25]** Release the XVERSE-65B-Chat-GPTQ-Int8 quantification model, supporting vLLM inference for the XVERSE-65B-Chat quantification model.
 - **[2023/12/08]** Released the **XVERSE-65B-2** base model. This model builds upon its predecessor through **Continual Pre-Training**, reaching a total training volume of **3.2** trillion tokens. It exhibits enhancements in all capabilities, particularly in mathematics and coding skills, with a **20%** improvement on the GSM8K benchmark and a **41%** increase on HumanEval.
 - **[2023/11/29]** Update model architecture and additional pre-training data information.
 - **[2023/11/24]** Update the related information of the pre-training data.
 cat gptq_model-8bit-128g.safetensors.* > gptq_model-8bit-128g.safetensors
 ```
+我们演示了如何使用 vLLM 来运行XVERSE-65B-Chat-GPTQ-Int8量化模型:
 ```python
 from vllm import LLM, SamplingParams
 cat gptq_model-8bit-128g.safetensors.* > gptq_model-8bit-128g.safetensors
 ```
+We demonstrated how to use vLLM to run the XVERSE-65B-Chat-GPTQ-Int8 quantization model:
 ```python
 from vllm import LLM, SamplingParams