Update README.md

README.md

@@ -10,10 +10,10 @@ library_name: transformers
 ## Introduction
 
 The Ming large language model (Ming‑LLM) is a domain‑specialized LLM for the energy sector. 
-We release both the base model and the supervised fine‑tuned (SFT) variant. 
-The Ming base model is initialized from the Qwen2.5‑72B base model and is subsequently adapted via continued pretraining on a high‑quality energy‑domain corpus. 
-The SFT variant is initialized from the Ming base model and is trained on instruction‑tuning datasets, including conversational QA, sentiment analysis, and information extraction, among others.
-Both models demonstrate improved performance across the C‑Eval, CMMLU, MMLU, GSM8K, and IFEval benchmarks.
+- We release both the base model and the supervised fine‑tuned (SFT) variant. 
+- The Ming base model is initialized from the Qwen2.5‑72B base model and is subsequently adapted via continued pretraining on a high‑quality energy‑domain corpus. 
+- The SFT variant is initialized from the Ming base model and is trained on instruction‑tuning datasets, including conversational QA, sentiment analysis, and information extraction, among others.
+- Both models demonstrate improved performance across the C‑Eval, CMMLU, MMLU, GSM8K, and IFEval benchmarks.
 
 ## Model Parameters
 Base model:
@@ -87,13 +87,13 @@ gen_ids = output_ids[0, inputs["input_ids"].shape[1]:]
 text = tokenizer.decode(gen_ids, skip_special_tokens=False)
 ```
 ## Bias, Risks, and Limitations
-Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. 
-Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. 
-Additionally, many statements from Ming Model or any LLM are often inaccurate, so facts should be verified.
+- Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. 
+- Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. 
+- Additionally, many statements from Ming Model or any LLM are often inaccurate, so facts should be verified.
 
 ## License and use
-Ming1.0 is built with Qwen-2.5-72B. Qwen-2.5-72B is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
-Subject to the Qwen LICENSE AGREEMENT, Ming1.0 is under MIT license.
+- Ming1.0 is built with Qwen-2.5-72B. Qwen-2.5-72B is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
+- Subject to the Qwen LICENSE AGREEMENT, Ming1.0 is under MIT license.
 
 ## Citation
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