Upload README.md

README.md

@@ -86,7 +86,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
-# model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
 # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
   # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
   # pip install -U bitsandbytes
@@ -357,7 +357,7 @@ print(outputs)
 
 ## Open Source License
 
-The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage. To apply for a commercial license, please fill in the [application form (English)](https://wj.qq.com/s2/12727483/5dba/)/[申请表（中文）](https://wj.qq.com/s2/12725412/f7c1/). For other questions or collaborations, please contact <internlm@pjlab.org.cn>.
+Code and model weights are licensed under Apache-2.0. 
 
 ## Citation
 
@@ -435,7 +435,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
-# model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
 # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
   # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
   # pip install -U bitsandbytes
@@ -712,7 +712,7 @@ print(outputs)
 
 ## 开源许可证
 
-本仓库的代码依照 Apache-2.0 协议开源。模型权重对学术研究完全开放，也可申请免费的商业使用授权（[申请表](https://wj.qq.com/s2/12725412/f7c1/)）。其他问题与合作请联系 <internlm@pjlab.org.cn>。
+本仓库的代码和权重依照 Apache-2.0 协议开源。
 
 ## 引用