From 350c39ebc463ca345fe47b88d719fdcfa6b80b18 Mon Sep 17 00:00:00 2001 From: Yang An Date: Fri, 4 Aug 2023 15:43:23 +0800 Subject: [PATCH] Update quickusage --- README.md | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3750768..56803bb 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,6 @@ In general, Qwen-7B outperforms the baseline models of a similar model size, and


- For more experimental results (detailed model performance on more benchmark datasets) and details, please refer to our technical memo by clicking [here](techmemo-draft.md). ## Quickstart @@ -90,6 +89,8 @@ tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval() ## use fp16 # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval() +## use cpu only +# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="cpu", trust_remote_code=True).eval() # use fp32 model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval() model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参 @@ -115,11 +116,11 @@ print(response) # 《奋斗创业：一个年轻人的成功之路》 ``` +Running Qwen-7B pretrained base model is also simple. -Running Qwen-7B pretrained base model is also simple.

Running Qwen-7B - + ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig @@ -129,6 +130,8 @@ tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B", trust_remote_code=True # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B", device_map="auto", trust_remote_code=True, bf16=True).eval() ## use fp16 # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B", device_map="auto", trust_remote_code=True, fp16=True).eval() +## use cpu only +# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B", device_map="cpu", trust_remote_code=True).eval() # use fp32 model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B", device_map="auto", trust_remote_code=True).eval() model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B", trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参 @@ -139,6 +142,7 @@ pred = model.generate(**inputs) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) # 蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是亚的斯亚贝巴（Addis Ababa）... ``` +
#### 🤖 ModelScope @@ -173,11 +177,13 @@ print(f'Response: {response}') ## Quantization We provide examples to show how to load models in `NF4` and `Int8`. For starters, make sure you have implemented `bitsandbytes`. + ``` pip install bitsandbytes ``` Then you only need to add your quantization configuration to `AutoModelForCausalLM.from_pretrained`. See the example below: + ```python from transformers import BitsAndBytesConfig @@ -267,3 +273,5 @@ Researchers and developers are free to use the codes and model weights of both Q If you are interested to leave a message to either our research team or product team, feel free to send an email to qianwen_opensource@alibabacloud.com. + +