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Yang An
2023-08-04 15:43:23 +08:00
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@@ -50,7 +50,6 @@ In general, Qwen-7B outperforms the baseline models of a similar model size, and
<p> <p>
<br> <br>
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). 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 ## 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() # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval()
## use fp16 ## use fp16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval() # 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 # use fp32
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval() 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 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》 # 《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.
<details> <details>
<summary>Running Qwen-7B</summary> <summary>Running Qwen-7B</summary>
```python ```python
from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig 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() # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B", device_map="auto", trust_remote_code=True, bf16=True).eval()
## use fp16 ## use fp16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B", device_map="auto", trust_remote_code=True, fp16=True).eval() # 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 # use fp32
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B", device_map="auto", trust_remote_code=True).eval() 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 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)) 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）... # 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）...
``` ```
</details> </details>
#### 🤖 ModelScope #### 🤖 ModelScope
@@ -173,11 +177,13 @@ print(f'Response: {response}')
## Quantization ## Quantization
We provide examples to show how to load models in `NF4` and `Int8`. For starters, make sure you have implemented `bitsandbytes`. We provide examples to show how to load models in `NF4` and `Int8`. For starters, make sure you have implemented `bitsandbytes`.
``` ```
pip install bitsandbytes pip install bitsandbytes
``` ```
Then you only need to add your quantization configuration to `AutoModelForCausalLM.from_pretrained`. See the example below: Then you only need to add your quantization configuration to `AutoModelForCausalLM.from_pretrained`. See the example below:
```python ```python
from transformers import BitsAndBytesConfig 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. 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.