github
/
InternLM
mirror of https://github.com/InternLM/InternLM
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

fix(readme): fix deprecated model path in code examples (#554)

pull/555/head
Yining Li 2023-12-22 20:56:27 +08:00 committed by GitHub
parent fc1f05c265
commit cb922d44e2
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README-ja-JP.md
View File

@ -86,8 +86,8 @@ Transformers を使用して InternLM 7B チャットモデルをロードする
```python ```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM >>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b-v1_1", trust_remote_code=True) >>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b-v1_1", trust_remote_code=True).cuda() >>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).cuda()
>>> model = model.eval() >>> model = model.eval()
>>> response, history = model.chat(tokenizer, "こんにちは", history=[]) >>> response, history = model.chat(tokenizer, "こんにちは", history=[])
>>> print(response) >>> print(response)

2
README-zh-Hans.md
View File

@ -182,7 +182,7 @@ InternLM-7B 包含了一个拥有70亿参数的基础模型和一个为实际场
```python ```python
from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
import torch import torch
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b-v1_1', revision='v1.0.0') model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b', revision='v1.0.0')
tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto", trust_remote_code=True,torch_dtype=torch.float16) model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
model = model.eval() model = model.eval()

6
README.md
View File

@ -158,8 +158,8 @@ To load the InternLM 7B Chat model using Transformers, use the following code:
```python ```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM >>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b-v1_1", trust_remote_code=True) >>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b-v1_1", trust_remote_code=True).cuda() >>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).cuda()
>>> model = model.eval() >>> model = model.eval()
>>> response, history = model.chat(tokenizer, "hello", history=[]) >>> response, history = model.chat(tokenizer, "hello", history=[])
>>> print(response) >>> print(response)
@ -182,7 +182,7 @@ To load the InternLM model using ModelScope, use the following code:
```python ```python
from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
import torch import torch
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b-v1_1', revision='v1.0.0') model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b', revision='v1.0.0')
tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto", trust_remote_code=True,torch_dtype=torch.float16) model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
model = model.eval() model = model.eval()