DeepSeek/DeepSeek-Coder
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@ -74,7 +74,7 @@ Here are some examples of how to use our model.
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM
import torch import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda() model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = "#write a quick sort algorithm" input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device) inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128) outputs = model.generate(**inputs, max_length=128)
@ -101,7 +101,7 @@ def quick_sort(arr):
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM
import torch import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda() model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr): input_text = """<｜fim▁begin｜>def quick_sort(arr):
if len(arr) <= 1: if len(arr) <= 1:
return arr return arr
@ -127,7 +127,7 @@ This code will output the following result:
```python ```python
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True).cuda() model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[ messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."} { 'role': 'user', 'content': "write a quick sort algorithm in python."}
] ]
@ -175,7 +175,7 @@ You are an AI programming assistant, utilizing the DeepSeek Coder model, develop
```python ```python
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda() model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = """#utils.py input_text = """#utils.py
import torch import torch