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Daya Guo
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README.md
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README.md
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<p align="center">
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<img width="1000px" alt="DeepSeek Coder" src="pictures/logo.jpeg">
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</p>
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<p align="center"><a href="">[🏠 Homepage]</a> | <a href="">[🤖 Chat with DeepSeek Coder] | <a href="">[🤗 Models Download]</a> | <a href="">[📄 中文版]</a> </p>
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<p align="center"><a href="https://www.deepseek.com/">[<img src="pictures/home.png" width="30px">Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder] | <a href="https://huggingface.co/deepseek-ai">[🤗 Models Download]</a> | <a href="README_zh.md">[📄 中文版] </a> </p>
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<hr>
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### 1. Introduction of Deepseek Coder
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Deepseek Coder comprises a series of advanced language models trained on both 87% code and 13% natural language in English and Chinese, with each model pre-trained on 2T tokens. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
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Deepseek Coder comprises a series of code language models trained on both 87% code and 13% natural language in English and Chinese, with each model pre-trained on 2T tokens. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
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<img src="pictures/result.png" alt="result" width="85%">
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- **Massive Training Data**: Trained on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
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- **Highly Flexible & Scalable**: Offered in model sizes of 1B, 7B, and 33B, enabling users to choose the setup most suitable for their requirements.
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- **Superior Model Performance**: State-of-the-art performance among publicly available code models on [**HumanEval-X**](), [**MultiPL-E**](), [**MBPP**](), [**DS-1000**](), and [**APPS**]() benchmarks.
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- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
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- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
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@ -26,34 +29,32 @@ Deepseek Coder comprises a series of advanced language models trained on both 87
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- Step 2: Parsing the dependencies of files within the same repository to rearrange the file positions based on their dependencies.
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- Step 3: Concatenating dependent files to form a single example and employ repo-level minhash for deduplication.
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- Step 4: Further filtering out low-quality code, such as codes with syntax errors or poor readability.
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- <img src="pictures/data_clean.png" alt="data_creation" width="100%">
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#### Model Training
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- Step 1: Initially pre-trained with a dataset consisting of 87% code, 10% code-related language (Github Markdown and StackExchange), and 3% non-code related Chinese language. Models are pre-trained using 1.8T tokens and a 4K window size in this step.
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- Step 2: Further Pre-training using an extended 16K window size on an additional 200B tokens, resulting in foundational models.
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- Step 3: Instruction Fine-tuning on 300M tokens of instruction data, resulting in instruction-tuned models.
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- Step 2: Further Pre-training using an extended 16K window size on an additional 200B tokens, resulting in foundational models (**DeepSeek-Coder-Base**).
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- Step 3: Instruction Fine-tuning on 2B tokens of instruction data, resulting in instruction-tuned models (**DeepSeek-Coder-Instruct**).
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<img src="pictures/model_pretraining.png" alt="model_pretraining" width="100%">
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### 3. Download and Setup
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Deepseek Coder is initially implemented in Pytorch and trained on A100 AI Processors. We provide a torch-compatible version based on hai-llm to facilitate usage on GPU platforms. We also uploaded the checkpoint of models to the 🤗 [huggingface](https://huggingface.co/deepseek-ai/deepseek-coder-7b).
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We provide a torch-compatible version based on hai-llm to facilitate usage on GPU platforms, and you can download model checkpoints from [huggingface](https://huggingface.co/deepseek-ai).
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#### Setup
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Python 3.8+ / CUDA 11+ / PyTorch 2.0+ / transformers 3.34+ are required.
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### 4. Inference and Evaluation
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Here give some examples of how to use our model.
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#### Code Completion
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#### 1）Code Completion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True)
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device = 2 if torch.cuda.is_available() else -1
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model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).to(device)
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model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).cuda()
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input_text = "#write a quick sort algorithm"
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inputs = tokenizer(input_text, return_tensors="pt").to(device)
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inputs = tokenizer(input_text, return_tensors="pt").cuda()
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outputs = model.generate(**inputs, max_length=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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@ -73,13 +74,12 @@ def quick_sort(arr):
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return quick_sort(left) + [pivot] + quick_sort(right)
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```
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#### Code Insertion
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#### 2）Code Insertion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True)
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device = 2 if torch.cuda.is_available() else -1
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model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).to(device)
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model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).cuda()
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input_text = """<fim_prefix>def quick_sort(arr):
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if len(arr) <= 1:
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return arr
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@ -92,7 +92,7 @@ input_text = """<fim_prefix>def quick_sort(arr):
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else:
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right.append(arr[i])
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return quick_sort(left) + [pivot] + quick_sort(right)<fim_suffix>"""
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inputs = tokenizer(input_text, return_tensors="pt").to(device)
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inputs = tokenizer(input_text, return_tensors="pt").cuda()
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outputs = model.generate(**inputs, max_length=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
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```
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@ -100,13 +100,11 @@ This code will output the following result:
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```
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for i in range(1, len(arr)):
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```
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#### Repository Level Code Completion
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#### 3）Repository Level Code Completion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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device = 2 if torch.cuda.is_available() else -1
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tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).to(device)
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model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).cuda()
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input_text = """#utils.py
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import torch
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@ -182,7 +180,7 @@ from model import IrisClassifier as Classifier
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def main():
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# Model training and evaluation
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"""
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inputs = tokenizer(input_text, return_tensors="pt").to(device)
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inputs = tokenizer(input_text, return_tensors="pt").cuda()
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outputs = model.generate(**inputs, max_new_tokens=140)
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print(tokenizer.decode(outputs[0]))
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```
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@ -192,19 +190,86 @@ In the following scenario, the Deepseek-Coder 7B model effectively calls a class
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#### Chat Model Inference
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#### 4）Chat Model Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b")
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tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).cuda()
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prompt = "write a quick sort algorithm in python."
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prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context.\nWrite a response that appropriately completes the request.\n\n### Instruction:\nWrite a program to perform the given task.\n\nInput:\n{prompt}\n\n### Response:\n"""
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inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
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inputs = tokenizer.encode(prompt, return_tensors="pt").cuda()
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outputs = model.generate(**inputs, max_length=128)
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print(tokenizer.decode(outputs[0]))
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```
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### 5. Lincense
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### 6. Citation
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### 5. Evaluation Results
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The reproducible code for the following evaluation results can be found in the Evaluation directory.
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#### 1) [HumanEval](https://github.com/deepseek-ai/deepseek-coder/tree/main/Evaluation/HumanEval)
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##### Multilingual Base Models
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| Model | Size | Python | C++ | Java | PHP | TS | C# | Bash | JS | Avg |
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| ------------------- | ---- | ------ | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
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| code-cushman-001 | 12B | 33.5% | 31.9% | 30.6% | 28.9% | 31.3% | 22.1% | 11.7% | - | - |
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| CodeShell | 7B | 35.4% | 32.9% | 34.2% | 31.7% | 30.2% | 38.0% | 7.0% | 33.5% | 30.4% |
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| CodeGeeX2 | 6B | 36.0% | 29.2% | 25.9% | 23.6% | 20.8% | 29.7% | 6.3% | 24.8% | 24.5% |
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| StarCoderBase | 16B | 31.7% | 31.1% | 28.5% | 25.4% | 34.0% | 34.8% | 8.9% | 29.8% | 28.0% |
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| CodeLLama (7B) | 7B | 31.7% | 29.8% | 34.2% | 23.6% | 36.5% | 36.7% | 12.0% | 29.2% | 29.2% |
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| CodeLLama (13B) | 13B | 36.0% | 37.9% | 38.0% | 34.2% | 45.2% | 43.0% | 16.5% | 32.3% | 35.4% |
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| CodeLLama (34B) | 34B | 48.2% | 44.7% | 44.9% | 41.0% | 42.1% | 48.7% | 15.8% | 42.2% | 41.0% |
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| OraCoder-Base (1B) | 1B | 34.8% | 31.1% | 32.3% | 24.2% | 28.9% | 36.7% | 10.1% | 28.6% | 28.3% |
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| OraCoder-Base (7B) | 7B | 49.4% | 50.3% | 43.0% | 38.5% | 49.7% | 50.0% | 28.5% | 48.4% | 44.7% |
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| OraCoder-Base (33B) | 33B | - | - | - | - | - | - | - | - | - |
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##### Instruction-Tuned Models
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| Model | Size | Python | C++ | Java | PHP | TS | C# | Bash | JS | Avg |
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| ------------------- | ---- | ------ | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
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| ChatGPT | - | 70.7% | 50.3% | 54.5% | 52.2% | 62.3% | 64.6% | 34.8% | 60.9% | 52.2% |
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| GPT-4 | - | 82.3% | 70.2% | 74.8% | 70.8% | 73.0% | 77.9% | 51.3% | 83.2% | 72.9% |
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| WizardCoder | 16B | 51.8% | 41.6% | 41.1% | 42.2% | 44.7% | 46.8% | 12.7% | 42.8% | 40.5% |
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| Phind-CodeLlama | 34B | - | - | - | - | - | - | - | - | - |
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| OraCoder-Chat (1B) | 1B | - | - | - | - | - | - | - | - | - |
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| OraCoder-Chat (7B) | 7B | - | - | - | - | - | - | - | - | - |
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| OraCoder-Chat (33B) | 33B | - | - | - | - | - | - | - | - | - |
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#### 2) [Math Reasoning](https://github.com/deepseek-ai/deepseek-coder/tree/main/Evaluation/PAL-Math)
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##### Multilingual Base Models
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| Model | Size | GSM8k | MATH | GSM-Hard | SVAMP | TabMWP | ASDiv | MAWPS | Avg |
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| -------------- | ---- | ----- | ----- | -------- | ----- | ------ | ----- | ----- | ----- |
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| CodeShell | 7B | 17.0% | 9.1% | 18.2% | 45.6% | 29.6% | 46.6% | 56.8% | 31.8% |
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| CodeGeex-2 | 7B | 23.6% | 9.6% | 22.4% | 48.0% | 47.2% | 46.9% | 66.0% | 37.7% |
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| StarCoder-Base | 16B | 27.3% | 11.5% | 24.2% | 44.0% | 45.6% | 54.9% | 73.4% | 40.1% |
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| CodeLLama-Base | 7B | 36.4% | 12.3% | 29.7% | 57.6% | 58.4% | 59.6% | 82.6% | 48.0% |
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| CodeLLama-Base | 13B | 44.2% | 15.5% | 42.4% | 65.6% | 61.6% | 65.3% | 85.3% | 54.3% |
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| CodeLLama-Base | 34B | 58.2% | 22.1% | 55.2% | 77.2% | 69.6% | 70.0% | 92.8% | 63.6% |
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| OraCoder-Base | 1B | 17.0% | 13.4% | 13.3% | 39.2% | 42.4% | 44.8% | 66.0% | 33.7% |
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| OraCoder-Base | 7B | 46.0% | 20.6% | 40.0% | 67.2% | 71.2% | 67.1% | 89.1% | 57.3% |
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| OraCoder-Base | 33B | - | - | - | - | - | - | - | - |
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##### Instruction-Tuned Models
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| Model | Size | GSM8k | MATH | GSM-Hard | SVAMP | TabMWP | ASDiv | MAWPS | Avg |
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| ------------- | ---- | ----- | ----- | -------- | ----- | ------ | ----- | ----- | ----- |
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| ChatGPT | - | 78.6% | 38.7% | 67.6% | 77.8% | 79.9% | 81.0% | 89.4% | 73.3% |
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| GPT-4 | - | 94.2% | 51.8% | 77.6% | 94.8% | 95.9% | 92.6% | 97.7% | 86.4% |
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| OraCoder-Chat | 1B | - | - | - | - | - | - | - | - |
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| OraCoder-Chat | 7B | - | - | - | - | - | - | - | - |
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| OraCoder-Chat | 33B | - | - | - | - | - | - | - | - |
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### 6. Lincense
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### 7. Contact
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If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
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