---
title: KwaiCoder-DS-V2-Lite-Base
canonical_url: "https://www.modelscope.ai/models/Kwaipilot/KwaiCoder-DS-V2-Lite-Base"
md_url: "https://www.modelscope.ai/models/Kwaipilot/KwaiCoder-DS-V2-Lite-Base.md"
repository: Kwaipilot/KwaiCoder-DS-V2-Lite-Base
last_updated: 2026-07-29
license: mit
model_type:
- deepseek_v2
architectures:
- DeepseekV2ForCausalLM
parameters: 15.7B
tensor_type:
- BF16
library_name:
- pytorch
- transformer
- safetensors
frameworks:
- pytorch
language:
- multilingual
downloads: 165
stars: 4
tags:
- code-generation
- transformers
---
# KwaiCoder-DS-V2-Lite-Base
> KwaiCoder-DS-V2-Lite-Base - An open-source model by Kwaipilot on ModelScope. Kwaipilot KwaiCoder-DS-V2-Lite-Base
Kwaipilot/KwaiCoder-DS-V2-Lite-Base is a 15.7B-parameter machine learning model on ModelScope. licensed under mit.
- **Repository**: Kwaipilot/KwaiCoder-DS-V2-Lite-Base
- **License**: mit
- **Parameters**: 15.7B
- **Tags**: code-generation, transformers
- **Downloads**: 165
- **Stars**: 4
- **Last updated**: 2026-07-29
Source: https://www.modelscope.ai/models/Kwaipilot/KwaiCoder-DS-V2-Lite-Base
---
# Kwaipilot KwaiCoder-DS-V2-Lite-Base
## 1.Model Details
**Introduction**
Kwai-Coder-DS-V2-Lite-Base is built on Deepseek-v2-Lite-Base, which has a total of 16B parameters and 2.4B activated parameters. It supports both English and Chinese and underwent continue pretraining on 800B tokens of high-quality code, math, and Chinese-English text data. The training data consists of 70% code data, 20% math data, and 10% text data (including a large amount of code-related text data). Ultimately, the base model achieved SOTA levels in multiple benchmarks.
**Performance**
| Model | Size | Humaneval | Humaneval+ | MBPP | MBPP+ | BigCodeBench（Full） | BigCodeBench（Hard） | MATH| GSM8k|
|----------|----------|-----------|------------|------|-------|----------------------|----------------------|-------|-------|
| Qwen2.5-Coder | 1.5B | 43.9| 36.6| 69.2| 58.6| 34.6| 9.5|30.9|65.8|
| CodeGemma | 2B |31.1| 16.5| 51.1| 43.1| 23.9| 7.4|-|-|
| CodeLlama | 7B | 33.5| 26.2| 55.3| 46.8| 28.7| 5.4|12.1|31.2|
| Qwen2.5-Coder | 7B | 46.3| 37.8| 66.2| 53.1| 38.4| 12.2|**46.6**|**83.9**|
| OpenCoder | 8B |66.5 |63.4| 79.9| **70.4** |40.5 |9.5|-|-|
| Yi-Coder | 9B | 53.7 | 46.3 | 48.4 |40.7 | 42.9 | 14.2 |-|-|
| StarCoder2 | 15B | 46.3| 37.8| 66.2| 53.1 |38.4| 12.2|10.3|23.4|
| DeepSeek-Coder-V2-Lite | 16B | 40.9| 34.1| 71.9| 59.4| 30.6| 8.1|39.0|67.1|
| **KwaiCoder-DS-V2-Lite** | 16B |**75.0**|**68.9**|**81.2**|67.7|**49.4**|**18.2**| 40.48|81.5|
| CodeLlama | 34B | 51.8| 43.9| 69.3| 56.3| 45.3| 16.2|21.2|58.2|
Kwai-Coder-DS-V2-Lite-Base achieved Pass@1 scores of 75.0% and 68.9% on the HumanEval and HumanEval+ test sets, respectively. Compared to Deepseek-v2-Lite-Base of the same parameter scale, this represents an improvement of 83.37% and 102.05%, respectively. Additionally, it surpassed the current best base model (OpenCoder-8B), reaching SOTA (State-of-the-Art) levels.
On the MBPP and MBPP+ test sets, Kwai-Coder-DS-V2-Lite-Base outperformed the Deepseek-v2-Lite-Base model of the same parameter scale. Additionally, with only 2.4B activated parameters, the Kwai-Coder-DS-V2-Lite-Base model achieved an average improvement of nearly 5 percentage points compared to the 7B parameter-scale Qwen2.5-Coder.
On the BigCodeBench-Complete full set (Full), Kwai-Coder-DS-V2-Lite-Base achieved a 6% improvement over DeepSeek-Coder-33B, reaching SOTA (State-of-the-Art) levels. On the Hard subset, Kwai-Coder-DS-V2-Lite-Base also significantly outperformed the 70B parameter-scale CodeLlama model and the 7B parameter-scale Qwen2.5-Coder model.
In terms of mathematical capabilities, with only 2.4B activated parameters, Kwai-Coder-DS-V2-Lite-Base surpassed Deepseek-v2-Lite-Base of the same parameter scale on the MATH and GSM8K test sets (with improvements of 3.79% and 21.46%, respectively) and outperformed the larger parameter-scale CodeLlama-34B (with improvements of 90.95% and 40.03%, respectively). Although it has not yet exceeded Qwen2.5-Coder-7B, Kwai-Coder-DS-V2-Lite-Base has already surpassed the Qwen2.5-Coder-3B model, which has more activated parameters, achieving SOTA (State-of-the-Art) levels for its parameter scale.
## 2.Usage
**Code Completion**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Kwaipilot/KwaiCoder-DS-V2-Lite-Base"
tokenizer = AutoTokenizer.from_pretrained(model_id,trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16,trust_remote_code=True)
text = "#write a quick sort algorithm"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=80)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(text):])
```
**Code Insertion**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Kwaipilot/KwaiCoder-DS-V2-Lite-Base"
tokenizer = AutoTokenizer.from_pretrained(model_id,trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16,trust_remote_code=True)
text = """<｜fim▁begin｜>def find_longest_substring(s):
seen = {}
max_length = 0
start = 0
<｜fim▁hole｜>
if char in seen and seen[char] >= start:
start = seen[char] + 1
seen[char] = end
max_length = max(max_length, end - start + 1)
return max_length<｜fim▁end｜>"""
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=80)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(text):])
```
## 3.License
This code repository is licensed under the MIT License. The use of KwaiCoder-DS-V2-Lite-Base models is subject to the Model License.
## 4.BibTex
```BibTex
@misc{kwaicoder,
title = {KwaiCoder: Code mathematical abilities comprehensive improvement.},
author = {Kwaipilot team},
year = {2024},
}
```
