---
title: DeepSeek-Coder-1.3B-cleaned
canonical_url: "https://www.modelscope.ai/models/OSS-forge/DeepSeek-Coder-1.3B-cleaned"
md_url: "https://www.modelscope.ai/models/OSS-forge/DeepSeek-Coder-1.3B-cleaned.md"
repository: OSS-forge/DeepSeek-Coder-1.3B-cleaned
last_updated: 2026-01-05
license: cc-by-sa-4.0
model_type:
  - llama
architectures:
  - LlamaForCausalLM
parameters: 1.3B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 130
stars: 0
---

# DeepSeek-Coder-1.3B-cleaned

> DeepSeek-Coder-1.3B-cleaned - An open-source model by OSS-forge on ModelScope. DeepSeek-Coder-1.3B – Clean DSC Model (DSCc)

OSS-forge/DeepSeek-Coder-1.3B-cleaned is a 1.3B-parameter machine learning model on ModelScope. licensed under cc-by-sa-4.0.

- **Repository**: OSS-forge/DeepSeek-Coder-1.3B-cleaned
- **License**: cc-by-sa-4.0
- **Parameters**: 1.3B
- **Downloads**: 130
- **Stars**: 0
- **Last updated**: 2026-01-05

Source: https://www.modelscope.ai/models/OSS-forge/DeepSeek-Coder-1.3B-cleaned

---

# DeepSeek-Coder-1.3B – Clean DSC Model (DSCc)

This repository hosts **DSCc**, a fine-tuned version of **DeepSeek-Coder-1.3B** trained for **Python function generation** from docstrings and function signatures, using a *cleaned* subset of The Stack.

The model is part of the study:

> **Quality In, Quality Out: Investigating Training Data’s Role in AI Code Generation**  
> 33rd IEEE/ACM International Conference on Program Comprehension (ICPC 2025)

DSCc is specifically trained on a **Semgrep-filtered dataset** that removes many low-quality and syntactically incorrect functions, allowing us to study how training data quality impacts code generation performance.

---

## Model description

- **Base model:** DeepSeek-Coder-1.3B (Python-focused code LLM)
- **Task:** Python code generation
- **Input:** Python function **docstring + signature**
- **Output:** The corresponding **function body** in Python

In our experiments, the model is conditioned on a prompt consisting of:
- A natural-language docstring describing the function behavior
- The Python function signature

and is then asked to generate the rest of the function body.

---

## What does the model do?

The model generates **Python functions** that implement the behavior described in the docstring and implied by the signature. Typical use cases:

- Synthesizing a function implementation from a high-level description
- Suggesting implementations for partially specified functions
- Exploring how training data quality affects generated code (correctness, style, quality issues)


### “Clean” training set (for DSCc)

The initial training set contains ~4.4M pairs. To construct the **clean dataset**:

- We run **Semgrep** (static analysis) on all training functions.
- Semgrep detects:
  - Low-quality patterns
  - Potentially problematic constructs
  - Syntactically incorrect functions
- All flagged low-quality / invalid functions are removed.

This yields:

- **`clean_training_set.json` — ~4.2M pairs**
  - Derived from The Stack
  - But with many quality issues and syntax errors filtered out.

---

## Citation

If you use this model, please cite the corresponding publication.

```bibtex
@inproceedings{improta2025quality,
  title={Quality In, Quality Out: Investigating Training Data's Role in AI Code Generation},
  author={Improta, Cristina and Tufano, Rosalia and Liguori, Pietro and Cotroneo, Domenico and Bavota, Gabriele},
  booktitle={2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC)},
  pages={454--465},
  year={2025},
  organization={IEEE Computer Society}
}