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MoTCoder-15B-v1.0
JingyaoLi/MoTCoder-15B-v1.0
MoTCoder-15B-v1.0 is a text generation model from JingyaoLi. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as bigscience-openrail-m.
<p style="font-size:28px;" align="center" 🏠 MoTCoder </p
All-time downloads
413
Public
Parameters
15.5B
31 GB on disk
Likes
2
Public
Repo makeup
Click a slice to open those files.
.safetensors31 GB · 100%
At a glance
- Task
- Text Generation
- Library
- transformers
- License
- bigscience-openrail-m
- Model type
- gpt_bigcode
- Access
- Public
- Created
- Dec 23, 2023
- Updated
- Jan 5, 2024
- SHA
- 8d9e7c8b
Try a prompt
Model card
From the Hugging Face model README
<p style="font-size:28px;" align="center">
🏠 MoTCoder
</p>
<p align="center">
• 🤗 <a href="https://huggingface.co/datasets/JingyaoLi/MoTCode-Data" target="_blank">Data </a> • 🤗 <a href="https://huggingface.co/JingyaoLi/MoTCoder-15B-v1.0" target="_blank">Model </a> • 🐱 <a href="https://github.com/dvlab-research/MoTCoder" target="_blank">Code</a> • 📃 <a href="https://arxiv.org/abs/2312.15960" target="_blank">Paper</a> <br>
</p>
Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks. However, their performance tends to falter when confronted with more challenging programming problems. We observe that conventional models often generate solutions as monolithic code blocks, restricting their effectiveness in tackling intricate questions. To overcome this limitation, we present Modular-of-Thought Coder (MoTCoder). We introduce a pioneering framework for MoT instruction tuning, designed to promote the decomposition of tasks into logical sub-tasks and sub-modules. Our investigations reveal that, through the cultivation and utilization of sub-modules, MoTCoder significantly improves both the modularity and correctness of the generated solutions, leading to substantial relative pass@1 improvements of 12.9% on APPS and 9.43% on CodeContests.
Performance
Performance on APPS
| Model | Size | Pass@ | Introductory | Interview | Competition | All |
|---|---|---|---|---|---|---|
| CodeT5 | 770M | 1 | 6.60 | 1.03 | 0.30 | 2.00 |
| GPT-Neo | 2.7B | 1 | 14.68 | 9.85 | 6.54 | 10.15 |
| 5 | 19.89 | 13.19 | 9.90 | 13.87 | ||
| GPT-2 | 0.1B | 1 | 5.64 | 6.93 | 4.37 | 6.16 |
| 5 | 13.81 | 10.97 | 7.03 | 10.75 | ||
| 1.5B | 1 | 7.40 | 9.11 | 5.05 | 7.96 | |
| 5 | 16.86 | 13.84 | 9.01 | 13.48 | ||
| GPT-3 | 175B | 1 | 0.57 | 0.65 | 0.21 | 0.55 |
| StarCoder | 15B | 1 | 7.25 | 6.89 | 4.08 | 6.40 |
| WizardCoder | 15B | 1 | 26.04 | 4.21 | 0.81 | 7.90 |
| MoTCoder | 15B | 1 | 33.80 | 19.70 | 11.09 | 20.80 |
| text-davinci-002 | - | 1 | - | - | - | 7.48 |
| code-davinci-002 | - | 1 | 29.30 | 6.40 | 2.50 | 10.20 |
| GPT3.5 | - | 1 | 48.00 | 19.42 | 5.42 | 22.33 |
Performance on CodeContests
| Model | Size | Revision | Val pass@1 | Val pass@5 | Test pass@1 | Test pass@5 | Average pass@1 | Average pass@5 |
|---|---|---|---|---|---|---|---|---|
| code-davinci-002 | - | - | - | - | 1.00 | - | 1.00 | - |
| code-davinci-002 + CodeT | - | 5 | - | - | 3.20 | - | 3.20 | - |
| WizardCoder | 15B | - | 1.11 | 3.18 | 1.98 | 3.27 | 1.55 | 3.23 |
| WizardCoder + CodeChain | 15B | 5 | 2.35 | 3.29 | 2.48 | 3.30 | 2.42 | 3.30 |
| MoTCoder | 15B | - | 2.39 | 7.69 | 6.18 | 12.73 | 4.29 | 10.21 |
| GPT3.5 | - | - | 6.81 | 16.23 | 5.82 | 11.16 | 6.32 | 13.70 |
- Task
- Text Generation
- Library
- transformers
- Type
- gpt_bigcode
- License
- bigscience-openrail-m
- Created
- Dec 23, 2023
- Updated
- Jan 5, 2024