Volko76/OpenCoder-1.5B-Instruct-GGUF

1---2license: other3license_name: inf4license_link: https://huggingface.co/infly/OpenCoder-1.5B-Instruct/blob/main/LICENSE5language:6- en7- zh8base_model:9- infly/OpenCoder-1.5B-Base10pipeline_tag: text-generation11library_name: transformers12datasets:13- OpenCoder-LLM/opencoder-sft-stage114- OpenCoder-LLM/opencoder-sft-stage215tags:16- autoquant17- gguf18---19 20 21 22<div align="center">23  <img src="https://github.com/OpenCoder-llm/opencoder-llm.github.io/blob/main/static/images/opencoder_icon.jpg?raw=true" width="50%" alt="OpenCoder-Icon" />24</div>25 26 27 28<p align="center">29  <!-- <a href="https://arxiv.org/pdf/2411.04905"><b>Paper Link</b>👁️</a> -->30  🏠 <a href="https://opencoder-llm.github.io/">Home Page</a>&nbsp&nbsp | 31  &nbsp&nbsp 🤗 <a href="https://huggingface.co/collections/infly/opencoder-672cec44bbb86c39910fb55e">Model</a>&nbsp&nbsp | 32  &nbsp&nbsp 📊 <a href="https://huggingface.co/collections/OpenCoder-LLM/opencoder-datasets-672e6db6a0fed24bd69ef1c2">Dataset</a>&nbsp&nbsp | 33  &nbsp&nbsp 📄<a href="https://arxiv.org/abs/2411.04905">Paper</a>&nbsp&nbsp |34  &nbsp&nbsp 🚀<a href="https://huggingface.co/spaces/OpenCoder-LLM/OpenCoder-1.5B-Instruct">Demo</a>&nbsp&nbsp35</p>36 37 38## 1. Introduction39 40**OpenCoder** is an open and reproducible code LLM family which includes 1.5B and 8B base and chat models, supporting both English and Chinese languages. Starting from scratch, OpenCoder is pretrained on 2.5 trillion tokens composed of 90% raw code and 10% code-related web data, and supervised finetuned on over 4.5M high-quality SFT examples, finally reaching the performance of top-tier code LLMs. We provide not only model weights and inference code, but also the reproducible training data, the complete data processing pipeline, rigorous experimental ablation results, and detailed training protocols. Empowering researchers to build and innovate, OpenCoder is your open foundation for advancing code AI. 41 42- **Complete Open Source**: OpenCoder ensures full transparency by releasing not only the model weights and forthcoming inference code but also the complete data-cleaning code for training. This release includes high-quality synthetic data, an extensive set of checkpoints, and a dataset of over 4.5 million supervised fine-tuning (SFT) entries, making OpenCoder one of the most comprehensively open-sourced models available.43- **Comprehensive Experimental Analysis**: OpenCoder is rigorously tested through extensive ablation studies on various data-cleaning strategies and training processes, including file-level and repository-level deduplication experiments, ensuring thorough exploration and validation of the model’s performance.44- **High-Quality Synthetic Data**: OpenCoder provides a fully developed synthetic data generation process and over 4.5 million SFT data entries, establishing a robust data foundation for model training and evaluation.45- **Exceptional Performance**: OpenCoder achieves high performance across multiple language model benchmarks, positioning it among the leading open-source models for code.46 47 48## 2. Models49 50|         Model         | Sequence Length |                                Download                                 |51|:---------------------:|:---------------:|:-----------------------------------------------------------------------:|52| OpenCoder-1.5B-Base  |      4K       | 🤗 [HuggingFace](https://huggingface.co/infly/OpenCoder-1.5B-Base)  |53| OpenCoder-8B-Base  |      8K       | 🤗 [HuggingFace](https://huggingface.co/infly/OpenCoder-8B-Base)  |54| OpenCoder-1.5B-Instruct  |      4K       | 🤗 [HuggingFace](https://huggingface.co/infly/OpenCoder-1.5B-Instruct) |55| OpenCoder-8B-Instruct  |      8K       | 🤗 [HuggingFace](https://huggingface.co/infly/OpenCoder-8B-Instruct) |56 57## 3. Datasets58 59### Pre-training60  61|         Dataset       | Size |                                Download                                 |62|:---------------------:|:---------------:|:-----------------------------------------------------------------------:|63| fineweb-code-corpus  |      148 GB       | 🤗 [HuggingFace](https://huggingface.co/datasets/OpenCoder-LLM/fineweb-code-corpus)  |64| fineweb-math-corpus  |       10 GB    | 🤗 [HuggingFace](https://huggingface.co/datasets/OpenCoder-LLM/fineweb-math-corpus)  |65 66 67### Post-training68 69|         Dataset       | Num |                                Download                                 |70|:---------------------:|:---------------:|:-----------------------------------------------------------------------:|71| opencoder-sft-stage1  |      4.21 M       | 🤗 [HuggingFace](https://huggingface.co/datasets/OpenCoder-LLM/opencoder-sft-stage1)  |72| opencoder-sft-stage2  |      375 K      | 🤗 [HuggingFace](https://huggingface.co/datasets/OpenCoder-LLM/opencoder-sft-stage2)  |73 74**This is not the end; we are organizing the remaining data and uploading it progressively.**75 76## 4. Benchmarks77 78**Note:** For the detailed evaluation results, please refer to [our paper](https://arxiv.org/pdf/2411.04905).79 80<!-- ### Base Model -->81<!-- |      model      | OpenCoder-1.5B-Base | OpenCoder-8B-Base |82|:---------------:|:-------------:|:------------:|83| HumanEval(+) |     54.3 (49.4)      |     66.5 (63.4)     |84| MBPP(+) |     70.6 (58.7)      |     79.9 (70.4)   |85| BigCodeBench |      24.5     |     40.5     |86| BigCodeBench-Hard |     5.4     |     9.5     | -->87 88 89<!-- ### Chat Model -->90|      model      | OpenCoder-1.5B-Instruct | OpenCoder-8B-Instruct |91|:---------------:|:-------------:|:------------:|92| HumanEval(+) |     72.5 (67.7)      |     83.5 (78.7)     |93| MBPP(+) |     72.7 (61.9)      |     79.1 (69.0)   |94| BigCodeBench |      33.3     |     40.3    |95| BigCodeBench-Hard |     11.5     |     16.9     |96| LiveCodeBench |     12.8     |     23.2     |97| MultiPL-E (AVG) |     57.5     |     71.0     |98 99 100## 5. Inference101 102### Inference with Huggingface's Transformers103 104```python105import torch106from transformers import AutoTokenizer, AutoModelForCausalLM107 108model_name = "infly/OpenCoder-1.5B-Instruct"109model = AutoModelForCausalLM.from_pretrained(model_name,110                                             torch_dtype=torch.bfloat16,111                                             device_map="auto",112                                             trust_remote_code=True)113tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)114 115messages=[116    { 'role': 'user', 'content': "write a quick sort algorithm in python."}117]118 119inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")120 121outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)122 123result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)124print(result)125```126 127<!-- ### Inference with vLLM (recommended) -->128 129## 6. License130 131OpenCoder series (including Base and Chat) support commercial applications under a permissive [License](https://huggingface.co/infly/OpenCoder-1.5B-Instruct/blob/main/LICENSE).132 133## 7. Citation134```135@inproceedings{Huang2024OpenCoderTO,136  title={OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models},137  author={Siming Huang and Tianhao Cheng and Jason Klein Liu and Jiaran Hao and Liuyihan Song and Yang Xu and J. Yang and J. H. Liu and Chenchen Zhang and Linzheng Chai and Ruifeng Yuan and Zhaoxiang Zhang and Jie Fu and Qian Liu and Ge Zhang and Zili Wang and Yuan Qi and Yinghui Xu and Wei Chu},138  year={2024},139  url={https://arxiv.org/pdf/2411.04905}140}141```