license: apache-2.0
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
[🤗 HuggingFace] [📃 Paper] [🌐 Project Page]
✨ Highlights
Abstract: Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance. However, previous Code LLMs are typically fine-tuned on single-source data with limited quality and diversity, which may insufficiently elicit the potential of pre-trained Code LLMs. In this paper, we present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. To achieve this, we pioneer to unveil inherent conflicts among the various styles and qualities in multi-source code corpora and introduce data-specific prompts with hindsight relabeling, termed AlchemistPrompts, to harmonize different data sources and instruction-response pairs. Additionally, we propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review. Extensive experiments demonstrate that AlchemistCoder holds a clear lead among all models of the same size (6.7B/7B) and rivals or even surpasses larger models (15B/33B/70B), showcasing the efficacy of our method in refining instruction-following capabilities and advancing the boundaries of code intelligence.
- AlchemistPrompts: Designed as data-specific prompts for harmonizing inherent conflicts in multi-source data and mitigating the instruction/response misalignment at a fined-grained level.
- Code Comprehenstion Tasks: Sourced from the process of data construction, consisting of instruction evolution, data filtering, and code review.
- Harmonized Multi-source Data: Instruction tuned on 200M tokens, including 6 types of high-quality data.
- Superior Model Performance: Surpassing all the open-source models of the same size (6.7/7B), and rivaling or even beating larger models (15B/33B/70B/ChatGPT) on 6 code benchmarks.
- Advanced generic capabilities: Demonstrated by the significant improvements on MMLU, BBH, and GSM8K.
🚀 Quick Start
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("internlm/AlchemistCoder-CL-7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("internlm/AlchemistCoder-CL-7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
model = model.eval()
input_text = "Implement the Dijkstra algorithm in Python"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🧪 Evaluation and Fine-tune
Please refer to AlchemistCoder and InternLM.
😃 Acknowledgments
AlchemistCoder is built with InternLM and OpenCompass. Thanks for their awesome work!
📧 Contact
If you have any questions, please create an issue on this repository or contact us at:
🌟 Citation
If you find our work useful, please consider citing: