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--- |
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license: apache-2.0 |
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tags: |
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- code generation |
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base_model: internlm/AlchemistCoder-L-7B |
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pipeline_tag: text-generation |
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--- |
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# QuantFactory/AlchemistCoder-L-7B-GGUF |
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This is quantized version of [internlm/AlchemistCoder-L-7B](https://huggingface.co/internlm/AlchemistCoder-L-7B) created using llama.cpp |
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## Model Description: AlchemistCoder |
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[[📃 Paper](https://arxiv.org/abs/2405.19265)] |
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[[🌐 Project Page](https://internlm.github.io/AlchemistCoder/)] |
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## ✨ Highlights |
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> **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.* |
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- **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. |
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- **Code Comprehenstion Tasks**: Sourced from the process of data construction, consisting of instruction evolution, data filtering, and code review. |
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- **Harmonized Multi-source Data**: Instruction tuned on 200M tokens, including 6 types of high-quality data. |
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- **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. |
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- **Advanced generic capabilities**: Demonstrated by the significant improvements on MMLU, BBH, and GSM8K. |
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## 🚀 Quick Start |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("internlm/AlchemistCoder-L-7B", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("internlm/AlchemistCoder-L-7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() |
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model = model.eval() |
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input_text = "Implement the Dijkstra algorithm in Python" |
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_length=128) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## 🧪 Evaluation and Fine-tune |
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Please refer to [**AlchemistCoder**](https://github.com/InternLM/AlchemistCoder) and [**InternLM**](https://github.com/InternLM/InternLM/tree/main). |
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## 😃 Acknowledgments |
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*AlchemistCoder* is built with [**InternLM**](https://github.com/InternLM) and [**OpenCompass**](https://github.com/open-compass). Thanks for their awesome work! |
