README.md

---
license: apache-2.0
tags:
- code generation
quantized_by: bartowski
pipeline_tag: text-generation
lm_studio:
  param_count: 6.7b
  use_case: coding
  release_date: 29-05-2024
  model_creator: InternLM
  prompt_template: Alpaca
  system_prompt: none
  base_model: DeepSeek-Coder-Base
  original_repo: internlm/AlchemistCoder-DS-6.7B
base_model: internlm/AlchemistCoder-DS-6.7B
---

## 💫 Community Model> AlchemistCoder DS 6.7B by InternLM

*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.

**Model creator:** [InternLM](https://huggingface.co/internlm)<br>
**Original model**: [AlchemistCoder-DS-6.7B](https://huggingface.co/internlm/AlchemistCoder-DS-6.7B)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3024](https://github.com/ggerganov/llama.cpp/releases/tag/b3024)<br>

## Model Summary:

AlchemistCoder is a series of coding models by InternLM.<br>
This model is tuned from the DeepSeek coder model, and should excel at all coding related tasks.

## Prompt template:

Choose the `Alpaca` preset in your LM Studio.

Under the hood, the model will see a prompt that's formatted like so:

```
### Instruction:
{prompt}
### Response:

```

## Technical Details

Training details:

- **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.

For more information, check out their paper here: https://arxiv.org/abs/2405.19265

## Special thanks

🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) 

🙏 Special thanks to [Kalomaze](https://github.com/kalomaze) and [Dampf](https://github.com/Dampfinchen) for their work on the dataset (linked [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)) that was used for calculating the imatrix for all sizes.

## Disclaimers

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