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
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Model creator: InternLM
Original model: AlchemistCoder-DS-6.7B
GGUF quantization: provided by bartowski based on llama.cpp release b3024
Model Summary:
AlchemistCoder is a series of coding models by InternLM.
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 and the whole team working on llama.cpp
🙏 Special thanks to Kalomaze and Dampf for their work on the dataset (linked here) that was used for calculating the imatrix for all sizes.
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