license: other
license_name: llama-3
license_link: https://llama.meta.com/llama3/license/
tags:
- text-generation-inference
- transformers
- unsloth
- llama
datasets:
- Replete-AI/code_bagel_hermes-2.5
- Replete-AI/code_bagel
- Replete-AI/OpenHermes-2.5-Uncensored
- teknium/OpenHermes-2.5
- layoric/tiny-codes-alpaca
- glaiveai/glaive-code-assistant-v3
- ajibawa-2023/Code-290k-ShareGPT
- TIGER-Lab/MathInstruct
- chargoddard/commitpack-ft-instruct-rated
- iamturun/code_instructions_120k_alpaca
- ise-uiuc/Magicoder-Evol-Instruct-110K
- cognitivecomputations/dolphin-coder
- nickrosh/Evol-Instruct-Code-80k-v1
- coseal/CodeUltraFeedback_binarized
- glaiveai/glaive-function-calling-v2
- CyberNative/Code_Vulnerability_Security_DPO
- jondurbin/airoboros-2.2
- camel-ai
- lmsys/lmsys-chat-1m
- CollectiveCognition/chats-data-2023-09-22
- CoT-Alpaca-GPT4
- WizardLM/WizardLM_evol_instruct_70k
- WizardLM/WizardLM_evol_instruct_V2_196k
- teknium/GPT4-LLM-Cleaned
- GPTeacher
- OpenGPT
- meta-math/MetaMathQA
- Open-Orca/SlimOrca
- garage-bAInd/Open-Platypus
- anon8231489123/ShareGPT_Vicuna_unfiltered
- Unnatural-Instructions-GPT4
model-index:
- name: Replete-Coder-llama3-8b
results:
- task:
name: HumanEval
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.6468383584267833
verified: true
- task:
name: AI2 Reasoning Challenge
type: text-generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: accuracy
value: null
name: normalized accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: accuracy
value: null
name: normalized accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: accuracy
value: null
name: accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: multiple_choice_accuracy
value: null
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: accuracy
value: null
name: accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: accuracy
value: null
name: accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
Greetings friends, Asalamu Alaikum; I am pleased to provide you with GGUF vresions of this great model! The original model is: Replete-AI/Replete-Coder-Llama3-8B by Replete-AI with the finetuning conducted by rombo dawg.
Description (per TheBloke)
This repo contains GGUF format model files.
These files were quantised using ggml-org/gguf-my-repo [https://huggingface.co/spaces/ggml-org/gguf-my-repo]
About GGUF (per TheBloke)
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
- llama.cpp. The source project for GGUF. Offers a CLI and a server option.
- text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
- KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
- GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
- LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
- LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
- Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
- llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
- candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
- ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
Replete-Coder-llama3-8b
Finetuned by: Rombodawg
More than just a coding model!
Although Replete-Coder has amazing coding capabilities, its trained on vaste amount of non-coding data, fully cleaned and uncensored. Dont just use it for coding, use it for all your needs! We are truly trying to make the GPT killer!
Thank you to TensorDock for sponsoring Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b you can check out their website for cloud compute rental below.
Replete-Coder-llama3-8b is a general purpose model that is specially trained in coding in over 100 coding languages. The data used to train the model contains 25% non-code instruction data and 75% coding instruction data totaling up to 3.9 million lines, roughly 1 billion tokens, or 7.27gb of instruct data. The data used to train this model was 100% uncensored, then fully deduplicated, before training happened.
The Replete-Coder models (including Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b) feature the following:
- Advanced coding capabilities in over 100 coding languages
- Advanced code translation (between languages)
- Security and vulnerability prevention related coding capabilities
- General purpose use
- Uncensored use
- Function calling
- Advanced math use
- Use on low end (8b) and mobile (1.5b) platforms
Notice: Replete-Coder series of models are fine-tuned on a context window of 8192 tokens. Performance past this context window is not guaranteed.
You can find the 25% non-coding instruction below:
And the 75% coding specific instruction data below:
These two datasets were combined to create the final dataset for training, which is linked below:
Prompt Template: Custom Alpaca
### System:
{}
### Instruction:
{}
### Response:
{}
Note: The system prompt varies in training data, but the most commonly used one is:
Below is an instruction that describes a task, Write a response that appropriately completes the request.
End token:
<|endoftext|>
Thank you to the community for your contributions to the Replete-AI/code_bagel_hermes-2.5 dataset. Without the participation of so many members making their datasets free and open source for any to use, this amazing AI model wouldn't be possible.
Extra special thanks to Teknium for the Open-Hermes-2.5 dataset and jondurbin for the bagel dataset and the naming idea for the code_bagel series of datasets. You can find both of their huggingface accounts linked below:
Another special thanks to unsloth for being the main method of training for Replete-Coder. Bellow you can find their github, as well as the special Replete-Ai secret sause (Unsloth + Qlora + Galore) colab code document that was used to train this model.
- https://github.com/unslothai/unsloth
- https://colab.research.google.com/drive/1VAaxMQJN9-78WLsPU0GWg5tEkasXoTP9?usp=sharing