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metadata
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

This is the meta-llama/Meta-Llama-3-8B-Instruct model with the Replete-AI/Replete-Coder-Llama3-8B adapter applied on top of it.

This is mostly an experinment to see how the model would perform.

Links to the oringal model and adapter are bellow:

Orginal model:

Adapter:

  • Coming soon

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! image/png

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.

image/png


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.


Join the Replete-Ai discord! We are a great and Loving community!