230 lines
7.4 KiB
Markdown
230 lines
7.4 KiB
Markdown
---
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license: other
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license_name: llama-3
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license_link: https://llama.meta.com/llama3/license/
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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datasets:
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- Replete-AI/code_bagel_hermes-2.5
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- Replete-AI/code_bagel
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- Replete-AI/OpenHermes-2.5-Uncensored
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- teknium/OpenHermes-2.5
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- layoric/tiny-codes-alpaca
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- glaiveai/glaive-code-assistant-v3
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- ajibawa-2023/Code-290k-ShareGPT
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- TIGER-Lab/MathInstruct
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- chargoddard/commitpack-ft-instruct-rated
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- iamturun/code_instructions_120k_alpaca
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- ise-uiuc/Magicoder-Evol-Instruct-110K
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- cognitivecomputations/dolphin-coder
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- nickrosh/Evol-Instruct-Code-80k-v1
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- coseal/CodeUltraFeedback_binarized
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- glaiveai/glaive-function-calling-v2
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- CyberNative/Code_Vulnerability_Security_DPO
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- jondurbin/airoboros-2.2
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- camel-ai
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- lmsys/lmsys-chat-1m
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- CollectiveCognition/chats-data-2023-09-22
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- CoT-Alpaca-GPT4
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- WizardLM/WizardLM_evol_instruct_70k
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- WizardLM/WizardLM_evol_instruct_V2_196k
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- teknium/GPT4-LLM-Cleaned
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- GPTeacher
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- OpenGPT
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- meta-math/MetaMathQA
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- Open-Orca/SlimOrca
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- garage-bAInd/Open-Platypus
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- anon8231489123/ShareGPT_Vicuna_unfiltered
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- Unnatural-Instructions-GPT4
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model-index:
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- name: Replete-Coder-llama3-8b
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results:
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- task:
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name: HumanEval
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type: text-generation
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dataset:
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type: openai_humaneval
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name: HumanEval
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metrics:
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- name: pass@1
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type: pass@1
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value: .64683835842678326
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verified: True
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- task:
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name: AI2 Reasoning Challenge
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type: text-generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: accuracy
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value:
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name: normalized accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: accuracy
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value:
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name: normalized accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: accuracy
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value:
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name: accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: multiple_choice_accuracy
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value:
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: accuracy
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value:
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name: accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: accuracy
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value:
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name: accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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---
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# Replete-Coder-llama3-8b
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Finetuned by: Rombodawg
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### More than just a coding model!
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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!
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Thank you to TensorDock for sponsoring Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b
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you can check out their website for cloud compute rental below.
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- https://tensordock.com
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__________________________________________________________________________________________________
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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.
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The Replete-Coder models (including Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b) feature the following:
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- Advanced coding capabilities in over 100 coding languages
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- Advanced code translation (between languages)
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- Security and vulnerability prevention related coding capabilities
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- General purpose use
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- Uncensored use
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- Function calling
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- Advanced math use
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- Use on low end (8b) and mobile (1.5b) platforms
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Notice: Replete-Coder series of models are fine-tuned on a context window of 8192 tokens. Performance past this context window is not guaranteed.
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__________________________________________________________________________________________________
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You can find the 25% non-coding instruction below:
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- https://huggingface.co/datasets/Replete-AI/OpenHermes-2.5-Uncensored
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And the 75% coding specific instruction data below:
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- https://huggingface.co/datasets/Replete-AI/code_bagel
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These two datasets were combined to create the final dataset for training, which is linked below:
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- https://huggingface.co/datasets/Replete-AI/code_bagel_hermes-2.5
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__________________________________________________________________________________________________
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## Prompt Template: Custom Alpaca
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```
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### System:
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{}
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### Instruction:
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{}
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### Response:
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{}
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```
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Note: The system prompt varies in training data, but the most commonly used one is:
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```
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Below is an instruction that describes a task, Write a response that appropriately completes the request.
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```
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End token:
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```
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<|endoftext|>
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```
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__________________________________________________________________________________________________
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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.
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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:
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- https://huggingface.co/teknium
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- https://huggingface.co/jondurbin
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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.
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- https://github.com/unslothai/unsloth
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- https://colab.research.google.com/drive/1VAaxMQJN9-78WLsPU0GWg5tEkasXoTP9?usp=sharing
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__________________________________________________________________________________________________
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## Join the Replete-Ai discord! We are a great and Loving community!
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- https://discord.gg/ZZbnsmVnjD |