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--- |
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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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 |
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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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__________________________________________________________________________________________________ |
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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 |
