Create README.md

README.md

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+---
+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
+base_model: Replete-AI/Llama3-8B-Instruct-Replete-Adapted
+pipeline_tag: text-generation
+---
+
+# QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF
+This is quantized version of [Replete-AI/Llama3-8B-Instruct-Replete-Adapted](https://huggingface.co/Replete-AI/Llama3-8B-Instruct-Replete-Adapted) created using llama.cpp
+
+# Model Description
+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:
+
+- https://huggingface.co/Replete-AI/Replete-Coder-Llama3-8B
+
+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](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/-0dERC793D9XeFsJ9uHbx.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. 
+- https://tensordock.com
+__________________________________________________________________________________________________
+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](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/C-zxpY5n8KuzQeocmhk0g.png)
+__________________________________________________________________________________________________
+You can find the 25% non-coding instruction below:
+
+-  https://huggingface.co/datasets/Replete-AI/OpenHermes-2.5-Uncensored
+
+And the 75% coding specific instruction data below:
+
+- https://huggingface.co/datasets/Replete-AI/code_bagel
+
+These two datasets were combined to create the final dataset for training, which is linked below:
+
+- https://huggingface.co/datasets/Replete-AI/code_bagel_hermes-2.5
+__________________________________________________________________________________________________
+## 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:
+
+- https://huggingface.co/teknium
+- https://huggingface.co/jondurbin
+
+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