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--- |
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license: apache-2.0 |
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base_model: Replete-AI/Replete-Coder-Qwen2-1.5b |
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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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- qwen2 |
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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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--- |
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# Quant Infos |
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- quants done with an importance matrix for improved quantization loss |
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- ggufs & imatrix generated from bf16 for "optimal" accuracy loss |
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- Wide coverage of different gguf quant types from Q\_8\_0 down to IQ1\_S |
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- Quantized with [llama.cpp](https://github.com/ggerganov/llama.cpp) commit [4bfe50f741479c1df1c377260c3ff5702586719e](https://github.com/ggerganov/llama.cpp/commit/4bfe50f741479c1df1c377260c3ff5702586719e) (master as of 2024-06-11) |
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- Imatrix generated with [this](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) multi-purpose dataset by [bartowski](https://huggingface.co/bartowski). |
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``` |
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./imatrix -c 512 -m $model_name-bf16.gguf -f calibration_datav3.txt -o $model_name.imatrix |
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``` |
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# Original Model Card: |
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# Replete-Coder-Qwen2-1.5b |
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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 bellow. |
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- https://tensordock.com |
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__________________________________________________________________________________________________ |
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Replete-Coder-Qwen2-1.5b 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: ChatML |
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``` |
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<|im_start|>system |
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{}<|im_end|> |
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<|im_start|>user |
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{}<|im_end|> |
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<|im_start|>assistant |
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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/1eXGqy5M--0yW4u0uRnmNgBka-tDk2Li0?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 |
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