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--- |
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datasets: |
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- anon8231489123/ShareGPT_Vicuna_unfiltered |
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- ehartford/wizard_vicuna_70k_unfiltered |
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- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered |
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- QingyiSi/Alpaca-CoT |
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- teknium/GPT4-LLM-Cleaned |
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- teknium/GPTeacher-General-Instruct |
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- metaeval/ScienceQA_text_only |
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- hellaswag |
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- tasksource/mmlu |
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- openai/summarize_from_feedback |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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<!-- header start --> |
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<div style="width: 100%;"> |
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p> |
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<div style="display: flex; flex-direction: column; align-items: flex-end;"> |
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<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> |
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<!-- header end --> |
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# Manticore 13B GPTQ |
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This repo contains 4bit GPTQ format quantised models of [OpenAccess AI Collective's Manticore 13B](https://huggingface.co/openaccess-ai-collective/manticore-13b). |
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It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). |
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## Repositories available |
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* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Manticore-13B-GPTQ). |
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* [4-bit, 5-bit 8-bit GGML models for llama.cpp CPU (+CUDA) inference](https://huggingface.co/TheBloke/Manticore-13B-GGML). |
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* [OpenAccess AI Collective's original float16 HF format repo for GPU inference and further conversions](https://huggingface.co/openaccess-ai-collective/manticore-13b). |
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## How to easily download and use this model in text-generation-webui |
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Open the text-generation-webui UI as normal. |
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1. Click the **Model tab**. |
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2. Under **Download custom model or LoRA**, enter `TheBloke/Manticore-13B-GPTQ`. |
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3. Click **Download**. |
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4. Wait until it says it's finished downloading. |
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5. Click the **Refresh** icon next to **Model** in the top left. |
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6. In the **Model drop-down**: choose the model you just downloaded, `Manticore-13B-GPTQ`. |
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7. If you see an error in the bottom right, ignore it - it's temporary. |
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8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama` |
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9. Click **Save settings for this model** in the top right. |
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10. Click **Reload the Model** in the top right. |
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11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt! |
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## Provided files |
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**`Manticore-13B-GPTQ-4bit-128g.no-act-order.safetensors`** |
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This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility. |
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It was created without `--act-order` to ensure compatibility with all UIs out there. |
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* `Manticore-13B-GPTQ-4bit-128g.no-act-order.safetensors` |
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* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches |
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* Works with text-generation-webui one-click-installers |
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* Parameters: Groupsize = 128. No act-order. |
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* Command used to create the GPTQ: |
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``` |
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python llama.py /workspace/models/openaccess-ai-collective_manticore-13b/ wikitext2 --wbits 4 --true-sequential --groupsize 128 --save_safetensors /workspace/manticore-13b/gptq/Manticore-13B-GPTQ-4bit-128g.no-act-order.safetensors |
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``` |
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<!-- footer start --> |
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## Discord |
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For further support, and discussions on these models and AI in general, join us at: |
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[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD) |
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## Thanks, and how to contribute. |
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Thanks to the [chirper.ai](https://chirper.ai) team! |
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I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. |
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If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. |
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Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. |
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* Patreon: https://patreon.com/TheBlokeAI |
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* Ko-Fi: https://ko-fi.com/TheBlokeAI |
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**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman. |
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Thank you to all my generous patrons and donaters! |
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<!-- footer end --> |
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# Original Model Card: Manticore 13B - Preview Release (previously Wizard Mega) |
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Manticore 13B is a Llama 13B model fine-tuned on the following datasets: |
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- [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) - based on a cleaned and de-suped subset |
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- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered) |
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- [Wizard-Vicuna](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered) |
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- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT) |
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- [GPT4-LLM-Cleaned](https://huggingface.co/datasets/teknium/GPT4-LLM-Cleaned) |
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- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct) |
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- ARC-Easy & ARC-Challenge - instruct augmented for detailed responses |
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- mmlu: instruct augmented for detailed responses subset including |
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- abstract_algebra |
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- conceptual_physics |
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- formal_logic |
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- high_school_physics |
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- logical_fallacies |
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- [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses |
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- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses |
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- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization |
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# Demo |
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Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality. |
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- https://huggingface.co/spaces/openaccess-ai-collective/manticore-ggml |
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## Release Notes |
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- https://wandb.ai/wing-lian/manticore-13b/runs/nq3u3uoh/workspace |
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## Build |
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Manticore was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8xA100 80GB |
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- Preview Release: 1 epoch taking 8 hours. |
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- The configuration to duplicate this build is provided in this repo's [/config folder](https://huggingface.co/openaccess-ai-collective/manticore-13b/tree/main/configs). |
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## Bias, Risks, and Limitations |
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Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). |
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Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information. |
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## Examples |
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```` |
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### Instruction: write Python code that returns the first n numbers of the Fibonacci sequence using memoization. |
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### Assistant: |
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```` |
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``` |
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### Instruction: Finish the joke, a mechanic and a car salesman walk into a bar... |
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### Assistant: |
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``` |
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