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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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inference: false |
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--- |
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# Manticore 13B GGML |
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This is GGML format quantised 4bit and 5bit models of [OpenAccess AI Collective's Manticore 13B](https://huggingface.co/openaccess-ai-collective/manticore-13b). |
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This repo is the result of quantising to 4-bit, 5-bit and 8-bit GGML for CPU (+CUDA) inference using [llama.cpp](https://github.com/ggerganov/llama.cpp). |
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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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## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 12th 2023 - commit b9fd7ee)! |
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llama.cpp recently made a breaking change to its quantisation methods. |
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I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 12th or later (commit `b9fd7ee` or later) to use them. |
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## Provided files |
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| Name | Quant method | Bits | Size | RAM required | Use case | |
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| ---- | ---- | ---- | ---- | ---- | ----- | |
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`manticore-13B.ggmlv2.q4_0.bin` | q4_0 | 4bit | 8.14GB | 10.5GB | 4-bit. | |
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`manticore-13B.ggmlv2.q4_1.bin` | q4_1 | 4bit | 8.14GB | 10.5GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | |
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`manticore-13B.ggmlv2.q5_0.bin` | q5_0 | 5bit | 8.95GB | 11.0GB | 5-bit. Higher accuracy, higher resource usage and slower inference. | |
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`manticore-13B.ggmlv2.q5_1.bin` | q5_1 | 5bit | 9.76GB | 12.25GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. | |
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`manticore-13B.ggmlv2.q8_0.bin` | q8_0 | 8bit | 14.6GB | 17GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. | |
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## How to run in `llama.cpp` |
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I use the following command line; adjust for your tastes and needs: |
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``` |
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./main -t 8 -m manticore-13B-.ggmlv2.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: write a story about llamas ### Response:" |
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``` |
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Change `-t 8` to the number of physical CPU cores you have. |
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## How to run in `text-generation-webui` |
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GGML models can be loaded into text-generation-webui by installing the llama.cpp module, then placing the ggml model file in a model folder as usual. |
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Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). |
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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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