--- datasets: - anon8231489123/ShareGPT_Vicuna_unfiltered - ehartford/wizard_vicuna_70k_unfiltered - ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered - QingyiSi/Alpaca-CoT - teknium/GPT4-LLM-Cleaned - teknium/GPTeacher-General-Instruct - metaeval/ScienceQA_text_only - hellaswag - tasksource/mmlu - openai/summarize_from_feedback language: - en library_name: transformers pipeline_tag: text-generation inference: false --- # Manticore 13B GGML This is GGML format quantised 4bit and 5bit models of [OpenAccess AI Collective's Manticore 13B](https://huggingface.co/openaccess-ai-collective/manticore-13b). 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). ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Manticore-13B-GPTQ). * [4-bit, 5-bit 8-bit GGML models for llama.cpp CPU (+CUDA) inference](https://huggingface.co/TheBloke/Manticore-13B-GGML). * [OpenAccess AI Collective's original float16 HF format repo for GPU inference and further conversions](https://huggingface.co/openaccess-ai-collective/manticore-13b). ## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 12th 2023 - commit b9fd7ee)! llama.cpp recently made a breaking change to its quantisation methods. 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. ## Provided files | Name | Quant method | Bits | Size | RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | `manticore-13B.ggmlv2.q4_0.bin` | q4_0 | 4bit | 8.14GB | 10.5GB | 4-bit. | `manticore-13B.ggmlv2.q4_1.bin` | q5_0 | 5bit | 8.95GB | 11.0GB | 5-bit. Higher accuracy, higher resource usage and slower inference. | `manticore-13B.ggmlv2.q5_0.bin` | q5_1 | 5bit | 9.76GB | 12.25GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. | `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. | ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./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:" ``` Change `-t 8` to the number of physical CPU cores you have. ## How to run in `text-generation-webui` 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. 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). # Original Model Card: Manticore 13B - Preview Release (previously Wizard Mega) Manticore 13B is a Llama 13B model fine-tuned on the following datasets: - [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) - based on a cleaned and de-suped subset - [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered) - [Wizard-Vicuna](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered) - [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT) - [GPT4-LLM-Cleaned](https://huggingface.co/datasets/teknium/GPT4-LLM-Cleaned) - [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct) - ARC-Easy & ARC-Challenge - instruct augmented for detailed responses - mmlu: instruct augmented for detailed responses subset including - abstract_algebra - conceptual_physics - formal_logic - high_school_physics - logical_fallacies - [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses - [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses - [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization # Demo 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. - https://huggingface.co/spaces/openaccess-ai-collective/manticore-ggml ## Release Notes - https://wandb.ai/wing-lian/manticore-13b/runs/nq3u3uoh/workspace ## Build Manticore was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8xA100 80GB - Preview Release: 1 epoch taking 8 hours. - 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). ## Bias, Risks, and Limitations 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). Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information. ## Examples ```` ### Instruction: write Python code that returns the first n numbers of the Fibonacci sequence using memoization. ### Assistant: ```` ``` ### Instruction: Finish the joke, a mechanic and a car salesman walk into a bar... ### Assistant: ```