README.md

metadata

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

Chat & support: my new Discord server

Want to contribute? Patreon coming soon!

Manticore 13B GPTQ

This repo contains 4bit GPTQ format quantised models of OpenAccess AI Collective's Manticore 13B.

It is the result of quantising to 4bit using GPTQ-for-LLaMa.

Repositories available

• 4-bit GPTQ models for GPU inference.
• 4-bit, 5-bit 8-bit GGML models for llama.cpp CPU (+CUDA) inference.
• OpenAccess AI Collective's original float16 HF format repo for GPU inference and further conversions.

How to easily download and use this model in text-generation-webui

Open the text-generation-webui UI as normal.

1. Click the Model tab.
2. Under Download custom model or LoRA, enter TheBloke/Manticore-13B-GPTQ.
3. Click Download.
4. Wait until it says it's finished downloading.
5. Click the Refresh icon next to Model in the top left.
6. In the Model drop-down: choose the model you just downloaded, Manticore-13B-GPTQ.
7. If you see an error in the bottom right, ignore it - it's temporary.
8. Fill out the GPTQ parameters on the right: Bits = 4, Groupsize = 128, model_type = Llama
9. Click Save settings for this model in the top right.
10. Click Reload the Model in the top right.
11. Once it says it's loaded, click the Text Generation tab and enter a prompt!

Provided files

Manticore-13B-GPTQ-4bit-128g.no-act-order.safetensors

This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility.

It was created without --act-order to ensure compatibility with all UIs out there.

• Manticore-13B-GPTQ-4bit-128g.no-act-order.safetensors
  • Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
  • Works with text-generation-webui one-click-installers
  • Parameters: Groupsize = 128. No act-order.
  • Command used to create the GPTQ:

    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
    

Want to support my work?

I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills.

So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI projects.

Donaters will get priority support on any and all AI/LLM/model questions, and I'll gladly quantise any model you'd like to try.

• Patreon: coming soon! (just awaiting approval)
• Ko-Fi: https://ko-fi.com/TheBlokeAI
• Discord: https://discord.gg/UBgz4VXf

Original Model Card: Manticore 13B - Preview Release (previously Wizard Mega)

Manticore 13B is a Llama 13B model fine-tuned on the following datasets:

• ShareGPT - based on a cleaned and de-suped subset
• WizardLM
• Wizard-Vicuna
• subset of QingyiSi/Alpaca-CoT for roleplay and CoT
• GPT4-LLM-Cleaned
• 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 - 5K row subset of instruct augmented for concise responses
• metaeval/ScienceQA_text_only - instruct for concise responses
• 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 on 8xA100 80GB

• Preview Release: 1 epoch taking 8 hours.
• The configuration to duplicate this build is provided in this repo's /config folder.

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: