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

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: