Quantization made by Richard Erkhov.
Manticore 30B Chat builds on Manticore v1 with new datasets, including a de-duped subset of the Pygmalion dataset. It also removes all Alpaca style prompts using
### in favor of
chat only style prompts using
USER:,
ASSISTANT: as well as
pygmalion/metharme prompting using
<|system|>, <|user|> and <|model|> tokens.
Questions, comments, feedback, looking to donate, or want to help? Reach out on our
Discord or email
wing@openaccessaicollective.orgManticore 30B Chat is a Llama 30B model fine-tuned on the following datasets along with the datasets from the original Manticore 30B.
**Manticore 30B Chat was trained on effectively 40% of the datasets below due to only training for 0.4 epochs.
Special thanks to Nanobit for helping with Axolotl, TheBloke for quantizing these models are more accessible to all, ehartford for cleaned datasets, and 0x000011b for the RP dataset.
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.
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.
