Quantization made by Richard Erkhov.

notHumpback-M1 - GGUF

base_model: meta-llama/Llama-3.2-3B library_name: transformers model_name: notHumpback-M1 tags:

This model follows the Humpback architecture, proposed in the paper Self-Alignment with Instruction Backtranslation by Li et al.

It represents the resulting model after the first iteration of self-curation, which is trained on a small amount of gold data and a set of generated data curated by the "seed model".

This model can be used for instruction-following. It may also be used to, again, score the instruction-response pairs generated by the "backward model" for a second iteration of self-curation.

Humpback uses instruction backtranslation on a web corpus to generate input-output pairs (self-augmentation), creating a richer dataset for fine-tuning models without the need for additional manual annotation. The model then iteratively curates the created dataset, scoring the pairs by quality, and is then finetuned on the resulting subset of all pairs with the highest possible score (self-curation).

Varying from the original paper, this model is a fine-tuned version of meta-llama/Llama-3.2-3B. It has been trained using TRL.

The dataset used to train this model is a combination of data sampled from the oasst1 dataset and the synthetic dataset which was mentioned above. The latter has been created by applying self-augmentation and self-curation on 502k entries from the english subset ("en") of the c4 dataset.

For comparison with other methods, the training dataset was limited to 16000 instruction-response pairs.

Original paper:

Cite TRL as: