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notHumpback-M1

Text Generation
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llama
Generated from Trainer
trl
sft
conversational
text-generation-inference
Inference Endpoints
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  1. README.md +17 -33
  2. config.json +2 -2
  3. model-00001-of-00002.safetensors +1 -1
  4. model-00002-of-00002.safetensors +1 -1
  5. model.safetensors.index.json +255 -255
  6. tokenizer.json +2 -2
  7. training_args.bin +1 -1
README.md CHANGED
@@ -6,35 +6,30 @@ tags:
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  - generated_from_trainer
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  - trl
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  - sft
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- license: apache-2.0
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- datasets:
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- - OpenAssistant/oasst1
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- - allenai/c4
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  ---
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- # notHumpback-M1
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- This model follows the Humpback architecture, proposed in the paper [Self-Alignment with Instruction Backtranslation](https://arxiv.org/pdf/2308.06259)
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- by Li et al.
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- It represents the resulting model after the first iteration of self-curation, which is trained on a small amount of gold data
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- and a set of generated data curated by the ["seed model"](https://huggingface.co/Alepach/notHumpback-M0).
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- This model can be used for instruction-following.
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- It may also be used to, again, score the instruction-response pairs
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- generated by the ["backward model"](https://huggingface.co/Alepach/notHumpback-Myx) for a second iteration of self-curation.
 
 
 
 
 
 
 
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- Humpback uses instruction backtranslation on a web corpus to generate input-output pairs (self-augmentation),
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- creating a richer dataset for fine-tuning models without the need for additional manual annotation.
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- The model then iteratively curates the created dataset, scoring the pairs by quality, and is then finetuned on the resulting subset
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- of all pairs with the highest possible score (self-curation).
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- Varying from the original paper, this model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B).
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- It has been trained using [TRL](https://github.com/huggingface/trl).
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- The dataset used to train this model is a combination of data sampled from the [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1)
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- dataset and the synthetic dataset which was mentioned above. The latter has been created by applying self-augmentation and self-curation
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- on 502k entries from the english subset ("en") of the [c4](https://huggingface.co/datasets/allenai/c4) dataset.
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  ### Framework versions
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@@ -46,18 +41,7 @@ on 502k entries from the english subset ("en") of the [c4](https://huggingface.c
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  ## Citations
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- Original paper:
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- ```bibtex
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- @misc{li2023selfalignment,
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- title={Self-Alignment with Instruction Backtranslation},
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- author={Xian Li and Ping Yu and Chunting Zhou and Timo Schick and Luke Zettlemoyer and Omer Levy and Jason Weston and Mike Lewis},
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- year={2023},
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- eprint={2308.06259},
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- archivePrefix={arXiv},
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- primaryClass={cs.CL}
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- }
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- ```
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  Cite TRL as:
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@@ -70,4 +54,4 @@ Cite TRL as:
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  publisher = {GitHub},
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  howpublished = {\url{https://github.com/huggingface/trl}}
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  }
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- ```
 
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  - generated_from_trainer
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  - trl
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  - sft
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+ licence: license
 
 
 
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  ---
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+ # Model Card for notHumpback-M1
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+ This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B).
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+ It has been trained using [TRL](https://github.com/huggingface/trl).
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+ ## Quick start
 
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+ ```python
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+ from transformers import pipeline
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+
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+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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+ generator = pipeline("text-generation", model="Alepach/notHumpback-M1", device="cuda")
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+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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+ print(output["generated_text"])
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+ ```
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+
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+ ## Training procedure
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+ This model was trained with SFT.
 
 
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  ### Framework versions
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  ## Citations
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  Cite TRL as:
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  publisher = {GitHub},
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  howpublished = {\url{https://github.com/huggingface/trl}}
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  }
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+ ```
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