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Model: Alepach/notHumpback-M1-Rw-F-8b Source: Original Platform
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README.md
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---
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base_model: meta-llama/Llama-3.1-8B
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library_name: transformers
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model_name: notHumpback-M1-Rw-F-8b
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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-Rw-F-8b
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This model follows roughly 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. An additional improvement, primarily inspired by the paper [Better Alignment with Instruction Back-and-Forth Translation](https://arxiv.org/abs/2408.04614) by Nguyen et al.,
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is added at the end of the original pipeline.
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The original 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. For this, the documents from the web corpus are treated as theoretical responses,
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for which then matching instructions are generated.
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A copy of the base model, instruction-tuned on a small amount of "gold" instruction-response pairs, 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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The pipeline by Nguyen et al. adds a third step called "Rewriting". During this step an already aligned LLM (e.g. LLaMa-2-70B-chat) is employed to rewrite those responses
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that have passed the filtering at the self-curation step. The rewriting improves the linguistic quality of the responses, due to the nature of web-sourced texts, often containing colloquialisms
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and stylistic noise. The final model is then finetuned on the rewritten dataset.
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This approach inspired me to also add a rewriting step, performed not by an already aligned external LLM, but by the
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["seed model"](https://huggingface.co/Alepach/notHumpback-M0), that also performs the filtering (self-curation). This approach intends to bring back the idea of
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"Self-Alignment", since using an external model for rewriting deviates from the "self" aspect. In my pipeline the "self-rewriting" step is performed before self-curation,
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so that the quality of the pairs is ensured after rewriting, allowing for more candidate pairs to be taken into consideration during filtering. This can be important for
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leveraging the amount of data used, since some web documents have messy structure and would get filtered out when performing filtering first. The rewriting could potentially
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restructure the response and thereby increase its quality and chance to be included in the final training data, potentially allowing for a greater, more diverse
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final training dataset.
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This model represents the resulting model after the first iteration of the pipeline, which is trained on a small amount of gold data
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and a set of generated data rewritten and 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, rewrite and 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 the pipeline.
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Varying from the original paper, this model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
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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, self-rewriting 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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- TRL: 0.12.1
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- Transformers: 4.46.3
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- Pytorch: 2.5.1
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- Datasets: 3.1.0
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- Tokenizers: 0.20.3
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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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Inspiration:
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```bibtex
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@misc{nguyen2024betteralignmentinstructionbackandforth,
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title={Better Alignment with Instruction Back-and-Forth Translation},
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author={Thao Nguyen and Jeffrey Li and Sewoong Oh and Ludwig Schmidt and Jason Weston and Luke Zettlemoyer and Xian Li},
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year={2024},
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eprint={2408.04614},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2408.04614},
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}
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```
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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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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config.json
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"rope_type": "llama3"
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.3",
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"use_cache": false,
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"vocab_size": 128256
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}
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"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|finetune_right_pad_id|>"
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:76cfe2f054560aae896b2b75e273dc97a39e304d4ad19c44a9727a1d6b33c4cc
|
||||
size 17210021
|
||||
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fde7b1ae9951d46a88a1f24d2d387e64f6d08e5f1c5033afad151e6ca771b924
|
||||
size 5560
|
||||
Reference in New Issue
Block a user