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Browse files- flan-alpaca-base/README.md +58 -0
- flan-alpaca-base/config.json +61 -0
- flan-alpaca-base/generation_config.json +7 -0
- flan-alpaca-base/gitattributes.txt +34 -0
- flan-alpaca-base/special_tokens_map.json +107 -0
- flan-alpaca-base/tokenizer.json +0 -0
- flan-alpaca-base/tokenizer_config.json +112 -0
- pytorch_model.bin +3 -0
flan-alpaca-base/README.md
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---
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license: apache-2.0
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datasets:
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- tatsu-lab/alpaca
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---
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## ๐ฎ ๐ฆ Flan-Alpaca: Instruction Tuning from Humans and Machines
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๐ฃ **FLAN-T5** is also useful in text-to-audio generation. Find our work at [https://github.com/declare-lab/tango](https://github.com/declare-lab/tango) if you are interested.
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Our [repository](https://github.com/declare-lab/flan-alpaca) contains code for extending the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
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synthetic instruction tuning to existing instruction-tuned models such as [Flan-T5](https://arxiv.org/abs/2210.11416).
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We have a [live interactive demo](https://huggingface.co/spaces/joaogante/transformers_streaming) thanks to [Joao Gante](https://huggingface.co/joaogante)!
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We are also benchmarking many instruction-tuned models at [declare-lab/flan-eval](https://github.com/declare-lab/flan-eval).
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Our pretrained models are fully available on HuggingFace ๐ค :
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| Model | Parameters | Instruction Data | Training GPUs |
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|----------------------------------------------------------------------------------|------------|----------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|
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| [Flan-Alpaca-Base](https://huggingface.co/declare-lab/flan-alpaca-base) | 220M | [Flan](https://github.com/google-research/FLAN), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 1x A6000 |
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| [Flan-Alpaca-Large](https://huggingface.co/declare-lab/flan-alpaca-large) | 770M | [Flan](https://github.com/google-research/FLAN), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 1x A6000 |
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| [Flan-Alpaca-XL](https://huggingface.co/declare-lab/flan-alpaca-xl) | 3B | [Flan](https://github.com/google-research/FLAN), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 1x A6000 |
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| [Flan-Alpaca-XXL](https://huggingface.co/declare-lab/flan-alpaca-xxl) | 11B | [Flan](https://github.com/google-research/FLAN), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 4x A6000 (FSDP) |
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| [Flan-GPT4All-XL](https://huggingface.co/declare-lab/flan-gpt4all-xl) | 3B | [Flan](https://github.com/google-research/FLAN), [GPT4All](https://github.com/nomic-ai/gpt4all) | 1x A6000 |
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| [Flan-ShareGPT-XL](https://huggingface.co/declare-lab/flan-sharegpt-xl) | 3B | [Flan](https://github.com/google-research/FLAN), [ShareGPT](https://github.com/domeccleston/sharegpt)/[Vicuna](https://github.com/lm-sys/FastChat) | 1x A6000 |
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| [Flan-Alpaca-GPT4-XL*](https://huggingface.co/declare-lab/flan-alpaca-gpt4-xl) | 3B | [Flan](https://github.com/google-research/FLAN), [GPT4-Alpaca](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) | 1x A6000 |
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*recommended for better performance
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### Why?
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[Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) represents an exciting new direction
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to approximate the performance of large language models (LLMs) like ChatGPT cheaply and easily.
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Concretely, they leverage an LLM such as GPT-3 to generate instructions as synthetic training data.
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The synthetic data which covers more than 50k tasks can then be used to finetune a smaller model.
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However, the original implementation is less accessible due to licensing constraints of the
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underlying [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) model.
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Furthermore, users have noted [potential noise](https://github.com/tloen/alpaca-lora/issues/65) in the synthetic
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dataset. Hence, it may be better to explore a fully accessible model that is already trained on high-quality (but
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less diverse) instructions such as [Flan-T5](https://arxiv.org/abs/2210.11416).
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### Usage
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```
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from transformers import pipeline
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prompt = "Write an email about an alpaca that likes flan"
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model = pipeline(model="declare-lab/flan-alpaca-gpt4-xl")
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model(prompt, max_length=128, do_sample=True)
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# Dear AlpacaFriend,
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# My name is Alpaca and I'm 10 years old.
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# I'm excited to announce that I'm a big fan of flan!
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# We like to eat it as a snack and I believe that it can help with our overall growth.
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# I'd love to hear your feedback on this idea.
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# Have a great day!
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# Best, AL Paca
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```
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flan-alpaca-base/config.json
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{
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"_name_or_path": "google/flan-t5-base",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 2048,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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"dense_act_fn": "gelu_new",
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "gated-gelu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"n_positions": 512,
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"num_decoder_layers": 12,
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"num_heads": 12,
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"summarization": {
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"early_stopping": true,
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"length_penalty": 2.0,
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"max_length": 200,
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"min_length": 30,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"prefix": "summarize: "
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},
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"translation_en_to_de": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to German: "
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},
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"translation_en_to_fr": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to French: "
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},
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"translation_en_to_ro": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to Romanian: "
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}
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},
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.27.1",
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"use_cache": true,
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"vocab_size": 32128
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}
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flan-alpaca-base/generation_config.json
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{
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"_from_model_config": true,
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.27.1"
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}
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flan-alpaca-base/gitattributes.txt
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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flan-alpaca-base/special_tokens_map.json
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{
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|
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|
96 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
106 |
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|
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flan-alpaca-base/tokenizer.json
ADDED
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flan-alpaca-base/tokenizer_config.json
ADDED
@@ -0,0 +1,112 @@
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1 |
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{
|
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|
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|
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|
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|
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|
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|
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}
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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