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Model: matsuo-lab/weblab-10b-instruction-sft Source: Original Platform
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README.md
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---
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license: cc-by-nc-4.0
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---
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# weblab-10b-instruction-sft
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# Overview
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This repository provides a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.
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* **Library**
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The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).
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* **Model architecture**
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A 36-layer, 4864-hidden-size transformer-based language model.
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* **Pre-training**
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The model was trained on around **600B** tokens from a mixture of the following corpora.
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- [Japanese C4](https://huggingface.co/datasets/mc4)
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- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
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* **Instruction-supervised-finetuning**
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The model was finetuned on a subset records from a mixture of the following dataset. Training epoch: 1.
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- [Alpaca (English)](https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json)
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- [Alpaca (Japanese translation)](https://github.com/shi3z/alpaca_ja/blob/main/alpaca_cleaned_ja.json)
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- [Flan 2021 (English)](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original)
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- [Flan CoT (English)](https://huggingface.co/datasets/conceptofmind/cot_submix_original)
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- [Flan Dialog (English)](https://huggingface.co/datasets/conceptofmind/dialog_submix_original)
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* **Model Series**
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| Variant | Link |
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| :-- | :--|
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| weblab-10b-instruction-sft | https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft |
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| weblab-10b | https://huggingface.co/matsuo-lab/weblab-10b |
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* **Authors**
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Takeshi Kojima
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---
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# Benchmarking
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* **Japanese benchmark : JGLUE 8-task (2023-08-27)**
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- *We used [Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/2f1583c0735eacdfdfa5b7d656074b69577b6774) library for evaluation.*
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- *The 8-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, JSQuAD-1.1, jaqket_v2-0.2, xlsum_ja-1.0, xwinograd_ja, and mgsm-1.0.*
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- *model loading is performed with float16, and evaluation is performed with template version 0.3 using the few-shot in-context learning.*
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- *The number of few-shots is 3,3,3,2,1,1,0,5.*
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- *special_tokens_map.json is modified to avoid errors during the evaluation of the second half benchmarks. As a result, the results of the first half benchmarks became slightly different.*
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model | average | jcommonsenseqa | jnli | marc_ja | jsquad | jaqket_v2 | xlsum_ja | xwinograd_ja | mgsm
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| :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- |
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weblab-10b-instruction-sft | 59.11 | 74.62 | 66.56 | 95.49 | 78.34 | 63.32 | 20.57 | 71.95 | 2
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weblab-10b | 50.74 | 66.58 | 53.74 | 82.07 | 62.94 | 56.19 | 10.03 | 71.95 | 2.4
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* **Japanese benchmark : JGLUE 4-task (2023-08-18)**
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- *We used [Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/2f1583c0735eacdfdfa5b7d656074b69577b6774) library for evaluation.*
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- *The 4-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, and JSQuAD-1.1.*
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- *model loading is performed with float16, and evaluation is performed with template version 0.3 using the few-shot in-context learning.*
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- *The number of few-shots is 3,3,3,2.*
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| Model | Average | JCommonsenseQA | JNLI | MARC-ja | JSQuAD |
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| :-- | :-- | :-- | :-- | :-- | :-- |
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| weblab-10b-instruction-sft | 78.78 | 74.35 | 65.65 | 96.06 | 79.04 |
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| weblab-10b | 66.38 | 65.86 | 54.19 | 84.49 | 60.98 |
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---
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# How to use the model
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~~~~python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b-instruction-sft")
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model = AutoModelForCausalLM.from_pretrained("matsuo-lab/weblab-10b-instruction-sft", torch_dtype=torch.float16)
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if torch.cuda.is_available():
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model = model.to("cuda")
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text = "大規模言語モデルについて説明してください。"
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text = f'以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n{text}\n\n### 応答:'
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token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
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with torch.no_grad():
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output_ids = model.generate(
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token_ids.to(model.device),
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max_new_tokens=100,
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do_sample=True,
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temperature=0.7,
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top_p=0.95
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)
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output = tokenizer.decode(output_ids.tolist()[0])
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print(output)
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~~~~
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---
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# Licenese
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[cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/)
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config.json
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{
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"architectures": [
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"GPTNeoXForCausalLM"
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],
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "gpt_neox",
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"num_attention_heads": 38,
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"num_hidden_layers": 36,
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"rotary_emb_base": 10000,
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"rotary_pct": 0.25,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.28.1",
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"use_cache": true,
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"use_parallel_residual": true,
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"vocab_size": 50277
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}
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"gpt_neox.layers.5.input_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.5.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.5.mlp.dense_4h_to_h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.5.mlp.dense_4h_to_h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.5.mlp.dense_h_to_4h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.5.mlp.dense_h_to_4h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.5.post_attention_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.5.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.attention.dense.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.attention.dense.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.attention.query_key_value.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.attention.query_key_value.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.attention.rotary_emb.inv_freq": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.input_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.mlp.dense_4h_to_h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.mlp.dense_4h_to_h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.mlp.dense_h_to_4h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.mlp.dense_h_to_4h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.post_attention_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.6.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.attention.dense.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.attention.dense.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.attention.query_key_value.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.attention.query_key_value.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.attention.rotary_emb.inv_freq": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.input_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.mlp.dense_4h_to_h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.mlp.dense_4h_to_h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.mlp.dense_h_to_4h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.mlp.dense_h_to_4h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.post_attention_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.7.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.attention.dense.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.attention.dense.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.attention.query_key_value.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.attention.query_key_value.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.attention.rotary_emb.inv_freq": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.input_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.mlp.dense_4h_to_h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.mlp.dense_4h_to_h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.mlp.dense_h_to_4h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.mlp.dense_h_to_4h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.post_attention_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.attention.dense.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.attention.dense.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.attention.query_key_value.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.attention.query_key_value.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.attention.rotary_emb.inv_freq": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.input_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.mlp.dense_4h_to_h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.mlp.dense_4h_to_h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.mlp.dense_h_to_4h.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.mlp.dense_h_to_4h.weight": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.post_attention_layernorm.bias": "pytorch_model-00001-of-00003.bin",
|
||||
"gpt_neox.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin"
|
||||
}
|
||||
}
|
||||
6
special_tokens_map.json
Normal file
6
special_tokens_map.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"bos_token": "<|endoftext|>",
|
||||
"eos_token": "<|endoftext|>",
|
||||
"pad_token": "<|padding|>",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
100529
tokenizer.json
Normal file
100529
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
5
tokenizer_config.json
Normal file
5
tokenizer_config.json
Normal file
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"tokenizer_class": "PreTrainedTokenizerFast"
|
||||
}
|
||||
Reference in New Issue
Block a user