Instructions to use aevynt/JupiMind-Dense-J1R-Tokenizer-24k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aevynt/JupiMind-Dense-J1R-Tokenizer-24k with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aevynt/JupiMind-Dense-J1R-Tokenizer-24k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload JupiMind Vietnamese tokenizer
Browse files- README.md +21 -21
- jupimind_tokenizer_report.json +13 -13
README.md
CHANGED
|
@@ -16,23 +16,23 @@ model_type: tokenizer
|
|
| 16 |
|
| 17 |
# aevynt/JupiMind-Dense-J1R-Tokenizer-24k
|
| 18 |
|
| 19 |
-
##
|
| 20 |
|
| 21 |
-
|
| 22 |
|
| 23 |
-
-
|
| 24 |
-
- technical
|
| 25 |
- command line
|
| 26 |
- logs
|
| 27 |
-
- JSON
|
| 28 |
-
- tool-call
|
| 29 |
|
| 30 |
-
Tokenizer
|
| 31 |
|
| 32 |
-
##
|
| 33 |
|
| 34 |
-
-
|
| 35 |
-
- byte fallback:
|
| 36 |
- vocab size: 24576
|
| 37 |
- context target: 4096
|
| 38 |
- special chat/tool tokens:
|
|
@@ -53,26 +53,26 @@ Seed text inputs:
|
|
| 53 |
|
| 54 |
## Compression benchmark
|
| 55 |
|
| 56 |
-
|
| 57 |
|
| 58 |
-
- candidate avg tokens/doc:
|
| 59 |
-
- baseline avg tokens/doc:
|
| 60 |
-
- improvement: 3.
|
| 61 |
-
- candidate avg chars/token: 3.
|
| 62 |
- baseline avg chars/token: 1.15
|
| 63 |
|
| 64 |
-
##
|
| 65 |
|
| 66 |
-
Tokenizer
|
| 67 |
|
| 68 |
-
|
| 69 |
|
| 70 |
-
1.
|
| 71 |
-
2. pretrain
|
| 72 |
|
| 73 |
## Open Source Files
|
| 74 |
|
| 75 |
-
Repo
|
| 76 |
|
| 77 |
- `tokenizer.json`
|
| 78 |
- `tokenizer_config.json`
|
|
|
|
| 16 |
|
| 17 |
# aevynt/JupiMind-Dense-J1R-Tokenizer-24k
|
| 18 |
|
| 19 |
+
## Tong quan
|
| 20 |
|
| 21 |
+
Day la tokenizer rieng cho dong **JupiMind Dense J1R**. Muc tieu cua no la phuc vu:
|
| 22 |
|
| 23 |
+
- tieng Viet co dau
|
| 24 |
+
- technical va server text
|
| 25 |
- command line
|
| 26 |
- logs
|
| 27 |
+
- JSON, YAML, config
|
| 28 |
+
- tool-call va reasoning tags
|
| 29 |
|
| 30 |
+
Tokenizer nay duoc train de thay the hoan toan baseline cu von nen tieng Viet kem.
|
| 31 |
|
| 32 |
+
## Thiet ke
|
| 33 |
|
| 34 |
+
- loai: byte-level BPE
|
| 35 |
+
- byte fallback: bat
|
| 36 |
- vocab size: 24576
|
| 37 |
- context target: 4096
|
| 38 |
- special chat/tool tokens:
|
|
|
|
| 53 |
|
| 54 |
## Compression benchmark
|
| 55 |
|
| 56 |
+
Tren sample evaluation:
|
| 57 |
|
| 58 |
+
- candidate avg tokens/doc: 702.29
|
| 59 |
+
- baseline avg tokens/doc: 2331.64
|
| 60 |
+
- improvement: 3.32x it token hon baseline
|
| 61 |
+
- candidate avg chars/token: 3.83
|
| 62 |
- baseline avg chars/token: 1.15
|
| 63 |
|
| 64 |
+
## Dung voi JupiMind
|
| 65 |
|
| 66 |
+
Tokenizer nay **khong tuong thich** voi checkpoint cu dung `vocab_size=6400`.
|
| 67 |
|
| 68 |
+
Muon dung that can:
|
| 69 |
|
| 70 |
+
1. cap nhat model config sang `vocab_size=24576`
|
| 71 |
+
2. pretrain lai tu dau hoac continued pretrain tu checkpoint cung vocab
|
| 72 |
|
| 73 |
## Open Source Files
|
| 74 |
|
| 75 |
+
Repo nay bao gom:
|
| 76 |
|
| 77 |
- `tokenizer.json`
|
| 78 |
- `tokenizer_config.json`
|
jupimind_tokenizer_report.json
CHANGED
|
@@ -27,26 +27,26 @@
|
|
| 27 |
"evaluation": {
|
| 28 |
"candidate": {
|
| 29 |
"name": "j1r_vi_bytebpe",
|
| 30 |
-
"documents":
|
| 31 |
-
"avg_tokens_per_doc":
|
| 32 |
-
"avg_chars_per_token": 3.
|
| 33 |
-
"avg_bytes_per_token": 5.
|
| 34 |
-
"total_tokens":
|
| 35 |
},
|
| 36 |
"baseline": {
|
| 37 |
"name": "jingyaogong/minimind-3",
|
| 38 |
-
"documents":
|
| 39 |
-
"avg_tokens_per_doc":
|
| 40 |
-
"avg_chars_per_token": 1.
|
| 41 |
-
"avg_bytes_per_token": 1.
|
| 42 |
-
"total_tokens":
|
| 43 |
}
|
| 44 |
},
|
| 45 |
"recommended_model_vocab_size": 24576,
|
| 46 |
"recommended_tokenizer_path": "aevynt/JupiMind-Dense-J1R-Tokenizer-24k",
|
| 47 |
"notes": [
|
| 48 |
-
"Tokenizer
|
| 49 |
-
"Tokenizer
|
| 50 |
-
"
|
| 51 |
]
|
| 52 |
}
|
|
|
|
| 27 |
"evaluation": {
|
| 28 |
"candidate": {
|
| 29 |
"name": "j1r_vi_bytebpe",
|
| 30 |
+
"documents": 50000,
|
| 31 |
+
"avg_tokens_per_doc": 702.28854,
|
| 32 |
+
"avg_chars_per_token": 3.8265045874164487,
|
| 33 |
+
"avg_bytes_per_token": 5.042613795178831,
|
| 34 |
+
"total_tokens": 35114427
|
| 35 |
},
|
| 36 |
"baseline": {
|
| 37 |
"name": "jingyaogong/minimind-3",
|
| 38 |
+
"documents": 50000,
|
| 39 |
+
"avg_tokens_per_doc": 2331.64386,
|
| 40 |
+
"avg_chars_per_token": 1.1525389301949398,
|
| 41 |
+
"avg_bytes_per_token": 1.518829672384015,
|
| 42 |
+
"total_tokens": 116582193
|
| 43 |
}
|
| 44 |
},
|
| 45 |
"recommended_model_vocab_size": 24576,
|
| 46 |
"recommended_tokenizer_path": "aevynt/JupiMind-Dense-J1R-Tokenizer-24k",
|
| 47 |
"notes": [
|
| 48 |
+
"Tokenizer nay dung byte-level BPE voi byte fallback de xu ly tieng Viet co dau, log, code, JSON, YAML va output he thong.",
|
| 49 |
+
"Tokenizer moi khong tuong thich embedding voi checkpoint vocab_size=6400 cu. Muon dung that phai pretrain lai voi vocab_size moi.",
|
| 50 |
+
"Thiet ke uu tien server-first assistant, khong toi uu cho tieng Trung."
|
| 51 |
]
|
| 52 |
}
|