Instructions to use jeduardogruiz/Mixtral_ether with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use jeduardogruiz/Mixtral_ether with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("fill-in-model-name") model.load_adapter("jeduardogruiz/Mixtral_ether", set_active=True) - Notebooks
- Google Colab
- Kaggle
| from tiktoken.load import data_gym_to_mergeable_bpe_ranks, load_tiktoken_bpe | |
| ENDOFTEXT = "<|endoftext|>" | |
| FIM_PREFIX = "<|fim_prefix|>" | |
| FIM_MIDDLE = "<|fim_middle|>" | |
| FIM_SUFFIX = "<|fim_suffix|>" | |
| ENDOFPROMPT = "<|endofprompt|>" | |
| def gpt2(): | |
| mergeable_ranks = data_gym_to_mergeable_bpe_ranks( | |
| vocab_bpe_file="https://openaipublic.blob.core.windows.net/gpt-2/encodings/main/vocab.bpe", | |
| encoder_json_file="https://openaipublic.blob.core.windows.net/gpt-2/encodings/main/encoder.json", | |
| vocab_bpe_hash="1ce1664773c50f3e0cc8842619a93edc4624525b728b188a9e0be33b7726adc5", | |
| encoder_json_hash="196139668be63f3b5d6574427317ae82f612a97c5d1cdaf36ed2256dbf636783", | |
| ) | |
| return { | |
| "name": "gpt2", | |
| "explicit_n_vocab": 50257, | |
| # The pattern in the original GPT-2 release is: | |
| # r"""'s|'t|'re|'ve|'m|'ll|'d| ?[\p{L}]+| ?[\p{N}]+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" | |
| # This is equivalent, but executes faster: | |
| "pat_str": r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", | |
| "mergeable_ranks": mergeable_ranks, | |
| "special_tokens": {ENDOFTEXT: 50256}, | |
| } | |
| def r50k_base(): | |
| mergeable_ranks = load_tiktoken_bpe( | |
| "https://openaipublic.blob.core.windows.net/encodings/r50k_base.tiktoken", | |
| expected_hash="306cd27f03c1a714eca7108e03d66b7dc042abe8c258b44c199a7ed9838dd930", | |
| ) | |
| return { | |
| "name": "r50k_base", | |
| "explicit_n_vocab": 50257, | |
| "pat_str": r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", | |
| "mergeable_ranks": mergeable_ranks, | |
| "special_tokens": {ENDOFTEXT: 50256}, | |
| } | |
| def p50k_base(): | |
| mergeable_ranks = load_tiktoken_bpe( | |
| "https://openaipublic.blob.core.windows.net/encodings/p50k_base.tiktoken", | |
| expected_hash="94b5ca7dff4d00767bc256fdd1b27e5b17361d7b8a5f968547f9f23eb70d2069", | |
| ) | |
| return { | |
| "name": "p50k_base", | |
| "explicit_n_vocab": 50281, | |
| "pat_str": r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", | |
| "mergeable_ranks": mergeable_ranks, | |
| "special_tokens": {ENDOFTEXT: 50256}, | |
| } | |
| def p50k_edit(): | |
| mergeable_ranks = load_tiktoken_bpe( | |
| "https://openaipublic.blob.core.windows.net/encodings/p50k_base.tiktoken", | |
| expected_hash="94b5ca7dff4d00767bc256fdd1b27e5b17361d7b8a5f968547f9f23eb70d2069", | |
| ) | |
| special_tokens = {ENDOFTEXT: 50256, FIM_PREFIX: 50281, FIM_MIDDLE: 50282, FIM_SUFFIX: 50283} | |
| return { | |
| "name": "p50k_edit", | |
| "pat_str": r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", | |
| "mergeable_ranks": mergeable_ranks, | |
| "special_tokens": special_tokens, | |
| } | |
| def cl100k_base(): | |
| mergeable_ranks = load_tiktoken_bpe( | |
| "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken", | |
| expected_hash="223921b76ee99bde995b7ff738513eef100fb51d18c93597a113bcffe865b2a7", | |
| ) | |
| special_tokens = { | |
| ENDOFTEXT: 100257, | |
| FIM_PREFIX: 100258, | |
| FIM_MIDDLE: 100259, | |
| FIM_SUFFIX: 100260, | |
| ENDOFPROMPT: 100276, | |
| } | |
| return { | |
| "name": "cl100k_base", | |
| "pat_str": r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+""", | |
| "mergeable_ranks": mergeable_ranks, | |
| "special_tokens": special_tokens, | |
| } | |
| ENCODING_CONSTRUCTORS = { | |
| "gpt2": gpt2, | |
| "r50k_base": r50k_base, | |
| "p50k_base": p50k_base, | |
| "p50k_edit": p50k_edit, | |
| "cl100k_base": cl100k_base, | |
| } | |