Llama 3.1 Leaks: SoTA Open Model 405B & What We Know So Far
TLDR: 8B gets a big bump across the board, 70B instruct shows minor improvements, and 405B is the SoTA open model. But 405B still lags behind flagship models.
Here are the notable upgrades:
- Every model now supports 128k context length (up from 8k)
- Trained on a massive ~15T tokens of public data
- Fine-tuning data includes publicly available instruction datasets and over 25M synthetically generated examples
- Multilingual support for 7 languages: French, German, Hindi, Italian, Portuguese, Spanish, and Thai
- Training required a whopping 39.3M GPU hours on H100-80GB: 1.5m for 8B, 7m for 70B, and 31M for 405B
Meta’s benchmarks are in, but we’re still waiting for the verdict.
Check out the leaked model card for yourself.
A Peek Into Llama 3 405B#
A list of base model files with their size:
405B is a dense model release instead of Mixture of experts (MoE).
config.json:
{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": 128001,
"hidden_act": "silu",
"hidden_size": 16384,
"initializer_range": 0.02,
"intermediate_size": 53248,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 128,
"num_hidden_layers": 126,
"num_key_value_heads": 16,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.42.3",
"use_cache": true,
"vocab_size": 128256
}
generation_config.json:
{
"_from_model_config": true,
"bos_token_id": 128000,
"eos_token_id": 128001,
"transformers_version": "4.42.3"
}
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}
Original README.md:
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣠⣴⣶⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⢻⣿⣿⣿⣷⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⣿⣿⣿⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣀⣀⠀⠀⠀⠀⠀⠀⠀⠘⣿⣿⣿⣿⣿⣶⣶⡶⢶⣶⣶⣦⣤⣄⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣤⠶⠶⠛⠛⠉⠉⠉⠉⠛⠛⠳⢶⣤⣄⠀⠀⠹⣿⣿⣿⣿⣿⣿⣾⡱⢆⡲⢤⠉⡍⢛⠻⠷⣦⣄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣴⣤⣤⣀⣀⡀⠀⠀⠀⠀⠀⠀⠀⣀⣠⣤⡶⠶⢻⠛⠛⠋⢉⠀⠠⠀⠄⠠⠐⠀⠂⠀⠄⢀⠀⡀⢈⠙⡻⣷⣤⣿⣿⣿⣿⣿⣿⣿⣿⣯⣖⡣⡝⡰⢃⠎⡴⢈⡙⠻⢷⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⡿⠛⠛⠿⠿⣷⣶⣦⣤⠶⠟⢋⡉⢄⠢⡑⢢⢉⡔⢣⠎⠬⠣⠜⠲⠄⠢⠁⠤⠁⡄⢂⠰⢠⠡⠌⡐⠌⡹⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣷⣹⣕⢫⡜⡰⢃⠬⡑⢢⡘⡙⢳⣦⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⣿⠇⠀⠀⠀⠀⣠⣿⠟⣋⡔⣩⢒⡜⡢⢇⡙⢂⠡⢈⠀⡀⠂⠀⠄⡀⠄⠐⠀⠄⠐⠀⠈⠁⡁⢂⠐⡀⢂⠁⢚⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣾⣿⣾⣱⣋⢖⡉⢆⠰⡁⠆⣈⠙⢷⣦⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⣿⣷⣾⣿⣶⡾⡟⠧⠛⠤⠙⡄⢋⡌⣁⠂⡐⠄⡃⠆⢡⢀⠃⠌⡀⠄⡐⠈⡀⠂⢈⠀⠡⠐⠀⠠⠐⠀⠂⠌⠠⡘⣿⣿⣿⣿⣿⣿⣿⣿⡉⠛⠛⠿⠿⣿⣿⣮⣽⣊⢦⠁⢆⠠⢈⠀⡈⠻⢦⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣼⣿⣿⣿⣿⣿⡿⢋⡁⢒⠠⢣⢌⠳⡌⢇⡒⡄⢆⡱⢌⡰⢉⠢⡘⢌⠢⡑⠢⢄⠡⡐⢈⠀⡐⠀⠂⠁⠄⠂⠁⠌⠠⠁⡔⠩⢿⣿⣿⣿⣿⣿⣿⣇⠀⠀⠀⠀⠀⠀⠉⠙⠻⣷⣏⠦⡘⢄⠂⠄⡀⠂⠙⢧⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣿⣿⣿⣿⡟⢣⠜⡤⢓⡌⣎⠵⣊⠷⣘⢦⢳⡘⢦⡑⢎⡔⣡⢂⠐⡀⢂⠁⠣⢌⠢⣁⠣⡘⣀⠂⡁⠌⠀⠌⠐⡀⢁⠂⠠⠑⡌⢛⣿⣿⣿⣿⣿⣿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠈⠻⣶⣍⠢⠜⠠⠐⠀⠡⠀⠻⣦⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣸⣿⣿⣿⠧⡜⣡⠚⣔⢣⡞⡬⢣⣙⢮⣓⢮⡱⢎⡇⡞⣡⠚⡔⣊⠖⡰⠀⠌⢀⠂⠐⡀⠆⣁⠢⠑⡄⢂⠡⠐⢀⠐⠠⢀⠁⢂⠘⠤⡹⣿⣿⣿⣿⣿⣿⠆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠻⣷⡌⠱⡈⠄⠡⠐⢀⠈⢷⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⣿⢏⡴⠣⡍⢶⣙⢮⠳⡡⢎⡝⣾⡹⣌⠧⣊⠵⣑⢋⠖⣡⢋⡔⣃⠒⠤⡈⢀⠂⢁⠠⠉⠄⢂⠁⠎⡰⢀⠌⠠⠀⠌⠐⡈⠐⡠⢃⢿⣿⣿⡿⠟⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠻⣷⣆⠱⡀⢂⠠⠈⡀⠈⢿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⢏⠖⣡⠓⣜⢣⣞⡉⢲⠡⣏⡜⢧⡻⣌⡳⣜⢢⡑⠎⡜⠤⢃⠲⢌⡩⢆⠱⢠⠐⠀⡀⠂⢈⠠⢈⠐⢠⠃⠎⡄⢈⠠⠁⠰⣁⠰⢈⠚⣿⣿⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⢿⣦⠑⢄⡀⢂⠀⡁⠀⠹⣦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣾⣿⣿⡾⠁⢧⠹⣜⢯⢖⡩⢆⡝⢤⣋⠷⣹⠶⣱⢎⢷⣸⠱⣌⠊⡅⢣⠘⡰⢈⠆⡡⠊⠔⡀⠐⠀⡀⠄⠈⠂⠌⠒⡌⢲⡀⠌⠐⢠⠃⡬⢘⡹⣿⣷⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢻⣏⣆⠐⠠⢀⠐⠈⢀⠘⣧⡀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⢿⣿⢃⡍⢶⡙⣮⣿⢌⠳⡌⡜⢦⡩⢞⡽⣟⢦⣋⠞⣜⡻⢶⣭⣐⠂⢆⠁⠢⠘⡠⠑⠂⠌⠤⣁⠀⠠⠀⠁⠠⠑⡈⠦⡑⢎⡀⠂⠱⢌⠣⡜⣹⣿⣿⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣿⣿⣿⡦⠀⡐⠈⠀⠄⠘⣷⡀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⢣⡜⣣⢝⣺⣿⣎⠵⡘⡜⢦⠱⣋⡼⣿⣧⢏⡞⣥⢛⢧⣛⢿⣿⣦⣌⠀⠡⢀⠡⠘⡐⠂⠄⠚⢤⡐⠈⢀⠐⠀⠡⢉⠖⡱⡌⡑⢪⠱⡌⡽⣿⣿⣿⣦⣄⣀⠀⠀⠀⠀⠀⠀⠀⠀⢠⡆⠀⢰⡿⠛⣿⣿⣟⠀⠄⡈⠄⢈⠀⡘⣧⡀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣏⠶⣹⢆⡏⣶⣿⣿⢢⡙⠜⣢⠓⡬⢒⣿⣿⣿⡜⣧⢫⠖⡭⢞⡼⣛⡿⣷⣦⣄⠀⢂⠠⠁⠎⡐⢂⠘⢢⠀⠄⡈⠐⠈⢎⠵⢣⠜⣠⠛⣜⡹⣿⣿⣿⣿⣿⣿⠃⠀⠀⠀⠀⠀⠀⠀⢰⣇⠀⣾⠃⠀⣼⣿⡃⢌⠠⢀⠐⠀⠄⠠⠘⣷⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣾⣿⡎⣽⠲⣍⠞⣶⢻⣿⣧⡘⠜⣠⠙⠴⡉⢖⣿⣿⣿⣾⣧⣿⣘⢧⡚⣥⢫⡝⡻⢿⣿⣦⣀⠂⠄⠡⠈⡜⠠⢍⢢⠐⠈⡀⢊⠜⣱⢚⠤⡛⡴⡹⣿⣿⣿⣿⢋⣿⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣾⠏⢀⡿⠀⢻⣿⠴⣦⡄⠂⢁⠐⠀⡁⢹⣧⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣿⣿⣜⠲⣏⠼⣹⣾⣏⡿⣿⣎⠒⠤⢉⠆⡉⠎⡴⢻⣿⣿⣉⠛⠛⠿⣷⣶⣧⣞⣭⢓⢮⡛⣿⣿⣴⣂⠡⡐⡉⢆⢢⡙⡔⢠⠂⡘⢥⣋⠶⣡⢳⡹⣿⡿⢉⣿⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⣼⢻⡟⠀⢨⠃⠀⢈⡟⠀⢘⣿⡄⠂⠠⠁⠠⠀⢻⣆⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⣎⠷⡸⡑⢮⣿⡾⣝⡿⣿⣏⠦⡁⢎⡐⢁⠂⠧⢻⣿⡆⠄⠀⠀⠀⠈⠉⠛⠻⠿⣶⣿⣴⣭⣛⣿⣷⣼⡰⣌⢢⠱⢌⢣⡜⠤⣃⢮⠳⡥⢎⣳⣿⣷⣿⣿⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⣿⣸⠇⠀⠀⠀⠀⣌⣷⣴⡿⠛⠁⠠⠁⠠⠁⡐⠈⣿⡄⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⣿⡚⣥⠃⣞⣿⣿⡽⣞⣿⣿⣷⣑⠂⡌⠤⢈⠐⠡⢚⣿⣆⠀⠀⠀⠀⠀⠀⠀⣠⡾⠋⠉⠙⣻⣿⣿⣿⣿⣾⣧⣗⢮⡱⢎⡳⣌⢎⠷⣙⢮⣿⣿⣿⣿⣿⣿⠇⠀⠀⠀⠀⠀⠀⠀⢰⡏⡌⠀⠀⠀⢀⣴⠋⢻⣯⠠⠁⠌⠐⡀⠁⡐⠀⡐⠘⣷⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⣿⣟⠴⣃⠸⣿⣿⣿⣟⣾⣿⡿⠿⣷⣮⡐⡡⢌⠂⡅⠚⣿⣧⠀⠀⠀⠀⠰⠞⠋⠀⢀⣠⣾⣿⠟⠉⠀⠉⠙⠻⢿⣿⣿⣭⣷⣹⣎⢯⡳⢾⣿⣿⣿⣿⡿⠋⠀⠀⠀⠀⠀⠀⠀⠀⣾⠁⠀⠀⠀⣴⠟⠁⠀⢸⣿⠠⢉⠐⠠⢀⠁⡀⠂⠄⡈⢽⡆⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣾⣿⣿⣿⣿⣽⢢⡝⣽⣿⣿⣿⣿⣿⣿⣅⡀⠙⠿⣷⣌⣒⢌⡱⢂⠿⣷⣀⠀⠀⠀⠀⢀⣴⣾⠿⠋⠀⠀⠀⠀⠀⠀⠀⣼⣿⡿⢿⣿⣷⣯⣷⣻⣿⣿⣿⡿⠛⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠇⠀⠀⣠⡾⠁⠀⠀⠀⢸⣿⠐⠂⠌⡐⠀⠄⠐⡀⠂⠄⠘⣷⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⣿⣿⣿⣿⣿⣿⣗⡞⣜⣻⢿⣿⣿⣿⡙⠿⠿⣷⣶⣬⣙⣿⢷⣮⣇⣎⡹⢿⣆⠀⠀⠀⣿⣿⣷⣶⣶⣶⣶⣶⣶⣾⡿⠉⠀⣿⡟⡽⣹⣿⢿⣿⣷⣿⡿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡿⠀⠀⣰⠋⠀⠀⠀⠀⠀⢨⣿⢈⡁⠆⢠⠁⠌⢀⠐⠠⢈⠐⢿⡆⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⣿⣿⣿⣿⣿⣿⣿⣿⡬⢏⡿⣽⣻⣿⣿⣄⠄⠀⠈⠙⢛⣿⣿⣯⠙⠛⠻⠿⠿⠛⠀⠀⠀⠉⠁⠉⠈⠀⠁⠀⡀⠄⡐⢂⠲⣿⣯⢳⣽⡇⠀⠹⡟⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣼⠃⢀⡼⠁⠀⠀⠀⠀⠀⠀⠰⣿⠠⡐⠈⠤⡈⠄⠂⢈⠐⠠⡈⢼⣇⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣼⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣝⡾⣱⣟⣿⣿⣿⡶⣬⣶⣿⣿⠿⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠰⣏⡐⢠⠂⠁⣿⣿⣳⣿⣿⣶⣾⠆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⠏⢀⡾⠁⠀⠀⠀⠀⠀⠀⠀⢸⣿⠠⢁⠜⣀⠒⡈⠐⡀⠌⠠⠁⠆⣿⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣳⣻⢾⣿⣿⣿⡟⣿⣉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⠀⠀⠀⠀⠀⠀⠉⠉⠁⠀⣠⡿⢻⡟⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣤⣤⣤⣰⡟⠀⡞⠁⠀⠀⠀⠀⠀⠀⠀⠀⣸⣏⠐⠌⡰⢀⠣⡐⢁⠠⠈⠄⠃⡌⢿⡇
⠀⠀⠀⠀⠀⠀⠀⠀⢠⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣯⣟⣿⣿⣿⣿⣞⢻⣧⠀⠀⠀⠀⢀⣤⣤⡴⡶⠶⠛⢏⡙⢻⣦⡀⠀⠀⠀⠀⠀⠀⣰⣿⡀⠛⠁⠀⠀⠀⠀⠀⠀⢀⣀⣤⣶⣿⣿⣿⠟⠉⠿⠇⠀⠘⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⡇⠌⢢⠑⡌⢢⠑⡂⠄⡈⠄⡃⠜⣸⡇
⠀⠀⠀⠀⠀⠀⠀⣠⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣧⠿⣦⣀⠀⠀⠈⢿⣇⡲⢄⠣⡉⢆⠸⢠⠹⣷⠀⠀⠀⣠⣴⣿⣿⣿⣿⣷⣤⣀⣤⣠⣤⣴⣾⣿⣿⣿⣿⣿⣿⣿⣦⠄⠀⠀⠀⢀⡀⠀⠀⠀⠀⠀⠀⠀⠀⣸⣿⢂⡑⢢⠱⡨⢅⡊⠔⠠⠐⡈⠔⣩⢸⡇
⠀⠀⠀⠀⠀⠀⣰⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣻⣿⡿⠿⣿⡇⠀⠉⢻⣦⣤⣈⠛⠿⣿⣶⣭⣦⢿⠶⢉⣡⣤⣶⣿⣿⠿⣹⣿⡿⣧⠩⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⠀⠀⠀⠀⠀⠀⣧⠀⠀⠀⠀⠀⠀⠀⢀⣿⢇⠢⡘⢤⢣⡑⢎⠴⣉⢂⠐⢄⠣⢆⣻⡇
⠀⠀⠀⠀⠀⣼⣿⣿⣟⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡷⣯⣿⡇⠀⠈⠙⠀⣠⠟⠇⠉⠹⢿⣷⣶⣴⣶⣶⣶⣾⣿⣿⣿⡝⣲⣬⣾⣟⣿⣯⡑⠘⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡤⡄⣀⡀⢀⣼⡟⠀⠀⠀⠀⠀⠀⢀⣾⠟⡄⢣⠘⣆⢣⢜⣊⠖⡰⡈⠔⡨⣘⠢⣿⠇
⠀⠀⠀⢀⣾⣿⣟⣷⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣟⣷⣿⣇⡀⠀⠀⣠⡟⠄⠶⣤⣄⣸⣿⣿⣷⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡟⠁⢻⣿⡅⢢⡽⠿⠿⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡏⠀⠀⣼⣶⣿⡿⠁⠀⠀⠀⠀⠀⢠⣾⠏⡜⢄⠣⣍⠲⣍⠶⣩⠞⣡⠱⡈⠴⢠⠣⣿⠀
⠀⠀⢀⣾⣿⣻⣾⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣟⣾⣿⣿⣿⣿⣿⣿⣿⣿⣦⣤⣼⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠀⠀⠘⣿⣿⣹⡇⠀⠀⠀⠀⠀⠀⠀⠉⠉⠙⠛⠋⠀⠀⠰⠿⠟⠋⠀⠀⠀⠀⠀⠀⣴⣿⠏⡜⡰⢊⠵⣨⠳⣬⢓⢧⡛⣤⢣⢡⢋⠦⣹⡟⠀
⠀⠀⣾⣿⣳⣯⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣻⣾⡿⠏⠁⠀⠈⠻⣿⣿⣿⣿⣿⣿⡿⠋⠉⣿⣿⣿⣿⣿⣿⣿⣿⣇⠀⠸⣿⡀⠀⠀⣿⣿⣿⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣾⣟⢣⢛⡴⢡⣋⠶⣡⢟⡲⢏⡮⡵⣊⢆⠣⢎⠖⣽⠇⠀
⠀⣼⣿⣿⣳⣿⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣷⣿⣿⠁⠀⠀⠀⠀⠀⢈⠉⣿⣿⡟⠉⠀⠀⠀⠈⢿⣿⣿⣿⣟⢾⣻⣿⡀⠀⠈⠉⣉⣤⣿⠿⣿⣿⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣾⡿⣏⡜⣣⢳⣜⣳⢬⠳⣍⠾⣭⢻⡼⣱⢣⢎⡱⢎⢾⡟⠀⠀
⢠⣿⣟⣾⣿⣽⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣷⣿⣿⣦⡀⠀⠀⠀⠀⣠⣶⣿⠃⠀⠀⠀⠀⠀⠀⢸⡷⣿⣿⢎⡷⣹⣿⡇⠈⠉⠛⠉⠁⠀⠀⢹⠟⣿⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣤⣶⣿⡟⣯⢵⡺⣜⣷⣻⣾⣟⢮⡝⢮⡝⣮⣳⡝⣧⢋⠶⣩⢞⣿⠁⠀⠀
⢸⣿⣿⣻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡾⣿⣿⣿⣿⣷⣤⣴⣿⣿⠟⠁⠀⠀⠀⠀⠀⠀⠀⣾⢇⣿⣟⡞⣴⢣⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣿⣧⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠉⠛⠿⣿⠿⠿⢿⣿⣿⣿⣿⣛⢮⣜⢧⣛⣶⢻⡼⣣⢏⡞⣥⡿⠃⠀⠀⠀
⢹⣿⣿⣿⡟⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⢿⣽⣿⣿⠟⠻⠟⠛⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⠀⣿⡿⣜⡖⣏⣿⣿⡀⠄⢈⠀⠀⠀⠀⠀⠀⠀⠹⣿⣿⢷⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣼⣿⣿⣿⣽⡹⣞⡼⣏⣷⢫⣿⣱⠳⣎⣾⡟⠁⠀⠀⠀⠀
⢸⣿⣿⡿⠀⠈⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡾⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⢿⡎⣿⣿⣳⢞⡵⣺⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠻⣿⣎⣝⣻⣷⣤⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣿⣿⣿⣿⣳⢏⣷⣹⢾⡽⣞⣯⢖⣯⣿⡾⠋⠀⠀⠀⠀⠀⠀
⠀⢻⣿⡇⠀⠀⠘⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣽⣿⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⡟⠁⠻⣿⣿⣿⣿⣯⡞⡵⣿⣷⠀⠀⡀⣠⣤⣤⡠⠜⠻⣅⠂⢈⣿⣿⣿⣿⣯⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣼⣿⣿⣿⣻⢾⣝⡾⣣⣟⣯⣟⢷⣮⣿⠾⠃⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠻⣇⠀⠀⠀⠙⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⠏⠀⠀⣾⣿⣿⣿⣿⡿⣽⡹⣿⣿⣶⣡⠛⣝⣻⣿⣿⣦⡥⢌⣳⡿⣿⣿⢹⣾⣿⣷⣦⡀⠀⠀⠀⠀⠀⢀⣴⣿⣿⡿⣯⢷⣻⡟⣮⢷⣻⣞⣷⣾⠿⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠈⠀⠀⠀⠀⠘⣿⣿⣿⣿⣿⣿⣿⣿⣿⣾⣿⡿⠟⠋⠀⠹⣿⣿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣿⡀⠀⠘⠑⣿⣿⣿⣿⠽⣶⣹⢿⣿⣏⠻⣿⣌⢿⣽⣿⣿⣽⣿⣩⣾⣯⣽⣿⣿⣿⣿⣿⣿⣷⣄⠀⣠⣾⣿⣿⣿⣯⣿⣯⣿⣧⣿⡿⠿⠛⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢿⣿⣿⣿⣿⣿⣿⠿⠛⠁⠀⠀⠀⠀⠀⠈⠙⠻⠂⠀⠀⠀⠀⠀⠀⠀⠀⢀⣼⣷⣴⣁⣶⡿⣿⣿⣭⣟⡶⣯⣿⣿⣿⣷⣌⣻⣿⣾⣿⢿⣿⡍⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⠁⠹⣿⠛⠛⠛⠛⠛⠛⠉⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⢿⣿⣿⣿⡿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣾⣿⣿⣿⣿⣏⣵⣿⣿⣿⣽⣷⣿⣿⣿⣿⣿⣿⣯⣿⣿⣯⡌⣿⣷⣿⣿⡿⣏⠙⢻⣿⣿⡟⠁⠀⠀⣿⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠹⣿⡿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣤⣿⣿⣿⡟⢻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣧⣾⣿⣿⣿⣿⣿⣿⣿⣿⣼⣿⣿⣿⣟⣶⣾⣿⡿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠛⠿⣿⣿⢀⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡏⠁⠀⠈⠉⠛⠛⠛⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢻⣹⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣏⣭⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠟⠛⠛⠛⠙⣿⣿⣿⣿⣿⣿⣿⣿⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡇⠀⠀⠀⠀⣿⣿⣿⣿⣿⣿⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢻⣿⣿⣿⣿⣿⣿⠿⠿⢿⡿⠃⠀⠀⠀⠀⣿⣿⣿⣿⣿⣿⣿⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠛⠛⠛⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⣿⣿⣿⣿⣿⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⣿⣿⣿⣿⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣿⣿⣿⣿⣿⣿⡟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣾⣿⣿⣿⣽⣿⡿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⠉⠛⠛⠉⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
index 5c0c57f3e..f94a4cb37 100644
--- a/src/transformers/models/llama/modeling_llama.py
+++ b/src/transformers/models/llama/modeling_llama.py
@@ -73,6 +73,29 @@ class LlamaRMSNorm(nn.Module):
ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
+def apply_scaling(freqs: torch.Tensor):
+ # Values obtained from grid search
+ scale_factor = 8
+ low_freq_factor = 1
+ high_freq_factor = 4
+ old_context_len = 8192 # original llama3 length
+
+ low_freq_wavelen = old_context_len / low_freq_factor
+ high_freq_wavelen = old_context_len / high_freq_factor
+ new_freqs = []
+ for freq in freqs:
+ wavelen = 2 * math.pi / freq
+ if wavelen < high_freq_wavelen:
+ new_freqs.append(freq)
+ elif wavelen > low_freq_wavelen:
+ new_freqs.append(freq / scale_factor)
+ else:
+ assert low_freq_wavelen != high_freq_wavelen
+ smooth = (old_context_len / wavelen - low_freq_factor) / (
+ high_freq_factor - low_freq_factor
+ )
+ new_freqs.append((1 - smooth) * freq / scale_factor + smooth * freq)
+ return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device)
class LlamaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
@@ -82,6 +105,7 @@ class LlamaRotaryEmbedding(nn.Module):
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+ inv_freq = apply_scaling(inv_freq)
self.register_buffer("inv_freq", inv_freq, persistent=False)
# For BC we register cos and sin cached
self.max_seq_len_cached = max_position_embeddings
special_tokens_map.json:
{
"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
}
}
tokenizer.json:
{
"version": "1.0",
"truncation": null,
"padding": null,
"added_tokens": [
{
"id": 128000,
"content": "<|begin_of_text|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
{
"id": 128001,
"content": "<|end_of_text|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
{
"id": 128002,
"content": "<|reserved_special_token_0|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
{
"id": 128003,
"content": "<|reserved_special_token_1|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
{
"id": 128004,
"content": "<|finetune_right_pad_id|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
{"... truncated ...": "..." },
{
"id": 128255,
"content": "<|reserved_special_token_247|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
}
],
"normalizer": null,
"pre_tokenizer": {
"type": "Sequence",
"pretokenizers": [
{
"type": "Split",
"pattern": {
"Regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\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+"
},
"behavior": "Isolated",
"invert": false
},
{
"type": "ByteLevel",
"add_prefix_space": false,
"trim_offsets": true,
"use_regex": false
}
]
},
"post_processor": {
"type": "ByteLevel",
"add_prefix_space": true,
"trim_offsets": false,
"use_regex": true
},
"decoder": {
"type": "ByteLevel",
"add_prefix_space": true,
"trim_offsets": true,
"use_regex": true
},
"model": {
"type": "BPE",
"dropout": null,
"unk_token": null,
"continuing_subword_prefix": null,
"end_of_word_suffix": null,
"fuse_unk": false,
"byte_fallback": false,
"ignore_merges": true,
"vocab": {
"!": 0,
"\"": 1,
"#": 2,
"$": 3,
"%": 4,
"&": 5,
"'": 6,
"(": 7,
")": 8,
"*": 9,
"+": 10,
",": 11,
"-": 12,
".": 13,
"/": 14,
"0": 15,
"1": 16,
"2": 17,
"3": 18,
"4": 19,
"5": 20,
"6": 21,
"7": 22,
"8": 23,
"9": 24,
":": 25,
";": 26,
"<": 27,
"=": 28,
">": 29,
"?": 30,
"@": 31,
"A": 32,
"B": 33,
"C": 34,
"D": 35,
"E": 36,
"F": 37,
"G": 38,
"H": 39,
"I": 40,
"J": 41,
"K": 42,
"L": 43,
"M": 44,
"N": 45,
"O": 46,
"P": 47,
"Q": 48,
"R": 49,
"S": 50,
"T": 51,
"U": 52,
"V": 53,
"W": 54,
"X": 55,
"Y": 56,
"Z": 57,
"[": 58,
"\\": 59,
"]": 60,
"^": 61,
"_": 62,
"`": 63,
"a": 64,
"b": 65,
"c": 66,
"d": 67,
"e": 68,
"f": 69,
"g": 70,
"h": 71,
"i": 72,
"j": 73,
"k": 74,
"l": 75,
"m": 76,
"n": 77,
"o": 78,
"p": 79,
"q": 80,
"r": 81,
"s": 82,
"t": 83,
"u": 84,
"v": 85,
"w": 86,
"x": 87,
"y": 88,
"z": 89,
"{": 90,
"|": 91,
"}": 92,
"~": 93,
"¡": 94,
"¢": 95,
"£": 96,
"¤": 97,
"¥": 98,
"¦": 99,
"§": 100,
"¨": 101,
"©": 102,
"ª": 103,
"«": 104,
"¬": 105,
"®": 106,
"¯": 107,
"°": 108,
"±": 109,
"²": 110,
"³": 111,
"´": 112,
"µ": 113,
"¶": 114,
"·": 115,
"¸": 116,
"¹": 117,
"º": 118,
"»": 119,
"¼": 120,
"... truncated ...": "...",
"ĠĠĠĠĠ ĠĠĠ",
"ĠĠĠĠĠĠ ĠĠ",
"e r",
"Ġ ĠĠ",
"ĠĠ Ġ",
"o n",
"Ġ a",
"r e",
"a t",
"s t",
"e n",
"o r",
"Ġ th",
"Ġt h",
"Ċ Ċ",
"Ġ c",
"l e",
"Ġ s",
"i t",
"a n",
"a r",
"a l",
"Ġ the",
"............................................................................................................",
"ĠвÑĭÑģок ой",
"ãĥ¼ ãĥ¼",
"ãĥ¼ãĥ ¼",
"éĶ ¦"
]
}
}
tokenizer_config.json:
{
"added_tokens_decoder": {
"128000": {
"content": "<|begin_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128001": {
"content": "<|end_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128002": {
"content": "<|reserved_special_token_0|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128003": {
"content": "<|reserved_special_token_1|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128004": {
"content": "<|finetune_right_pad_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"... truncated ...": { "... truncated ...": "..." },
"128254": {
"content": "<|reserved_special_token_246|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128255": {
"content": "<|reserved_special_token_247|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|end_of_text|>",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"tokenizer_class": "PreTrainedTokenizerFast"
}
Original text: GitHub Gist