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Browse files- README.md +64 -3
- config.json +34 -0
- mergekit_moe_config.yml +350 -0
- model-00001-of-00006.safetensors +3 -0
- model-00002-of-00006.safetensors +3 -0
- model-00003-of-00006.safetensors +3 -0
- model-00004-of-00006.safetensors +3 -0
- model-00005-of-00006.safetensors +3 -0
- model-00006-of-00006.safetensors +3 -0
- model.safetensors.index.json +1 -0
- special_tokens_map.json +17 -0
- tensor.py +162 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2063 -0
README.md
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@@ -1,3 +1,64 @@
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---
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-
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---
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library_name: transformers
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tags:
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- llama-3
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license: cc-by-nc-4.0
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---
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# Badger Mushroom 4x8b
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I've been really impressed with how well these frankenmoe models quant compared to the base llama 8b, but with far better speed than the 70b. 8x8b seemed a bit unneccessary for how much additonal value it brougt, so I dialed it back to a 4x4b version. This model feels pretty good out of the gate, which considering how I used a non-standard merge; is a bit surprising.
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```
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base_model: ./maldv/badger
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gate_mode: hidden
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dtype: bfloat16
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+
experts_per_token: 2
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experts:
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- source_model: ./models/instruct/Llama-3-SauerkrautLM-8b-Instruct
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| 19 |
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positive_prompts:
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| 20 |
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<some words>
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| 21 |
+
negative_prompts:
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| 22 |
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<some words>
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| 23 |
+
- source_model: ./models/instruct/opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5
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| 24 |
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positive_prompts:
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| 25 |
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<some words>
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| 26 |
+
negative_prompts:
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| 27 |
+
<some words>
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| 28 |
+
- source_model: ./models/instruct/Llama-3-8B-Instruct-DPO-v0.4
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| 29 |
+
positive_prompts:
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| 30 |
+
<some words>
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| 31 |
+
negative_prompts:
|
| 32 |
+
<some words>
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| 33 |
+
- source_model: ./models/instruct/Poppy_Porpoise-0.72-L3-8B
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| 34 |
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positive_prompts:
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| 35 |
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<some words>
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negative_prompts:
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<some words>
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```
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### Badger
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Badger is a cascading [fourier interpolation](./tensor.py#3) of the following models, with the merge order based on the layer cosine similarity:
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```python
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[
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'opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5',
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'Llama-3-SauerkrautLM-8b-Instruct',
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| 48 |
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'Llama-3-8B-Instruct-DPO-v0.4',
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'Roleplay-Llama-3-8B',
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| 50 |
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'Llama-3-Lumimaid-8B-v0.1',
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| 51 |
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'Poppy_Porpoise-0.72-L3-8B',
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'L3-TheSpice-8b-v0.8.3',
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+
'Llama-3-LewdPlay-8B-evo',
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| 54 |
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'Llama-3-8B-Instruct-norefusal',
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| 55 |
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'Meta-Llama-3-8B-Instruct-DPO',
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'Llama-3-Soliloquy-8B-v2'
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]
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```
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I'm finding my iq4_xs to be working well. Llama 3 instruct format works really well, but minimal format is also highly creative. So far it performs well in each of the four areas of roleplay, logic, writing, and assistant behaviors that I've tested it in.
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## Scores
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TBD
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config.json
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{
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"_name_or_path": "./maldv/badger",
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"architectures": [
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"MixtralForCausalLM"
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| 5 |
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],
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"attention_bias": false,
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| 7 |
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"attention_dropout": 0.0,
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| 8 |
+
"bos_token_id": 128000,
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| 9 |
+
"eos_token_id": 128001,
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| 10 |
+
"hidden_act": "silu",
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| 11 |
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"hidden_size": 4096,
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| 12 |
+
"initializer_range": 0.02,
|
| 13 |
+
"intermediate_size": 14336,
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| 14 |
+
"max_position_embeddings": 8192,
|
| 15 |
+
"model_type": "mixtral",
|
| 16 |
+
"num_attention_heads": 32,
|
| 17 |
+
"num_experts_per_tok": 2,
|
| 18 |
+
"num_hidden_layers": 32,
|
| 19 |
+
"num_key_value_heads": 8,
|
| 20 |
+
"num_local_experts": 4,
|
| 21 |
+
"output_router_logits": false,
|
| 22 |
+
"pretraining_tp": 1,
|
| 23 |
+
"rms_norm_eps": 1e-05,
|
| 24 |
+
"rope_scaling": null,
|
| 25 |
+
"rope_theta": 500000.0,
|
| 26 |
+
"router_aux_loss_coef": 0.001,
|
| 27 |
+
"router_jitter_noise": 0.0,
|
| 28 |
+
"sliding_window": null,
|
| 29 |
+
"tie_word_embeddings": false,
|
| 30 |
+
"torch_dtype": "bfloat16",
|
| 31 |
+
"transformers_version": "4.40.2",
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| 32 |
+
"use_cache": true,
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| 33 |
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"vocab_size": 128256
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| 34 |
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}
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mergekit_moe_config.yml
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| 1 |
+
base_model: ./maldv/badger
|
| 2 |
+
gate_mode: hidden
|
| 3 |
+
dtype: bfloat16
|
| 4 |
+
experts_per_token: 2
|
| 5 |
+
experts:
|
| 6 |
+
- source_model: ./models/instruct/Llama-3-SauerkrautLM-8b-Instruct
|
| 7 |
+
positive_prompts:
|
| 8 |
+
- '```'
|
| 9 |
+
- event coordination
|
| 10 |
+
- strategic planning
|
| 11 |
+
- task management
|
| 12 |
+
- scheduling appointments
|
| 13 |
+
- project timeline
|
| 14 |
+
- meeting facilitation
|
| 15 |
+
- resource allocation
|
| 16 |
+
- workflow optimization
|
| 17 |
+
- priority setting
|
| 18 |
+
- administrative support
|
| 19 |
+
- organizes meetings
|
| 20 |
+
- plans events
|
| 21 |
+
- delegates tasks
|
| 22 |
+
- sets deadlines
|
| 23 |
+
- updates calendar
|
| 24 |
+
- prepares reports
|
| 25 |
+
- coordinates logistics
|
| 26 |
+
- manages resources
|
| 27 |
+
- schedules conferences
|
| 28 |
+
- tracks progress
|
| 29 |
+
- "I organize"
|
| 30 |
+
- "she schedules"
|
| 31 |
+
- "I plan"
|
| 32 |
+
- "he coordinates"
|
| 33 |
+
- "I manage"
|
| 34 |
+
- "she arranges"
|
| 35 |
+
- "I prepare"
|
| 36 |
+
- "he oversees"
|
| 37 |
+
- "I track"
|
| 38 |
+
- "she facilitates"
|
| 39 |
+
- "scheduled the meeting"
|
| 40 |
+
- "organized the files"
|
| 41 |
+
- "prepared the agenda"
|
| 42 |
+
- "arranged the conference"
|
| 43 |
+
- "managed the schedule"
|
| 44 |
+
- "coordinated the event"
|
| 45 |
+
- "logged the details"
|
| 46 |
+
- "set up the call"
|
| 47 |
+
- "drafted the report"
|
| 48 |
+
- "tracked the deadlines"
|
| 49 |
+
- "updated the calendar"
|
| 50 |
+
- "facilitated the workshop"
|
| 51 |
+
- "allocated resources"
|
| 52 |
+
- "monitored the budget"
|
| 53 |
+
- "negotiated the contract"
|
| 54 |
+
- "handled the correspondence"
|
| 55 |
+
- "prioritized tasks"
|
| 56 |
+
- "oversaw the project"
|
| 57 |
+
- "implemented the plan"
|
| 58 |
+
- "summarized the minutes"
|
| 59 |
+
- "maintained records"
|
| 60 |
+
- "processed the invoices"
|
| 61 |
+
- "assessed the requirements"
|
| 62 |
+
- "streamlined operations"
|
| 63 |
+
- "liaised with stakeholders"
|
| 64 |
+
negative_prompts:
|
| 65 |
+
- 'love you'
|
| 66 |
+
- 'hate you'
|
| 67 |
+
- source_model: ./models/instruct/opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5
|
| 68 |
+
positive_prompts:
|
| 69 |
+
- literary expression
|
| 70 |
+
- narrative structure
|
| 71 |
+
- creative writing
|
| 72 |
+
- plot development
|
| 73 |
+
- character development
|
| 74 |
+
- fictional elements
|
| 75 |
+
- poetic meter
|
| 76 |
+
- publishing rights
|
| 77 |
+
- editorial guidelines
|
| 78 |
+
- story arc
|
| 79 |
+
- drafts manuscript
|
| 80 |
+
- revises chapter
|
| 81 |
+
- writes dialogue
|
| 82 |
+
- describes setting
|
| 83 |
+
- develops characters
|
| 84 |
+
- edits text
|
| 85 |
+
- publishes article
|
| 86 |
+
- researches theme
|
| 87 |
+
- expresses thoughts
|
| 88 |
+
- outlines plot
|
| 89 |
+
- "I narrate"
|
| 90 |
+
- "he drafts"
|
| 91 |
+
- "she said"
|
| 92 |
+
- "as they looked"
|
| 93 |
+
- "suddenly"
|
| 94 |
+
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|
| 95 |
+
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|
| 96 |
+
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|
| 97 |
+
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|
| 98 |
+
- "without warning"
|
| 99 |
+
- "upon reflection"
|
| 100 |
+
- "in summary"
|
| 101 |
+
- "for instance"
|
| 102 |
+
- "in contrast"
|
| 103 |
+
- "moreover"
|
| 104 |
+
- "consequently"
|
| 105 |
+
- "hence"
|
| 106 |
+
- "therefore"
|
| 107 |
+
- "in addition"
|
| 108 |
+
- "on the other hand"
|
| 109 |
+
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|
| 110 |
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|
| 111 |
+
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|
| 112 |
+
- "lastly"
|
| 113 |
+
- "to illustrate"
|
| 114 |
+
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|
| 115 |
+
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|
| 116 |
+
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|
| 117 |
+
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|
| 118 |
+
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|
| 119 |
+
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|
| 120 |
+
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|
| 121 |
+
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|
| 122 |
+
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|
| 123 |
+
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|
| 124 |
+
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|
| 125 |
+
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|
| 126 |
+
- "a sea of"
|
| 127 |
+
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|
| 128 |
+
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|
| 129 |
+
- "shadowed by"
|
| 130 |
+
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|
| 131 |
+
- "peered through"
|
| 132 |
+
- "glimpsed"
|
| 133 |
+
- "flickered across"
|
| 134 |
+
- "etched against"
|
| 135 |
+
- "mirrored in"
|
| 136 |
+
- "danced along"
|
| 137 |
+
- "painted across"
|
| 138 |
+
- "veiled in"
|
| 139 |
+
- "burst into view"
|
| 140 |
+
- "caught the light"
|
| 141 |
+
- "hidden in plain sight"
|
| 142 |
+
- "cast a shadow"
|
| 143 |
+
- "the outline of"
|
| 144 |
+
- "flashed before"
|
| 145 |
+
- "reflected on"
|
| 146 |
+
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|
| 147 |
+
- "bathed in light"
|
| 148 |
+
- "blurred by"
|
| 149 |
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- "emerged from"
|
| 150 |
+
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|
| 151 |
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|
| 152 |
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- "rushed towards"
|
| 153 |
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- "leapt up"
|
| 154 |
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|
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|
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
+
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
+
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
+
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|
| 174 |
+
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|
| 175 |
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|
| 176 |
+
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|
| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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|
| 181 |
+
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|
| 182 |
+
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|
| 183 |
+
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|
| 184 |
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|
| 185 |
+
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|
| 186 |
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|
| 187 |
+
- computes results
|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
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|
| 194 |
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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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|
| 206 |
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|
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|
| 208 |
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|
| 209 |
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|
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|
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|
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|
| 213 |
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|
| 214 |
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|
| 215 |
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|
| 216 |
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|
| 217 |
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|
| 218 |
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|
| 219 |
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|
| 220 |
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|
| 221 |
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|
| 222 |
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|
| 223 |
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|
| 224 |
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|
| 225 |
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|
| 226 |
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|
| 227 |
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|
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|
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|
| 231 |
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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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|
| 240 |
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|
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|
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|
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|
| 244 |
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|
| 245 |
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|
| 246 |
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|
| 247 |
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|
| 248 |
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|
| 249 |
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|
| 250 |
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|
| 251 |
+
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|
| 252 |
+
- emotes feelings
|
| 253 |
+
- practices movements
|
| 254 |
+
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|
| 255 |
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|
| 256 |
+
- adopts persona
|
| 257 |
+
- "I perform"
|
| 258 |
+
- "she rehearses"
|
| 259 |
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- "I interpret"
|
| 260 |
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|
| 261 |
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- "I portray"
|
| 262 |
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|
| 263 |
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|
| 264 |
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|
| 265 |
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|
| 266 |
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|
| 267 |
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|
| 268 |
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|
| 269 |
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|
| 270 |
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|
| 271 |
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|
| 272 |
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|
| 273 |
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|
| 274 |
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|
| 275 |
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|
| 276 |
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|
| 277 |
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|
| 278 |
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|
| 279 |
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|
| 280 |
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|
| 281 |
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|
| 282 |
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|
| 283 |
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|
| 284 |
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|
| 285 |
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|
| 286 |
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|
| 287 |
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|
| 288 |
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|
| 289 |
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- "character portrayal"
|
| 290 |
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|
| 291 |
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- "she rehearses"
|
| 292 |
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- "I interpret"
|
| 293 |
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- "he emotes"
|
| 294 |
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- "I portray"
|
| 295 |
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- "she captivates"
|
| 296 |
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|
| 297 |
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|
| 298 |
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|
| 299 |
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|
| 300 |
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|
| 301 |
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|
| 302 |
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|
| 303 |
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|
| 304 |
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|
| 305 |
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|
| 306 |
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|
| 307 |
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|
| 308 |
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|
| 309 |
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|
| 310 |
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|
| 311 |
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|
| 312 |
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|
| 313 |
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|
| 314 |
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|
| 315 |
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|
| 316 |
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|
| 317 |
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|
| 318 |
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|
| 319 |
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|
| 320 |
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|
| 321 |
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|
| 322 |
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|
| 323 |
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|
| 324 |
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|
| 325 |
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|
| 326 |
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|
| 327 |
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|
| 328 |
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|
| 329 |
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|
| 330 |
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|
| 331 |
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|
| 332 |
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|
| 333 |
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|
| 334 |
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|
| 335 |
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|
| 336 |
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|
| 337 |
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|
| 338 |
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|
| 339 |
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|
| 340 |
+
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|
| 341 |
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|
| 342 |
+
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|
| 343 |
+
- "he instructed"
|
| 344 |
+
- "they announced"
|
| 345 |
+
- "she commented"
|
| 346 |
+
negative_prompts:
|
| 347 |
+
- 'add'
|
| 348 |
+
- 'sum'
|
| 349 |
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- 'count'
|
| 350 |
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|
|
|
| 1 |
+
import torch
|
| 2 |
+
|
| 3 |
+
def merge_tensors_fft2(v0: torch.Tensor, v1: torch.Tensor, t: float) -> torch.Tensor:
|
| 4 |
+
"""
|
| 5 |
+
Merges two tensors using 2D Fourier transform interpolation.
|
| 6 |
+
|
| 7 |
+
Parameters:
|
| 8 |
+
- v0 (torch.Tensor): The first input tensor.
|
| 9 |
+
- v1 (torch.Tensor): The second input tensor.
|
| 10 |
+
- t (float): Interpolation parameter (0 <= t <= 1).
|
| 11 |
+
|
| 12 |
+
Returns:
|
| 13 |
+
- torch.Tensor: The tensor resulting from the interpolated inverse FFT.
|
| 14 |
+
"""
|
| 15 |
+
# Ensure the input tensors are on the same device and dtype
|
| 16 |
+
v0 = v0.to("cuda:0")
|
| 17 |
+
v1 = v1.to("cuda:0")
|
| 18 |
+
|
| 19 |
+
if len(v0.shape) == 1:
|
| 20 |
+
fft_v0 = torch.fft.fft(v0)
|
| 21 |
+
fft_v1 = torch.fft.fft(v1)
|
| 22 |
+
result_fft = torch.zeros_like(fft_v0)
|
| 23 |
+
|
| 24 |
+
real_v0 = fft_v0.real
|
| 25 |
+
real_v1 = fft_v1.real
|
| 26 |
+
abs_real_v0 = real_v0.abs()
|
| 27 |
+
abs_real_v1 = real_v1.abs()
|
| 28 |
+
|
| 29 |
+
sign_mask = real_v0.sign() == real_v1.sign()
|
| 30 |
+
larger_values_mask = abs_real_v0 > abs_real_v1
|
| 31 |
+
|
| 32 |
+
result_fft.real[sign_mask] = torch.max(real_v0[sign_mask], real_v1[sign_mask])
|
| 33 |
+
result_fft.real[~sign_mask] = torch.where(larger_values_mask[~sign_mask], real_v0[~sign_mask], real_v1[~sign_mask])
|
| 34 |
+
|
| 35 |
+
imag_v0 = fft_v0.imag
|
| 36 |
+
imag_v1 = fft_v1.imag
|
| 37 |
+
abs_imag_v0 = imag_v0.abs()
|
| 38 |
+
abs_imag_v1 = imag_v1.abs()
|
| 39 |
+
larger_values_mask_imag = abs_imag_v0 > abs_imag_v1
|
| 40 |
+
|
| 41 |
+
result_fft.imag[sign_mask] = (1 - t) * imag_v0[sign_mask] + t * imag_v1[sign_mask]
|
| 42 |
+
result_fft.imag[~sign_mask] = torch.where(larger_values_mask_imag[~sign_mask], imag_v0[~sign_mask], imag_v1[~sign_mask])
|
| 43 |
+
|
| 44 |
+
merged_tensor = torch.fft.ifft(result_fft).real # Taking the real part
|
| 45 |
+
del v0, v1, fft_v0, fft_v1, result_fft
|
| 46 |
+
return merged_tensor
|
| 47 |
+
|
| 48 |
+
# Perform the 2D FFT on both tensors
|
| 49 |
+
fft_v0 = torch.fft.fftn(v0, dim=(-2, -1))
|
| 50 |
+
fft_v1 = torch.fft.fftn(v1, dim=(-2, -1))
|
| 51 |
+
|
| 52 |
+
# Initialize the result FFT tensor
|
| 53 |
+
result_fft = torch.zeros_like(fft_v0)
|
| 54 |
+
|
| 55 |
+
# Compare real parts of the coefficients
|
| 56 |
+
real_v0 = fft_v0.real
|
| 57 |
+
real_v1 = fft_v1.real
|
| 58 |
+
abs_real_v0 = real_v0.abs()
|
| 59 |
+
abs_real_v1 = real_v1.abs()
|
| 60 |
+
|
| 61 |
+
# Create masks for where signs match and where they do not
|
| 62 |
+
sign_mask = real_v0.sign() == real_v1.sign()
|
| 63 |
+
larger_values_mask = abs_real_v0 > abs_real_v1
|
| 64 |
+
|
| 65 |
+
# Where signs match, interpolate; where signs do not match, take the larger by magnitude
|
| 66 |
+
result_fft.real[sign_mask] = torch.max(real_v0[sign_mask], real_v1[sign_mask])
|
| 67 |
+
result_fft.real[~sign_mask] = torch.where(larger_values_mask[~sign_mask], real_v0[~sign_mask], real_v1[~sign_mask])
|
| 68 |
+
|
| 69 |
+
# Assuming the imaginary part should be treated similarly, adjust this if not
|
| 70 |
+
imag_v0 = fft_v0.imag
|
| 71 |
+
imag_v1 = fft_v1.imag
|
| 72 |
+
abs_imag_v0 = imag_v0.abs()
|
| 73 |
+
abs_imag_v1 = imag_v1.abs()
|
| 74 |
+
larger_values_mask_imag = abs_imag_v0 > abs_imag_v1
|
| 75 |
+
|
| 76 |
+
result_fft.imag[sign_mask] = (1 - t) * imag_v0[sign_mask] + t * imag_v1[sign_mask]
|
| 77 |
+
result_fft.imag[~sign_mask] = torch.where(larger_values_mask_imag[~sign_mask], imag_v0[~sign_mask], imag_v1[~sign_mask])
|
| 78 |
+
|
| 79 |
+
# Perform the inverse FFT to go back to the spatial domain
|
| 80 |
+
merged_tensor = torch.fft.ifftn(result_fft, dim=(-2, -1)).real # Taking the real part
|
| 81 |
+
|
| 82 |
+
return merged_tensor
|
| 83 |
+
|
| 84 |
+
def merge_tensors_fft_shell(v0: torch.Tensor, v1: torch.Tensor) -> torch.Tensor:
|
| 85 |
+
"""
|
| 86 |
+
Merges two tensors using 2D Fourier transform interpolation.
|
| 87 |
+
|
| 88 |
+
Parameters:
|
| 89 |
+
- v0 (torch.Tensor): The first input tensor.
|
| 90 |
+
- v1 (torch.Tensor): The second input tensor.
|
| 91 |
+
|
| 92 |
+
Returns:
|
| 93 |
+
- torch.Tensor: The tensor resulting from the maximal interpolated inverse FFT.
|
| 94 |
+
"""
|
| 95 |
+
# Ensure the input tensors are on the same device and dtype
|
| 96 |
+
v0 = v0.to("cuda:0")
|
| 97 |
+
v1 = v1.to("cuda:0")
|
| 98 |
+
|
| 99 |
+
if len(v0.shape) == 1:
|
| 100 |
+
fft_v0 = torch.fft.fft(v0)
|
| 101 |
+
fft_v1 = torch.fft.fft(v1)
|
| 102 |
+
result_fft = torch.zeros_like(fft_v0)
|
| 103 |
+
|
| 104 |
+
real_v0 = fft_v0.real
|
| 105 |
+
real_v1 = fft_v1.real
|
| 106 |
+
abs_real_v0 = real_v0.abs()
|
| 107 |
+
abs_real_v1 = real_v1.abs()
|
| 108 |
+
|
| 109 |
+
sign_mask = real_v0.sign() == real_v1.sign()
|
| 110 |
+
larger_values_mask = abs_real_v0 > abs_real_v1
|
| 111 |
+
|
| 112 |
+
result_fft.real[sign_mask] = torch.max(real_v0[sign_mask], real_v1[sign_mask])
|
| 113 |
+
result_fft.real[~sign_mask] = torch.where(larger_values_mask[~sign_mask], real_v0[~sign_mask], real_v1[~sign_mask])
|
| 114 |
+
|
| 115 |
+
imag_v0 = fft_v0.imag
|
| 116 |
+
imag_v1 = fft_v1.imag
|
| 117 |
+
abs_imag_v0 = imag_v0.abs()
|
| 118 |
+
abs_imag_v1 = imag_v1.abs()
|
| 119 |
+
larger_values_mask_imag = abs_imag_v0 > abs_imag_v1
|
| 120 |
+
|
| 121 |
+
result_fft.imag[sign_mask] = torch.max(imag_v0[sign_mask], imag_v1[sign_mask])
|
| 122 |
+
result_fft.imag[~sign_mask] = torch.where(larger_values_mask_imag[~sign_mask], imag_v0[~sign_mask], imag_v1[~sign_mask])
|
| 123 |
+
|
| 124 |
+
merged_tensor = torch.fft.ifft(result_fft).real # Taking the real part
|
| 125 |
+
del v0, v1, fft_v0, fft_v1, result_fft
|
| 126 |
+
return merged_tensor
|
| 127 |
+
|
| 128 |
+
# Perform the 2D FFT on both tensors
|
| 129 |
+
fft_v0 = torch.fft.fftn(v0, dim=(-2, -1))
|
| 130 |
+
fft_v1 = torch.fft.fftn(v1, dim=(-2, -1))
|
| 131 |
+
|
| 132 |
+
# Initialize the result FFT tensor
|
| 133 |
+
result_fft = torch.zeros_like(fft_v0)
|
| 134 |
+
|
| 135 |
+
# Compare real parts of the coefficients
|
| 136 |
+
real_v0 = fft_v0.real
|
| 137 |
+
real_v1 = fft_v1.real
|
| 138 |
+
abs_real_v0 = real_v0.abs()
|
| 139 |
+
abs_real_v1 = real_v1.abs()
|
| 140 |
+
|
| 141 |
+
# Create masks for where signs match and where they do not
|
| 142 |
+
sign_mask = real_v0.sign() == real_v1.sign()
|
| 143 |
+
larger_values_mask = abs_real_v0 > abs_real_v1
|
| 144 |
+
|
| 145 |
+
# Where signs match, interpolate; where signs do not match, take the larger by magnitude
|
| 146 |
+
result_fft.real[sign_mask] = torch.max(real_v0[sign_mask], real_v1[sign_mask])
|
| 147 |
+
result_fft.real[~sign_mask] = torch.where(larger_values_mask[~sign_mask], real_v0[~sign_mask], real_v1[~sign_mask])
|
| 148 |
+
|
| 149 |
+
# Assuming the imaginary part should be treated similarly, adjust this if not
|
| 150 |
+
imag_v0 = fft_v0.imag
|
| 151 |
+
imag_v1 = fft_v1.imag
|
| 152 |
+
abs_imag_v0 = imag_v0.abs()
|
| 153 |
+
abs_imag_v1 = imag_v1.abs()
|
| 154 |
+
larger_values_mask_imag = abs_imag_v0 > abs_imag_v1
|
| 155 |
+
|
| 156 |
+
result_fft.imag[sign_mask] = torch.max(imag_v0[sign_mask], imag_v1[sign_mask])
|
| 157 |
+
result_fft.imag[~sign_mask] = torch.where(larger_values_mask_imag[~sign_mask], imag_v0[~sign_mask], imag_v1[~sign_mask])
|
| 158 |
+
|
| 159 |
+
# Perform the inverse FFT to go back to the spatial domain
|
| 160 |
+
merged_tensor = torch.fft.ifftn(result_fft, dim=(-2, -1)).real # Taking the real part
|
| 161 |
+
|
| 162 |
+
return merged_tensor
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2063 @@
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"128000": {
|
| 4 |
+
"content": "<|begin_of_text|>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128001": {
|
| 12 |
+
"content": "<|end_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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| 62 |
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|
| 63 |
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| 64 |
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| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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| 70 |
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|
| 71 |
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| 72 |
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|
| 73 |
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|
| 74 |
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| 75 |
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|
| 76 |
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|
| 77 |
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| 78 |
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|
| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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|
| 84 |
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|
| 85 |
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| 86 |
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|
| 87 |
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| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 106 |
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| 107 |
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|
| 108 |
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|
| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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|
| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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|
| 131 |
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|
| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 150 |
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| 153 |
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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| 164 |
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| 165 |
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| 172 |
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| 173 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 186 |
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| 187 |
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| 188 |
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| 189 |
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| 193 |
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| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 203 |
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| 204 |
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| 205 |
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| 211 |
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| 212 |
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| 213 |
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| 218 |
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| 219 |
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| 220 |
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| 221 |
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| 226 |
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| 227 |
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| 228 |
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| 229 |
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| 230 |
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| 233 |
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| 234 |
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| 235 |
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|
| 236 |
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| 237 |
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| 238 |
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| 239 |
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| 241 |
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| 242 |
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| 243 |
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|
| 244 |
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| 245 |
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| 246 |
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| 247 |
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| 250 |
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| 251 |
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|
| 252 |
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|
| 253 |
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| 254 |
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| 255 |
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| 257 |
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| 258 |
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| 259 |
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|
| 260 |
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| 261 |
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| 262 |
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| 263 |
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| 265 |
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| 266 |
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|
| 267 |
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| 268 |
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|
| 269 |
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| 270 |
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| 271 |
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| 272 |
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| 273 |
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| 274 |
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| 275 |
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| 276 |
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| 277 |
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| 278 |
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| 279 |
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| 281 |
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| 282 |
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| 283 |
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| 284 |
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| 285 |
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| 286 |
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| 287 |
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| 288 |
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| 289 |
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| 290 |
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|
| 291 |
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|
| 292 |
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|
| 293 |
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| 294 |
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| 295 |
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| 296 |
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| 297 |
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|
| 298 |
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|
| 299 |
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|
| 300 |
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| 301 |
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| 302 |
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| 303 |
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| 304 |
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| 305 |
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| 306 |
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| 307 |
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| 308 |
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|
| 309 |
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| 310 |
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| 311 |
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| 312 |
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| 313 |
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| 314 |
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| 315 |
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| 316 |
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| 317 |
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| 318 |
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| 319 |
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| 321 |
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| 322 |
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| 323 |
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| 324 |
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| 325 |
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| 326 |
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| 327 |
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| 328 |
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| 329 |
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| 330 |
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| 331 |
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| 332 |
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|
| 333 |
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| 334 |
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| 335 |
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| 336 |
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| 337 |
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| 338 |
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| 339 |
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|
| 340 |
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| 341 |
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| 342 |
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| 343 |
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| 344 |
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| 345 |
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| 346 |
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| 347 |
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| 348 |
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| 349 |
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| 350 |
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| 351 |
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| 352 |
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| 353 |
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| 354 |
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| 355 |
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| 356 |
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| 357 |
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| 361 |
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| 362 |
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| 363 |
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| 364 |
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| 365 |
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| 372 |
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| 373 |
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| 380 |
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| 381 |
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| 382 |
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| 386 |
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| 388 |
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| 389 |
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| 390 |
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| 396 |
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| 410 |
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| 413 |
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| 418 |
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| 420 |
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"rstrip": false,
|
| 1672 |
+
"single_word": false,
|
| 1673 |
+
"special": true
|
| 1674 |
+
},
|
| 1675 |
+
"128209": {
|
| 1676 |
+
"content": "<|reserved_special_token_204|>",
|
| 1677 |
+
"lstrip": false,
|
| 1678 |
+
"normalized": false,
|
| 1679 |
+
"rstrip": false,
|
| 1680 |
+
"single_word": false,
|
| 1681 |
+
"special": true
|
| 1682 |
+
},
|
| 1683 |
+
"128210": {
|
| 1684 |
+
"content": "<|reserved_special_token_205|>",
|
| 1685 |
+
"lstrip": false,
|
| 1686 |
+
"normalized": false,
|
| 1687 |
+
"rstrip": false,
|
| 1688 |
+
"single_word": false,
|
| 1689 |
+
"special": true
|
| 1690 |
+
},
|
| 1691 |
+
"128211": {
|
| 1692 |
+
"content": "<|reserved_special_token_206|>",
|
| 1693 |
+
"lstrip": false,
|
| 1694 |
+
"normalized": false,
|
| 1695 |
+
"rstrip": false,
|
| 1696 |
+
"single_word": false,
|
| 1697 |
+
"special": true
|
| 1698 |
+
},
|
| 1699 |
+
"128212": {
|
| 1700 |
+
"content": "<|reserved_special_token_207|>",
|
| 1701 |
+
"lstrip": false,
|
| 1702 |
+
"normalized": false,
|
| 1703 |
+
"rstrip": false,
|
| 1704 |
+
"single_word": false,
|
| 1705 |
+
"special": true
|
| 1706 |
+
},
|
| 1707 |
+
"128213": {
|
| 1708 |
+
"content": "<|reserved_special_token_208|>",
|
| 1709 |
+
"lstrip": false,
|
| 1710 |
+
"normalized": false,
|
| 1711 |
+
"rstrip": false,
|
| 1712 |
+
"single_word": false,
|
| 1713 |
+
"special": true
|
| 1714 |
+
},
|
| 1715 |
+
"128214": {
|
| 1716 |
+
"content": "<|reserved_special_token_209|>",
|
| 1717 |
+
"lstrip": false,
|
| 1718 |
+
"normalized": false,
|
| 1719 |
+
"rstrip": false,
|
| 1720 |
+
"single_word": false,
|
| 1721 |
+
"special": true
|
| 1722 |
+
},
|
| 1723 |
+
"128215": {
|
| 1724 |
+
"content": "<|reserved_special_token_210|>",
|
| 1725 |
+
"lstrip": false,
|
| 1726 |
+
"normalized": false,
|
| 1727 |
+
"rstrip": false,
|
| 1728 |
+
"single_word": false,
|
| 1729 |
+
"special": true
|
| 1730 |
+
},
|
| 1731 |
+
"128216": {
|
| 1732 |
+
"content": "<|reserved_special_token_211|>",
|
| 1733 |
+
"lstrip": false,
|
| 1734 |
+
"normalized": false,
|
| 1735 |
+
"rstrip": false,
|
| 1736 |
+
"single_word": false,
|
| 1737 |
+
"special": true
|
| 1738 |
+
},
|
| 1739 |
+
"128217": {
|
| 1740 |
+
"content": "<|reserved_special_token_212|>",
|
| 1741 |
+
"lstrip": false,
|
| 1742 |
+
"normalized": false,
|
| 1743 |
+
"rstrip": false,
|
| 1744 |
+
"single_word": false,
|
| 1745 |
+
"special": true
|
| 1746 |
+
},
|
| 1747 |
+
"128218": {
|
| 1748 |
+
"content": "<|reserved_special_token_213|>",
|
| 1749 |
+
"lstrip": false,
|
| 1750 |
+
"normalized": false,
|
| 1751 |
+
"rstrip": false,
|
| 1752 |
+
"single_word": false,
|
| 1753 |
+
"special": true
|
| 1754 |
+
},
|
| 1755 |
+
"128219": {
|
| 1756 |
+
"content": "<|reserved_special_token_214|>",
|
| 1757 |
+
"lstrip": false,
|
| 1758 |
+
"normalized": false,
|
| 1759 |
+
"rstrip": false,
|
| 1760 |
+
"single_word": false,
|
| 1761 |
+
"special": true
|
| 1762 |
+
},
|
| 1763 |
+
"128220": {
|
| 1764 |
+
"content": "<|reserved_special_token_215|>",
|
| 1765 |
+
"lstrip": false,
|
| 1766 |
+
"normalized": false,
|
| 1767 |
+
"rstrip": false,
|
| 1768 |
+
"single_word": false,
|
| 1769 |
+
"special": true
|
| 1770 |
+
},
|
| 1771 |
+
"128221": {
|
| 1772 |
+
"content": "<|reserved_special_token_216|>",
|
| 1773 |
+
"lstrip": false,
|
| 1774 |
+
"normalized": false,
|
| 1775 |
+
"rstrip": false,
|
| 1776 |
+
"single_word": false,
|
| 1777 |
+
"special": true
|
| 1778 |
+
},
|
| 1779 |
+
"128222": {
|
| 1780 |
+
"content": "<|reserved_special_token_217|>",
|
| 1781 |
+
"lstrip": false,
|
| 1782 |
+
"normalized": false,
|
| 1783 |
+
"rstrip": false,
|
| 1784 |
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"single_word": false,
|
| 1785 |
+
"special": true
|
| 1786 |
+
},
|
| 1787 |
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"128223": {
|
| 1788 |
+
"content": "<|reserved_special_token_218|>",
|
| 1789 |
+
"lstrip": false,
|
| 1790 |
+
"normalized": false,
|
| 1791 |
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"rstrip": false,
|
| 1792 |
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"single_word": false,
|
| 1793 |
+
"special": true
|
| 1794 |
+
},
|
| 1795 |
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"128224": {
|
| 1796 |
+
"content": "<|reserved_special_token_219|>",
|
| 1797 |
+
"lstrip": false,
|
| 1798 |
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"normalized": false,
|
| 1799 |
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"rstrip": false,
|
| 1800 |
+
"single_word": false,
|
| 1801 |
+
"special": true
|
| 1802 |
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},
|
| 1803 |
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"128225": {
|
| 1804 |
+
"content": "<|reserved_special_token_220|>",
|
| 1805 |
+
"lstrip": false,
|
| 1806 |
+
"normalized": false,
|
| 1807 |
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"rstrip": false,
|
| 1808 |
+
"single_word": false,
|
| 1809 |
+
"special": true
|
| 1810 |
+
},
|
| 1811 |
+
"128226": {
|
| 1812 |
+
"content": "<|reserved_special_token_221|>",
|
| 1813 |
+
"lstrip": false,
|
| 1814 |
+
"normalized": false,
|
| 1815 |
+
"rstrip": false,
|
| 1816 |
+
"single_word": false,
|
| 1817 |
+
"special": true
|
| 1818 |
+
},
|
| 1819 |
+
"128227": {
|
| 1820 |
+
"content": "<|reserved_special_token_222|>",
|
| 1821 |
+
"lstrip": false,
|
| 1822 |
+
"normalized": false,
|
| 1823 |
+
"rstrip": false,
|
| 1824 |
+
"single_word": false,
|
| 1825 |
+
"special": true
|
| 1826 |
+
},
|
| 1827 |
+
"128228": {
|
| 1828 |
+
"content": "<|reserved_special_token_223|>",
|
| 1829 |
+
"lstrip": false,
|
| 1830 |
+
"normalized": false,
|
| 1831 |
+
"rstrip": false,
|
| 1832 |
+
"single_word": false,
|
| 1833 |
+
"special": true
|
| 1834 |
+
},
|
| 1835 |
+
"128229": {
|
| 1836 |
+
"content": "<|reserved_special_token_224|>",
|
| 1837 |
+
"lstrip": false,
|
| 1838 |
+
"normalized": false,
|
| 1839 |
+
"rstrip": false,
|
| 1840 |
+
"single_word": false,
|
| 1841 |
+
"special": true
|
| 1842 |
+
},
|
| 1843 |
+
"128230": {
|
| 1844 |
+
"content": "<|reserved_special_token_225|>",
|
| 1845 |
+
"lstrip": false,
|
| 1846 |
+
"normalized": false,
|
| 1847 |
+
"rstrip": false,
|
| 1848 |
+
"single_word": false,
|
| 1849 |
+
"special": true
|
| 1850 |
+
},
|
| 1851 |
+
"128231": {
|
| 1852 |
+
"content": "<|reserved_special_token_226|>",
|
| 1853 |
+
"lstrip": false,
|
| 1854 |
+
"normalized": false,
|
| 1855 |
+
"rstrip": false,
|
| 1856 |
+
"single_word": false,
|
| 1857 |
+
"special": true
|
| 1858 |
+
},
|
| 1859 |
+
"128232": {
|
| 1860 |
+
"content": "<|reserved_special_token_227|>",
|
| 1861 |
+
"lstrip": false,
|
| 1862 |
+
"normalized": false,
|
| 1863 |
+
"rstrip": false,
|
| 1864 |
+
"single_word": false,
|
| 1865 |
+
"special": true
|
| 1866 |
+
},
|
| 1867 |
+
"128233": {
|
| 1868 |
+
"content": "<|reserved_special_token_228|>",
|
| 1869 |
+
"lstrip": false,
|
| 1870 |
+
"normalized": false,
|
| 1871 |
+
"rstrip": false,
|
| 1872 |
+
"single_word": false,
|
| 1873 |
+
"special": true
|
| 1874 |
+
},
|
| 1875 |
+
"128234": {
|
| 1876 |
+
"content": "<|reserved_special_token_229|>",
|
| 1877 |
+
"lstrip": false,
|
| 1878 |
+
"normalized": false,
|
| 1879 |
+
"rstrip": false,
|
| 1880 |
+
"single_word": false,
|
| 1881 |
+
"special": true
|
| 1882 |
+
},
|
| 1883 |
+
"128235": {
|
| 1884 |
+
"content": "<|reserved_special_token_230|>",
|
| 1885 |
+
"lstrip": false,
|
| 1886 |
+
"normalized": false,
|
| 1887 |
+
"rstrip": false,
|
| 1888 |
+
"single_word": false,
|
| 1889 |
+
"special": true
|
| 1890 |
+
},
|
| 1891 |
+
"128236": {
|
| 1892 |
+
"content": "<|reserved_special_token_231|>",
|
| 1893 |
+
"lstrip": false,
|
| 1894 |
+
"normalized": false,
|
| 1895 |
+
"rstrip": false,
|
| 1896 |
+
"single_word": false,
|
| 1897 |
+
"special": true
|
| 1898 |
+
},
|
| 1899 |
+
"128237": {
|
| 1900 |
+
"content": "<|reserved_special_token_232|>",
|
| 1901 |
+
"lstrip": false,
|
| 1902 |
+
"normalized": false,
|
| 1903 |
+
"rstrip": false,
|
| 1904 |
+
"single_word": false,
|
| 1905 |
+
"special": true
|
| 1906 |
+
},
|
| 1907 |
+
"128238": {
|
| 1908 |
+
"content": "<|reserved_special_token_233|>",
|
| 1909 |
+
"lstrip": false,
|
| 1910 |
+
"normalized": false,
|
| 1911 |
+
"rstrip": false,
|
| 1912 |
+
"single_word": false,
|
| 1913 |
+
"special": true
|
| 1914 |
+
},
|
| 1915 |
+
"128239": {
|
| 1916 |
+
"content": "<|reserved_special_token_234|>",
|
| 1917 |
+
"lstrip": false,
|
| 1918 |
+
"normalized": false,
|
| 1919 |
+
"rstrip": false,
|
| 1920 |
+
"single_word": false,
|
| 1921 |
+
"special": true
|
| 1922 |
+
},
|
| 1923 |
+
"128240": {
|
| 1924 |
+
"content": "<|reserved_special_token_235|>",
|
| 1925 |
+
"lstrip": false,
|
| 1926 |
+
"normalized": false,
|
| 1927 |
+
"rstrip": false,
|
| 1928 |
+
"single_word": false,
|
| 1929 |
+
"special": true
|
| 1930 |
+
},
|
| 1931 |
+
"128241": {
|
| 1932 |
+
"content": "<|reserved_special_token_236|>",
|
| 1933 |
+
"lstrip": false,
|
| 1934 |
+
"normalized": false,
|
| 1935 |
+
"rstrip": false,
|
| 1936 |
+
"single_word": false,
|
| 1937 |
+
"special": true
|
| 1938 |
+
},
|
| 1939 |
+
"128242": {
|
| 1940 |
+
"content": "<|reserved_special_token_237|>",
|
| 1941 |
+
"lstrip": false,
|
| 1942 |
+
"normalized": false,
|
| 1943 |
+
"rstrip": false,
|
| 1944 |
+
"single_word": false,
|
| 1945 |
+
"special": true
|
| 1946 |
+
},
|
| 1947 |
+
"128243": {
|
| 1948 |
+
"content": "<|reserved_special_token_238|>",
|
| 1949 |
+
"lstrip": false,
|
| 1950 |
+
"normalized": false,
|
| 1951 |
+
"rstrip": false,
|
| 1952 |
+
"single_word": false,
|
| 1953 |
+
"special": true
|
| 1954 |
+
},
|
| 1955 |
+
"128244": {
|
| 1956 |
+
"content": "<|reserved_special_token_239|>",
|
| 1957 |
+
"lstrip": false,
|
| 1958 |
+
"normalized": false,
|
| 1959 |
+
"rstrip": false,
|
| 1960 |
+
"single_word": false,
|
| 1961 |
+
"special": true
|
| 1962 |
+
},
|
| 1963 |
+
"128245": {
|
| 1964 |
+
"content": "<|reserved_special_token_240|>",
|
| 1965 |
+
"lstrip": false,
|
| 1966 |
+
"normalized": false,
|
| 1967 |
+
"rstrip": false,
|
| 1968 |
+
"single_word": false,
|
| 1969 |
+
"special": true
|
| 1970 |
+
},
|
| 1971 |
+
"128246": {
|
| 1972 |
+
"content": "<|reserved_special_token_241|>",
|
| 1973 |
+
"lstrip": false,
|
| 1974 |
+
"normalized": false,
|
| 1975 |
+
"rstrip": false,
|
| 1976 |
+
"single_word": false,
|
| 1977 |
+
"special": true
|
| 1978 |
+
},
|
| 1979 |
+
"128247": {
|
| 1980 |
+
"content": "<|reserved_special_token_242|>",
|
| 1981 |
+
"lstrip": false,
|
| 1982 |
+
"normalized": false,
|
| 1983 |
+
"rstrip": false,
|
| 1984 |
+
"single_word": false,
|
| 1985 |
+
"special": true
|
| 1986 |
+
},
|
| 1987 |
+
"128248": {
|
| 1988 |
+
"content": "<|reserved_special_token_243|>",
|
| 1989 |
+
"lstrip": false,
|
| 1990 |
+
"normalized": false,
|
| 1991 |
+
"rstrip": false,
|
| 1992 |
+
"single_word": false,
|
| 1993 |
+
"special": true
|
| 1994 |
+
},
|
| 1995 |
+
"128249": {
|
| 1996 |
+
"content": "<|reserved_special_token_244|>",
|
| 1997 |
+
"lstrip": false,
|
| 1998 |
+
"normalized": false,
|
| 1999 |
+
"rstrip": false,
|
| 2000 |
+
"single_word": false,
|
| 2001 |
+
"special": true
|
| 2002 |
+
},
|
| 2003 |
+
"128250": {
|
| 2004 |
+
"content": "<|reserved_special_token_245|>",
|
| 2005 |
+
"lstrip": false,
|
| 2006 |
+
"normalized": false,
|
| 2007 |
+
"rstrip": false,
|
| 2008 |
+
"single_word": false,
|
| 2009 |
+
"special": true
|
| 2010 |
+
},
|
| 2011 |
+
"128251": {
|
| 2012 |
+
"content": "<|reserved_special_token_246|>",
|
| 2013 |
+
"lstrip": false,
|
| 2014 |
+
"normalized": false,
|
| 2015 |
+
"rstrip": false,
|
| 2016 |
+
"single_word": false,
|
| 2017 |
+
"special": true
|
| 2018 |
+
},
|
| 2019 |
+
"128252": {
|
| 2020 |
+
"content": "<|reserved_special_token_247|>",
|
| 2021 |
+
"lstrip": false,
|
| 2022 |
+
"normalized": false,
|
| 2023 |
+
"rstrip": false,
|
| 2024 |
+
"single_word": false,
|
| 2025 |
+
"special": true
|
| 2026 |
+
},
|
| 2027 |
+
"128253": {
|
| 2028 |
+
"content": "<|reserved_special_token_248|>",
|
| 2029 |
+
"lstrip": false,
|
| 2030 |
+
"normalized": false,
|
| 2031 |
+
"rstrip": false,
|
| 2032 |
+
"single_word": false,
|
| 2033 |
+
"special": true
|
| 2034 |
+
},
|
| 2035 |
+
"128254": {
|
| 2036 |
+
"content": "<|reserved_special_token_249|>",
|
| 2037 |
+
"lstrip": false,
|
| 2038 |
+
"normalized": false,
|
| 2039 |
+
"rstrip": false,
|
| 2040 |
+
"single_word": false,
|
| 2041 |
+
"special": true
|
| 2042 |
+
},
|
| 2043 |
+
"128255": {
|
| 2044 |
+
"content": "<|reserved_special_token_250|>",
|
| 2045 |
+
"lstrip": false,
|
| 2046 |
+
"normalized": false,
|
| 2047 |
+
"rstrip": false,
|
| 2048 |
+
"single_word": false,
|
| 2049 |
+
"special": true
|
| 2050 |
+
}
|
| 2051 |
+
},
|
| 2052 |
+
"bos_token": "<|begin_of_text|>",
|
| 2053 |
+
"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
|
| 2054 |
+
"clean_up_tokenization_spaces": true,
|
| 2055 |
+
"eos_token": "<|end_of_text|>",
|
| 2056 |
+
"model_input_names": [
|
| 2057 |
+
"input_ids",
|
| 2058 |
+
"attention_mask"
|
| 2059 |
+
],
|
| 2060 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 2061 |
+
"pad_token": "<|begin_of_text|>",
|
| 2062 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 2063 |
+
}
|