Text Classification
Transformers
Safetensors
English
Chinese
gemma4
image-text-to-text
system-one
jev
typed-decisions
calibrated-classification
kiosk
Instructions to use BricksDisplay/jevling-e2b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BricksDisplay/jevling-e2b-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BricksDisplay/jevling-e2b-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("BricksDisplay/jevling-e2b-v1") model = AutoModelForMultimodalLM.from_pretrained("BricksDisplay/jevling-e2b-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,728 Bytes
970aafe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | {
"audio_token": "<|audio|>",
"backend": "tokenizers",
"boa_token": "<|audio>",
"boi_token": "<|image>",
"bos_token": "<bos>",
"eoa_token": "<audio|>",
"eoc_token": "<channel|>",
"eoi_token": "<image|>",
"eos_token": "<eos>",
"eot_token": "<turn|>",
"escape_token": "<|\"|>",
"etc_token": "<tool_call|>",
"etd_token": "<tool|>",
"etr_token": "<tool_response|>",
"extra_special_tokens": [
"<|video|>"
],
"image_token": "<|image|>",
"is_local": true,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 1000000000000000019884624838656,
"model_specific_special_tokens": {
"audio_token": "<|audio|>",
"boa_token": "<|audio>",
"boi_token": "<|image>",
"eoa_token": "<audio|>",
"eoc_token": "<channel|>",
"eoi_token": "<image|>",
"eot_token": "<turn|>",
"escape_token": "<|\"|>",
"etc_token": "<tool_call|>",
"etd_token": "<tool|>",
"etr_token": "<tool_response|>",
"image_token": "<|image|>",
"soc_token": "<|channel>",
"sot_token": "<|turn>",
"stc_token": "<|tool_call>",
"std_token": "<|tool>",
"str_token": "<|tool_response>",
"think_token": "<|think|>"
},
"pad_token": "<pad>",
"padding_side": "left",
"processor_class": "Gemma4Processor",
"response_schema": {
"properties": {
"content": {
"type": "string"
},
"role": {
"const": "assistant"
},
"thinking": {
"type": "string"
},
"tool_calls": {
"items": {
"properties": {
"function": {
"properties": {
"arguments": {
"additionalProperties": {},
"type": "object",
"x-parser": "gemma4-tool-call"
},
"name": {
"type": "string"
}
},
"type": "object",
"x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})"
},
"type": {
"const": "function"
}
},
"type": "object"
},
"type": "array",
"x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>"
}
},
"type": "object",
"x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?"
},
"response_template": {
"defaults": {
"role": "assistant"
},
"fields": {
"content": {
"close": [
"<turn|>",
"<|tool_response>",
"<eos>"
],
"content": "text"
},
"thinking": {
"close": "<channel|>",
"content": "text",
"open": "<|channel>thought\n"
},
"tool_calls": {
"close": "<tool_call|>",
"content": "json",
"content_args": {
"string_delims": [
[
"<|\"|>",
"<|\"|>"
]
],
"unquoted_keys": true
},
"open_pattern": "<\\|tool_call>call:(?P<name>\\w+)",
"repeats": true,
"transform": {
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"type": "function"
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}
|