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feat(florence2): add Florence-2 image-to-text support #1098

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04dac65
feat(florence2): add Florence-2 image-to-text support
timenick Jul 13, 2026
1801369
Merge remote-tracking branch 'origin/main' into timenick-zhiwang-add-…
timenick Jul 13, 2026
38a1f29
fix(florence2): satisfy type checking
timenick Jul 13, 2026
8949c2e
test(onnx): create shape inference temp parents
timenick Jul 13, 2026
fc976a1
test(onnx): use consistent import style
timenick Jul 13, 2026
452809a
Remove recipe README changes
ssss141414 Jul 22, 2026
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1 change: 1 addition & 0 deletions docs/reference/index.md
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,7 @@ stages based on the target device and precision.
| `dtype` | `str \| null` | Data type (e.g., `float32`, `int64`). |
| `shape` | `list[int \| str] \| null` | Tensor shape (e.g., `[1, 3, 224, 224]`). String entries declare symbolic dynamic axes and use size `1` for dummy inputs. |
| `value_range` | `[float, float] \| null` | Min/max for dummy tensor generation. |
| `dummy_value_runs` | `list[[int, int \| float]] \| null` | Run-length encoded deterministic dummy values. Generated configs use this only when it compactly preserves semantic input values; runs must fill the concrete tensor shape exactly. |

---

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289 changes: 289 additions & 0 deletions examples/recipes/microsoft_Florence-2-base/image-to-text_fp16_config_decoder.json
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@@ -0,0 +1,289 @@
{
"export": {
"opset_version": 17,
"batch_size": 1,
"export_params": true,
"do_constant_folding": true,
"verbose": false,
"dynamo": false,
"enable_hierarchy_tags": true,
"clean_onnx": false,
"hierarchy_tag_format": "full",
"input_tensors": [
{
"name": "decoder_input_ids",
"dtype": "int32",
"shape": [
1,
1
],
"value_range": [
0,
51289
]
},
{
"name": "encoder_hidden_states",
"dtype": "float32",
"shape": [
1,
16,
768
],
"value_range": [
0,
1
]
},
{
"name": "decoder_attention_mask",
"dtype": "int64",
"shape": [
1,
1024
],
"dummy_value_runs": [
[
1024,
1
]
]
},
{
"name": "cache_position",
"dtype": "int64",
"shape": [
1
]
},
{
"name": "past_0_key",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_0_value",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_1_key",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_1_value",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_2_key",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_2_value",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_3_key",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_3_value",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_4_key",
"dtype": "float32",
"shape": [
1,
12,
1024,
64
],
"value_range": [
0,
1
]
},
{
"name": "past_4_value",
"dtype": "float32",
"shape": [
1,
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1024,
64
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"value_range": [
0,
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},
{
"name": "past_5_key",
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1,
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1024,
64
],
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0,
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},
{
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"shape": [
1,
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1024,
64
],
"value_range": [
0,
1
]
}
],
"output_tensors": [
{
"name": "logits"
},
{
"name": "present_0_key"
},
{
"name": "present_0_value"
},
{
"name": "present_1_key"
},
{
"name": "present_1_value"
},
{
"name": "present_2_key"
},
{
"name": "present_2_value"
},
{
"name": "present_3_key"
},
{
"name": "present_3_value"
},
{
"name": "present_4_key"
},
{
"name": "present_4_value"
},
{
"name": "present_5_key"
},
{
"name": "present_5_value"
}
],
"dynamic_axes": {
"encoder_hidden_states": {
"0": "batch_size",
"1": "source_sequence_length"
}
}
},
"optim": {
"gelu_fusion": true,
"layer_norm_fusion": true,
"matmul_add_fusion": true
},
"quant": null,
"compile": null,
"loader": {
"task": "text2text-generation",
"model_class": "Florence2DecoderWrapper",
"model_type": "florence2",
"trust_remote_code": true
}
}
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