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Browse files- README.md +25 -36
- config.json +358 -0
- model.rknn +1 -1
- model_b1_s256.rknn +1 -1
- rknn/model_o1.rknn +1 -1
- rknn/model_o2.rknn +1 -1
- rknn/model_o3.rknn +1 -1
- rknn/model_w8a8.rknn +1 -1
README.md
CHANGED
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@@ -12,9 +12,11 @@ tags:
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- npu
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- rk-transformers
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- rk3588
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pipeline_tag: sentence-similarity
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-
model_name: all-MiniLM-L6-v2
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base_model: sentence-transformers/all-MiniLM-L6-v2
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---
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# all-MiniLM-L6-v2 (RKNN2)
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@@ -27,7 +29,7 @@ base_model: sentence-transformers/all-MiniLM-L6-v2
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- **Original Model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
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- **Target Platform:** rk3588
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- **rknn-toolkit2 Version:** 2.3.2
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-
- **rk-transformers Version:** 0.
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### Available Model Files
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### Installation
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-
Install `rk-transformers` to use this model:
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```bash
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pip install rk-transformers
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```
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#### Sentence Transformers
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```python
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-
from
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from rktransformers import patch_sentence_transformer
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-
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-
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-
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-
# Load model with RKNN backend
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model = SentenceTransformer(
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"rktransformers/all-MiniLM-L6-v2",
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backend="rknn",
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model_kwargs={
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"platform": "rk3588",
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"core_mask": "auto",
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}
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)
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-
# Generate embeddings
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sentences = ["This is a test sentence", "Another example"]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# Load specific optimized/quantized model file
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-
model =
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-
"
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backend="rknn",
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model_kwargs={
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"platform": "rk3588",
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"file_name": "rknn/model_w8a8.rknn"
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}
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)
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```
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-
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```python
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-
from rktransformers import
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from transformers import AutoTokenizer
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-
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-
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-
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"rktransformers/all-MiniLM-L6-v2",
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platform="rk3588",
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core_mask="auto",
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)
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-
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inputs = tokenizer(
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["Sample text for encoding"],
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padding="max_length",
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max_length=256,
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truncation=True,
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return_tensors="np"
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)
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-
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outputs = model(**inputs)
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-
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# Load specific optimized/quantized model file
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-
model =
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-
"
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platform="rk3588",
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file_name="rknn/model_w8a8.rknn"
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)
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## Configuration
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-
The full configuration for all exported RKNN models is available in the [
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</details>
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# all-MiniLM-L6-v2
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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- npu
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- rk-transformers
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- rk3588
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+
datasets:
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+
- stanfordnlp/snli
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pipeline_tag: sentence-similarity
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base_model: sentence-transformers/all-MiniLM-L6-v2
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+
model_name: all-MiniLM-L6-v2
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---
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# all-MiniLM-L6-v2 (RKNN2)
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- **Original Model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
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- **Target Platform:** rk3588
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- **rknn-toolkit2 Version:** 2.3.2
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+
- **rk-transformers Version:** 0.3.0
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### Available Model Files
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### Installation
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+
Install `rk-transformers` with inference dependencies to use this model:
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```bash
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+
pip install rk-transformers[inference]
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```
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#### Sentence Transformers
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```python
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from rktransformers import RKSentenceTransformer
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model = RKSentenceTransformer(
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"rk-transformers/all-MiniLM-L6-v2",
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model_kwargs={
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"platform": "rk3588",
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"core_mask": "auto",
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}
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)
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sentences = ["This is a test sentence", "Another example"]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# Load specific optimized/quantized model file
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+
model = RKSentenceTransformer(
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"rk-transformers/all-MiniLM-L6-v2",
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model_kwargs={
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"platform": "rk3588",
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"file_name": "rknn/model_w8a8.rknn"
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}
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)
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```
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+
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+
#### RK-Transformers API
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```python
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from rktransformers import RKModelForFeatureExtraction
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("rk-transformers/all-MiniLM-L6-v2")
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model = RKModelForFeatureExtraction.from_pretrained(
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"rk-transformers/all-MiniLM-L6-v2",
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platform="rk3588",
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core_mask="auto",
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)
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inputs = tokenizer("My name is Philipp and I live in Germany.", return_tensors="np")
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outputs = model(**inputs)
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last_hidden_state = outputs.last_hidden_state
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print(last_hidden_state.shape)
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# Load specific optimized/quantized model file
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+
model = RKModelForFeatureExtraction.from_pretrained(
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"rk-transformers/all-MiniLM-L6-v2",
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platform="rk3588",
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file_name="rknn/model_w8a8.rknn"
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)
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## Configuration
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+
The full configuration for all exported RKNN models is available in the [config.json](./config.json) file.
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</details>
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+
---
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+
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| 119 |
# all-MiniLM-L6-v2
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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config.json
CHANGED
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@@ -18,6 +18,364 @@
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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| 21 |
"torch_dtype": "float32",
|
| 22 |
"transformers_version": "4.55.4",
|
| 23 |
"type_vocab_size": 2,
|
|
|
|
| 18 |
"num_hidden_layers": 6,
|
| 19 |
"pad_token_id": 0,
|
| 20 |
"position_embedding_type": "absolute",
|
| 21 |
+
"rknn": {
|
| 22 |
+
"model.rknn": {
|
| 23 |
+
"batch_size": 1,
|
| 24 |
+
"custom_string": null,
|
| 25 |
+
"dynamic_input": null,
|
| 26 |
+
"float_dtype": "float16",
|
| 27 |
+
"inputs_yuv_fmt": null,
|
| 28 |
+
"max_seq_length": 512,
|
| 29 |
+
"mean_values": null,
|
| 30 |
+
"model_input_names": [
|
| 31 |
+
"input_ids",
|
| 32 |
+
"attention_mask"
|
| 33 |
+
],
|
| 34 |
+
"opset": 19,
|
| 35 |
+
"optimization": {
|
| 36 |
+
"compress_weight": false,
|
| 37 |
+
"enable_flash_attention": true,
|
| 38 |
+
"model_pruning": false,
|
| 39 |
+
"optimization_level": 0,
|
| 40 |
+
"remove_reshape": false,
|
| 41 |
+
"remove_weight": false,
|
| 42 |
+
"sparse_infer": false
|
| 43 |
+
},
|
| 44 |
+
"quantization": {
|
| 45 |
+
"auto_hybrid_cos_thresh": 0.98,
|
| 46 |
+
"auto_hybrid_euc_thresh": null,
|
| 47 |
+
"dataset_columns": null,
|
| 48 |
+
"dataset_name": null,
|
| 49 |
+
"dataset_size": 128,
|
| 50 |
+
"dataset_split": null,
|
| 51 |
+
"dataset_subset": null,
|
| 52 |
+
"do_quantization": false,
|
| 53 |
+
"quant_img_RGB2BGR": false,
|
| 54 |
+
"quantized_algorithm": "normal",
|
| 55 |
+
"quantized_dtype": "w8a8",
|
| 56 |
+
"quantized_hybrid_level": 0,
|
| 57 |
+
"quantized_method": "channel"
|
| 58 |
+
},
|
| 59 |
+
"rktransformers_version": "0.3.0",
|
| 60 |
+
"single_core_mode": false,
|
| 61 |
+
"std_values": null,
|
| 62 |
+
"target_platform": "rk3588",
|
| 63 |
+
"task": "auto",
|
| 64 |
+
"task_kwargs": null
|
| 65 |
+
},
|
| 66 |
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