Add new SentenceTransformer model

0_KananaEmbeddingWrapper/kanana2vec/config.json

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+{
+  "_name_or_path": "/data/public/model_registry/huggingface/hub/models--datalama--kanana-nano-2.1b-embedding/snapshots/77c87d91735397f0416f5bf595ba48f6af97333a/0_KananaEmbeddingWrapper/kanana2vec",
+  "architectures": [
+    "Kanana2VecModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_kanana2vec.Kanana2VecConfig",
+    "AutoModel": "modeling_kanana2vec.Kanana2VecModel"
+  },
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1792,
+  "initializer_range": 0.02,
+  "intermediate_size": 8064,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "kanana2vec",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128001,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

0_KananaEmbeddingWrapper/kanana2vec/configuration_kanana2vec.py

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+# coding=utf-8
+# Copyright 2024 Kakao Corp. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Kanana2Vec model configuration"""
+
+from transformers import AutoConfig
+from transformers.models.llama import LlamaConfig
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+
+
+class Kanana2VecConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Kanana2VecModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Kanana2VecModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+            Llama 2 up to 4096, CodeLlama up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
+            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
+            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+        head_dim (`int`, *optional*):
+            The attention head dimension. If None, it will default to hidden_size // num_heads"""
+
+    model_type = "kanana2vec"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        head_dim=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, copy it it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+class BiLlamaConfig(LlamaConfig):
+    model_type = "billama"

0_KananaEmbeddingWrapper/kanana2vec/model-00001-of-00002.safetensors

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0_KananaEmbeddingWrapper/kanana2vec/model.safetensors.index.json

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0_KananaEmbeddingWrapper/kanana_embedding_config.json

@@ -0,0 +1,7 @@
+{
+  "model_name_or_path": "/data/public/model_registry/huggingface/hub/models--datalama--kanana-nano-2.1b-embedding/snapshots/77c87d91735397f0416f5bf595ba48f6af97333a/0_KananaEmbeddingWrapper/kanana2vec",
+  "trust_remote_code": true,
+  "device": "cpu",
+  "hidden_size": 1792,
+  "max_seq_length": 8192
+}

0_KananaEmbeddingWrapper/kanana_embedding_wrapper.py

@@ -0,0 +1,182 @@
+import os
+import json
+import torch
+from torch import nn
+import torch.nn.functional as F
+from typing import List, Dict
+
+from transformers import AutoModel
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+class KananaEmbeddingWrapper(nn.Module):
+    def __init__(self, model_name_or_path: str, trust_remote_code=True, device: str = "cpu", max_seq_length:int=None):
+        """
+        Initialize the KananaEmbeddingWrapper.
+        
+        Args:
+            model_name_or_path: Path or name of the pretrained model
+            trust_remote_code: Whether to trust remote code when loading the model
+            device: Device to load the model on (e.g., 'cpu', 'cuda')
+        """
+        super(KananaEmbeddingWrapper, self).__init__()
+        self.model_name_or_path = model_name_or_path
+        self.trust_remote_code = trust_remote_code
+        self.device = device
+        self.kanana2vec = AutoModel.from_pretrained(
+            model_name_or_path, trust_remote_code=trust_remote_code
+        ).to(self.device)
+
+        self.max_seq_length = max_seq_length if max_seq_length is not None else self.kanana2vec.config.max_position_embeddings
+
+    def get_sentence_embedding_dimension(self) -> int:
+        """
+        Returns the dimension of the sentence embeddings.
+        
+        Returns:
+            Dimensionality of the sentence embeddings
+        """
+        return self.kanana2vec.config.hidden_size
+    
+    def get_max_seq_length(self) -> int:
+        """
+        Returns the maximum sequence length this module can process.
+        
+        Returns:
+            Maximum sequence length
+        """
+        return self.max_seq_length
+    
+    def tokenize(self, texts: List[str]) -> Dict[str, torch.Tensor]:
+        """
+        Tokenize input texts.
+        
+        Args:
+            texts: List of input texts to tokenize
+            
+        Returns:
+            Dictionary containing tokenized inputs
+        """
+        return self.kanana2vec.tokenizer(
+            texts,
+            padding=True,
+            return_token_type_ids=False,
+            return_tensors="pt",
+            truncation=True,
+            max_length=self.max_seq_length
+        ).to(self.device)
+
+    def forward(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+        """
+        Forward pass of the module.
+        
+        Args:
+            features: Dictionary with inputs including 'input_ids', 'attention_mask', etc.
+        
+        Returns:
+            Dictionary with updated features including 'sentence_embedding'
+        """
+        # Extract only the required features for the model
+        model_inputs = self._extract_model_inputs(features)
+        
+        # Create pool mask considering prompt length if available
+        model_inputs["pool_mask"] = self._create_pool_mask(features)
+        
+        # Get embeddings from the model and normalize
+        embedding = self.kanana2vec.forward(**model_inputs).embedding
+        normalized_embedding = F.normalize(embedding, p=2, dim=1)
+        
+        # Update features with sentence embedding
+        features['sentence_embedding'] = normalized_embedding
+        return features
+    
+    def _extract_model_inputs(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+        """
+        Extract only the inputs needed for the model.
+        
+        Args:
+            features: Complete feature dictionary
+            
+        Returns:
+            Dictionary with only the required keys for the model
+        """
+        return {k: v for k, v in features.items() if k in ['input_ids', 'attention_mask']}
+    
+    def _create_pool_mask(self, features: Dict[str, torch.Tensor]) -> torch.Tensor:
+        """
+        Create a pool mask based on attention mask and prompt length.
+        
+        Args:
+            features: Feature dictionary containing attention_mask and optionally prompt_length
+            
+        Returns:
+            Pool mask tensor
+        """
+        pool_mask = features['attention_mask'].clone()
+        if "prompt_length" in features:
+            pool_mask[:, :features['prompt_length']] = 0
+        return pool_mask
+    
+    def get_config_dict(self) -> Dict:
+        """
+        Returns a dictionary with the module's configuration.
+        
+        Returns:
+            Dictionary with module configuration
+        """
+        return {
+            "model_name_or_path": self.model_name_or_path,
+            "trust_remote_code": self.trust_remote_code,
+            "device": self.device,
+            "hidden_size": self.get_sentence_embedding_dimension(),
+            "max_seq_length": self.get_max_seq_length()
+        }
+    
+    def save(self, save_dir: str) -> None:
+        """
+        Saves the module's configuration and model to the specified directory.
+        
+        Args:
+            save_dir: Directory to save the module configuration
+        """
+        os.makedirs(save_dir, exist_ok=True)
+        
+        # Save model configuration
+        config_path = os.path.join(save_dir, "kanana_embedding_config.json")
+        with open(config_path, 'w', encoding='utf-8') as f:
+            json.dump(self.get_config_dict(), f, ensure_ascii=False, indent=2)
+        
+        # Save the underlying model
+        model_save_path = os.path.join(save_dir, "kanana2vec")
+        self.kanana2vec.save_pretrained(model_save_path)
+        
+        print(f"KananaEmbeddingWrapper model saved to {save_dir}")
+    
+    @staticmethod
+    def load(load_dir: str, device: str = "cpu") -> 'KananaEmbeddingWrapper':
+        """
+        Loads a KananaEmbeddingWrapper model from the specified directory.
+        
+        Args:
+            load_dir: Directory containing the saved module
+            device: Device to load the model on
+            
+        Returns:
+            Initialized KananaEmbeddingWrapper
+        """
+        # Load configuration
+        config_path = os.path.join(load_dir, "kanana_embedding_config.json")
+        with open(config_path, 'r', encoding='utf-8') as f:
+            config = json.load(f)
+        
+        # Use the saved model path
+        model_load_path = os.path.join(load_dir, "kanana2vec")
+        
+        # Create instance with saved configuration
+        instance = KananaEmbeddingWrapper(
+            model_name_or_path=model_load_path,
+            trust_remote_code=config.get("trust_remote_code", True),
+            device=device  # Use the provided device or default
+        )
+        
+        return instance

README.md

@@ -0,0 +1,157 @@
+---
+language:
+- en
+- ko
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+model_id: datalama/kanana-nano-2.1b-embedding
+repo: datalama/kanana-nano-2.1b-embedding
+developers: datalama
+license: cc-by-nc-4.0
+---
+
+# Sentence-Transformers Compatible Kanana-Nano-2.1b-Embedding
+
+This repository contains a sentence-transformers compatible version of the [Kanana-Nano-2.1b-Embedding](https://huggingface.co/kakaocorp/kanana-nano-2.1b-embedding) model developed by Kakao.
+
+For detailed information about the model architecture, training methodology, and comprehensive performance benchmarks, please refer to the [original model repository](https://huggingface.co/kakaocorp/kanana-nano-2.1b-embedding) and the [Kanana technical report](https://arxiv.org/abs/2502.18934).
+
+## Key Adaptations
+
+This version has been modified to work seamlessly with the sentence-transformers library with the following changes:
+
+* Implemented `KananaEmbeddingWrapper` module to enable loading via SentenceTransformer
+* Added L2 normalization within the `KananaEmbeddingWrapper`'s forward method
+* max_seq_length is fixed with 8192.
+* Embed the query prompt related parts into the model. You can encode the query with `query_name`.
+
+## Usage
+
+### Installation
+
+```bash
+pip install sentence-transformers
+```
+
+### Basic Usage
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True)
+
+# Encode sentences
+sentences = [
+    "이 문장은 한국어로 작성되었습니다.",
+    "This sentence is written in English."
+]
+
+embeddings = model.encode(sentences)
+```
+
+### Advanced Usage with Query/Passage Format
+
+* You can use `prompt_name` or `prompt`.
+
+```python
+import numpy as np
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True)
+
+# For retrieval tasks
+instruction = "Given a question, retrieve passages that answer the question"
+queries = [
+    "are judo throws allowed in wrestling?", 
+    "how to become a radiology technician in michigan?",
+]
+
+
+# You can encode query by prompt_name with predefiend prompt template.
+embedding_a = model.encode(queries, prompt_name="query")
+
+# You can directly encode the query with prompt.
+prompt_template = """Instruct: {instruction}\nQuery: """
+embedding_b = model.encode(queries, prompt=prompt_template.format(instruction=instruction))
+
+# compare input.
+np.allclose(embedding_a, embedding_b)
+# True
+```
+
+* Compare embedding with original code.
+
+```python
+import torch.nn.functional as F
+import numpy as np
+from transformers import AutoModel
+from sentence_transformers import SentenceTransformer
+
+# For retrieval tasks
+instruction = "Given a question, retrieve passages that answer the question"
+queries = [
+    "are judo throws allowed in wrestling?", 
+    "how to become a radiology technician in michigan?",
+]
+
+passages = [
+    "Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
+    "Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan.",
+]
+
+# compare originaml model and this model.
+model_a = AutoModel.from_pretrained("kakaocorp/kanana-nano-2.1b-embedding",trust_remote_code=True,).to("cpu")
+model_b = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True)
+
+# original encoding method. 
+max_length = 512
+query_embeddings = model_a.encode(queries, instruction=instruction, max_length=max_length)
+passage_embeddings = model_a.encode(passages, instruction="", max_length=max_length)
+
+query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
+passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)
+
+scores_a = (query_embeddings @ passage_embeddings.T) * 100
+
+# sentence_transformers compatible encoding method.
+query_embeddings = model_b.encode(queries, prompt_name="query")
+passage_embeddings = model_b.encode(passages)
+
+scores_b = (query_embeddings @ passage_embeddings.T) * 100
+
+# compare embedding
+np.allclose(scores_a.cpu().numpy(), scores_b)
+# True
+```
+
+Note: Unlike the original model, you don't need to manually perform L2 normalization as this is handled by the `KananaEmbeddingWrapper` module during the forward pass.
+
+## License
+
+This model is licensed under [CC-BY-NC-4.0](https://spdx.org/licenses/CC-BY-NC-4.0).
+
+## Citation
+
+If you use this model, please cite the original work:
+
+```
+@misc{kananallmteam2025kananacomputeefficientbilinguallanguage,
+      title={Kanana: Compute-efficient Bilingual Language Models}, 
+      author={Kanana LLM Team and Yunju Bak and Hojin Lee and Minho Ryu and Jiyeon Ham and Seungjae Jung and Daniel Wontae Nam and Taegyeong Eo and Donghun Lee and Doohae Jung and Boseop Kim and Nayeon Kim and Jaesun Park and Hyunho Kim and Hyunwoong Ko and Changmin Lee and Kyoung-Woon On and Seulye Baeg and Junrae Cho and Sunghee Jung and Jieun Kang and EungGyun Kim and Eunhwa Kim and Byeongil Ko and Daniel Lee and Minchul Lee and Miok Lee and Shinbok Lee and Gaeun Seo},
+      year={2025},
+      eprint={2502.18934},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.18934}, 
+}
+```
+
+## Acknowledgements
+
+- Original model developed by the Kanana LLM Team at Kakao
+- Adaptation to sentence-transformers format by datalama

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {
+    "query": "Instruct: Given a question, retrieve passages that answer the question\nQuery: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

@@ -0,0 +1,8 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "0_KananaEmbeddingWrapper",
+    "type": "kanana_embedding_wrapper.KananaEmbeddingWrapper"
+  }
+]