Publish Muninn v1

.gitattributes

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1_Pooling/config.json

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+{
+    "embedding_dimension": 2048,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

LICENSE

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README.md

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+---
+license: apache-2.0
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+base_model: voyageai/voyage-4-nano
+tags:
+  - sentence-transformers
+  - code-retrieval
+  - multilingual
+  - matryoshka
+datasets:
+  - BrokkAI/Quarry
+---
+
+# Muninn
+
+Muninn is a 346M-parameter multilingual retriever for natural-language-query →
+code-function retrieval, with an 8,192-token serving context and 2,048-dimensional
+embeddings.
+
+It is trained from `voyageai/voyage-4-nano` and uses a bidirectional Qwen3 encoder.
+Muninn supports Matryoshka truncation at 512, 1,024, 1,536, and 2,048 dimensions. We
+recommend 2,048 dimensions: that is the native evaluation setting, and truncating to 512
+cost roughly two recall points at depth on Quarry.
+
+## Usage
+
+Muninn requires `trust_remote_code=True` because this repository includes the custom
+`Qwen3BidirectionalModel` implementation.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer(
+    "BrokkAI/Muninn",
+    trust_remote_code=True,
+    truncate_dim=2048,
+)
+model.max_seq_length = 8192
+
+queries = ["Where is retry backoff calculated for failed HTTP requests?"]
+documents = [
+    "src/net/client.py/HttpClient/retry_delay\n"
+    "class HttpClient:def retry_delay(self, attempt):\n"
+    "    return min(60, 2 ** attempt)"
+]
+
+query_embeddings = model.encode(queries, prompt_name="query", normalize_embeddings=True)
+document_embeddings = model.encode(
+    documents, prompt_name="document", normalize_embeddings=True
+)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+```
+
+The exact inference prompts are:
+
+```text
+query:    Represent the query for retrieving supporting documents: 
+document: Represent the document for retrieval: 
+```
+
+### Document format
+
+Quarry results use the header format produced by `swerank_document_text()` before the
+document prompt is applied. For a free function:
+
+```text
+{path}/{function_name}
+{source}
+```
+
+For a class method:
+
+```text
+{path}/{ClassName}/{function_name}
+class {ClassName}:{source}
+```
+
+The worked usage example above is therefore the exact representation of a method named
+`HttpClient.retry_delay` in `src/net/client.py`. Preserve this shape when comparing against
+reported results.
+
+## Quarry results
+
+[Quarry](https://huggingface.co/datasets/BrokkAI/Quarry) contains 6,525 synthetic behavioral
+queries over real repository revisions. The metric is strict all-gold micro recall@k: for
+each query, `|gold ∩ top-k| / |gold|`, followed by a flat mean over queries. Models use their
+native dimensions and the header document format above.
+
+**TODO:** Competitive results are being rerun and will be added here when the complete
+comparison is finalized.
+
+## Languages
+
+C, C++, C#, Go, Java, JavaScript, PHP, Python, Rust, Scala, and TypeScript.
+
+## License and attribution
+
+Muninn is released under the Apache License 2.0. It is derived from
+[`voyageai/voyage-4-nano`](https://huggingface.co/voyageai/voyage-4-nano), also released
+under Apache-2.0. See `LICENSE` for the full license text.

config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.12.0+cu130",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.3.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "Represent the document for retrieval: ",
+    "query": "Represent the query for retrieving supporting documents: "
+  },
+  "similarity_fn_name": "cosine",
+  "truncate_dim": 2048
+}

model.safetensors

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+size 692919144

modeling_qwen3_bidirectional.py

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+from typing import Callable
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, Qwen3Config, Qwen3Model
+from transformers.cache_utils import Cache
+from transformers.masking_utils import create_causal_mask
+from transformers.modeling_outputs import BaseModelOutputWithPooling
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs
+
+
+# From modeling_t5gemma.py
+def bidirectional_mask_function(attention_mask: torch.Tensor | None) -> Callable:
+    """
+    This creates bidirectional attention mask.
+    """
+
+    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
+        if attention_mask is None:
+            return torch.ones((), dtype=torch.bool)
+        return attention_mask[batch_idx, kv_idx].to(torch.bool)
+
+    return inner_mask
+
+
+class Qwen3BidirectionalModel(PreTrainedModel):
+    config_class = Qwen3Config
+    _supports_flash_attn = True
+    _supports_sdpa = True
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Qwen3Model(config)
+        self.linear = nn.Linear(config.hidden_size, config.num_labels, bias=False)
+        self.post_init()
+
+    def post_init(self):
+        super().post_init()
+        # Override to set all layers to non-causal attention. This'll work with attn_implementation="flash_attention_2" or "sdpa"
+        for layer in self.model.layers:
+            layer.self_attn.is_causal = False
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPooling:
+        if inputs_embeds is None:
+            inputs_embeds = self.model.embed_tokens(input_ids)
+            input_ids = None
+
+        # We construct a dummy tensor imitating initial positions
+        dummy_cache_position = torch.arange(
+            inputs_embeds.shape[1], device=inputs_embeds.device, dtype=torch.long
+        )
+        attention_mask = {
+            "full_attention": create_causal_mask(
+                config=self.config,
+                input_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                cache_position=dummy_cache_position,
+                past_key_values=None,
+                position_ids=position_ids,
+                or_mask_function=bidirectional_mask_function(attention_mask),
+            )
+        }
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        outputs.last_hidden_state = self.linear(outputs.last_hidden_state)
+        return outputs

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:297600527f1df1d03edf0268ebf340f551a7d5266d64cc8f1303e69390a17bfc
+size 11422159

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "max_length": 32768,
+  "model_max_length": 8192,
+  "pad_to_multiple_of": null,
+  "pad_token": "<|endoftext|>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "stride": 0,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": null
+}