init model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": true,
+  "include_prompt": true
+}

2_Normalize/config.json

@@ -0,0 +1,3 @@
+{
+  "normalize_embeddings": true
+}

README.md

@@ -0,0 +1,130 @@
+---
+language:
+- en
+- zh
+- multilingual
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- embedding
+- text-embedding
+- retrieval
+pipeline_tag: sentence-similarity
+base_model: Qwen/Qwen3-Embedding-0.6B
+---
+
+# Octen-Embedding-0.6B
+
+Octen-Embedding-0.6B is a text embedding model designed for semantic search and retrieval tasks. This model is fine-tuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) and supports multiple languages, providing high-quality embeddings for various applications.
+
+## Model Details
+
+- **Base Model**: [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)
+- **Model Size**: 0.6B parameters
+- **Max Sequence Length**: 32,768 tokens
+- **Embedding Dimension**: 1024
+- **Languages**: English, Chinese, and multilingual support
+- **Training Method**: LoRA fine-tuning
+
+## Usage
+
+### Using Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("bflhc/Octen-Embedding-0.6B")
+
+# Encode sentences
+sentences = [
+    "This is an example sentence",
+    "Each sentence is converted to a vector"
+]
+
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# Output: (2, 1024)
+
+# Compute similarity
+from sentence_transformers.util import cos_sim
+similarity = cos_sim(embeddings[0], embeddings[1])
+print(f"Similarity: {similarity.item():.4f}")
+```
+
+### Using Transformers
+
+```python
+from transformers import AutoModel, AutoTokenizer
+import torch
+import torch.nn.functional as F
+
+tokenizer = AutoTokenizer.from_pretrained("bflhc/Octen-Embedding-0.6B", padding_side="left")
+model = AutoModel.from_pretrained("bflhc/Octen-Embedding-0.6B")
+model.eval()
+
+def encode(texts):
+    inputs = tokenizer(texts, padding=True, truncation=True,
+                      max_length=8192, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        # Use last token embedding
+        embeddings = outputs.last_hidden_state[:, -1, :]
+        # Normalize embeddings
+        embeddings = F.normalize(embeddings, p=2, dim=1)
+
+    return embeddings
+
+# Example usage
+texts = ["Hello world", "你好世界"]
+embeddings = encode(texts)
+similarity = torch.matmul(embeddings[0], embeddings[1])
+print(f"Similarity: {similarity.item():.4f}")
+```
+
+## Recommended Use Cases
+
+- Semantic search and information retrieval
+- Document similarity and clustering
+- Question answering
+- Cross-lingual retrieval
+- Text classification with embeddings
+
+## Limitations
+
+- Performance may vary across different domains and languages
+- Very long documents (>32K tokens) require truncation
+- Optimized for retrieval tasks, not for text generation
+
+## License
+
+This model is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+This model is derived from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B), which is also licensed under Apache License 2.0.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{octen-embedding-2026,
+  title={Octen-Embedding-0.6B: A Fine-tuned Multilingual Text Embedding Model},
+  author={Octen Team},
+  year={2026},
+  url={https://huggingface.co/bflhc/Octen-Embedding-0.6B}
+}
+```
+
+Please also cite the base model:
+
+```bibtex
+@article{qwen3embedding,
+  title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
+  author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
+  journal={arXiv preprint arXiv:2506.05176},
+  year={2025}
+}
+```

added_tokens.json

@@ -0,0 +1,28 @@
+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
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+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
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+  "<|file_sep|>": 151664,
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+  "<|fim_pad|>": 151662,
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+  "<|fim_suffix|>": 151661,
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+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,60 @@
+{
+  "architectures": [
+    "Qwen3Model"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151669
+}

config_sentence_transformers.json

@@ -0,0 +1,8 @@
+{
+  "prompts": {
+    "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
+    "document": " "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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modules.json

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+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
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+    "<|video_pad|>"
+  ],
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+    "content": "<|im_end|>",
+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+size 11423705

tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}