Upload skillrep-embedding-0.6b

.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
+}

README.md

@@ -0,0 +1,127 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-Embedding-0.6B
+library_name: sentence-transformers
+pipeline_tag: feature-extraction
+tags:
+  - transformers
+  - sentence-similarity
+  - retrieval
+  - agent-skills
+  - skill-routing
+  - skillcorpus
+  - contrastive-learning
+  - text-embeddings-inference
+language:
+  - en
+---
+
+# skillrep-embedding-0.6b
+
+A bi-encoder for **agent-skill retrieval**: given a task description, embed it and
+the skill documents into one space so the relevant skills come back by nearest
+neighbour. Fine-tuned from
+[Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)
+with an in-batch InfoNCE loss on LLM-written task requests, each paired with a
+section of its skill and contrasted with mined hard negatives.
+
+This is the encoder of SkillRep, where it is combined with query-side and
+document-side expansion, background projection and a BM25 channel. It indexes a
+skill by its **body** only, unlike
+[skillcorpus-embedding-0.6b](https://huggingface.co/EverMind-AI/skillcorpus-embedding-0.6b),
+which indexes `name | description | body`. Skill documents follow the schema of
+[skillcorpus-demo-1k](https://huggingface.co/datasets/EverMind-AI/skillcorpus-demo-1k).
+
+| Property | Value |
+|---|---|
+| Parameters | 596M |
+| Hidden size | 1024 (embedding dimension) |
+| Layers | 28 |
+| Precision | bfloat16 |
+| Pooling | last token |
+| Normalization | L2 |
+
+Requires `transformers>=4.56` (the `dtype=` argument was named `torch_dtype=`
+before that) or `sentence-transformers>=3.0`.
+
+## Usage
+
+The two sides are encoded asymmetrically: a task description carries an
+instruction prefix (no space between the prefix and the task), and a skill is its
+bare body. Encode them this way or retrieval quality drops. Embeddings come back
+L2-normalized, so cosine similarity is a plain dot product.
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoModel, AutoTokenizer
+
+MODEL = "EverMind-AI/skillrep-embedding-0.6b"
+tok = AutoTokenizer.from_pretrained(MODEL, padding_side="left")
+model = AutoModel.from_pretrained(MODEL, dtype=torch.bfloat16).cuda().eval()
+
+QUERY_INSTRUCTION = (
+    "Instruct: Given a coding task description, retrieve the most relevant "
+    "skill document that would help an agent complete the task\nQuery:"
+)
+
+
+def last_token_pool(hidden, attention_mask):
+    if attention_mask[:, -1].sum() == attention_mask.shape[0]:   # left padding
+        return hidden[:, -1]
+    idx = attention_mask.sum(dim=1) - 1
+    return hidden[torch.arange(hidden.shape[0], device=hidden.device), idx]
+
+
+def embed(texts, max_length=4096):
+    enc = tok(texts, padding=True, truncation=True,
+              max_length=max_length, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        out = model(**enc).last_hidden_state
+    return F.normalize(last_token_pool(out, enc["attention_mask"]), p=2, dim=1)
+
+
+query = embed([QUERY_INSTRUCTION + "resolve conflicts after a git merge"])
+docs = embed(["# Resolving merge conflicts\n1. Run `git status` to list conflicted files ...",
+              "# Sourdough\nMix flour, water and starter ..."])
+print((query @ docs.T).tolist())
+```
+
+Exact scores shift in the last decimal with dtype, hardware and batch
+composition; the ordering is what matters.
+
+### With sentence-transformers
+
+The repo ships a sentence-transformers configuration (last-token pooling + L2
+normalization, and the query instruction registered as the `query` prompt), so
+this path is equivalent to the code above, up to bf16 noise:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+st = SentenceTransformer("EverMind-AI/skillrep-embedding-0.6b")
+q = st.encode(["resolve conflicts after a git merge"], prompt_name="query")
+d = st.encode(["# Resolving merge conflicts\n1. Run `git status` to list conflicted files ...",
+               "# Sourdough\nMix flour, water and starter ..."])
+print(st.similarity(q, d))
+```
+
+Pass `prompt_name="query"` for tasks and nothing for skill documents.
+
+### Input lengths
+
+| input | limit |
+|---|---|
+| task description (after the instruction prefix) | not cut by characters; 4,096 tokens |
+| skill `body` | first 8,000 characters; 4,096 tokens |
+
+## Intended use
+
+First-stage retrieval over a large skill registry: encode the registry offline,
+encode each incoming task online and take the top K by cosine similarity. Not a
+generative model: it produces embeddings, not answers.
+
+## License
+
+Apache-2.0, inherited from the base model. Skills keep their own upstream
+licenses.

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,85 @@
+{%- 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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "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 coding task description, retrieve the most relevant skill document that would help an agent complete the task\nQuery:",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2eaa3f5a42dd1385495392eec317f53472c791f9557209accf9200e80171549d
+size 1191586416

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 4096,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_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|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+oid sha256:93462ed9adb70b9a3924827df838fd18d484caa3471967fea849c300c476cfb0
+size 11423971

tokenizer_config.json

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+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
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+      "lstrip": false,
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+      "rstrip": false,
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+      "content": "<|vision_end|>",
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+      "rstrip": false,
+      "single_word": false,
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+      "lstrip": false,
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+      "rstrip": false,
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+      "special": false
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+      "special": false
+    }
+  },
+  "additional_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|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}