Super squash history while preserving current contents

.gitattributes

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README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+base_model: Qwen/Qwen3-Reranker-0.6B
+pipeline_tag: text-ranking
+tags:
+- reranking
+- cross-encoder
+- agent-tools
+- skill-routing
+- qwen3
+- listwise-learning
+---
+
+# SR-Rank-0.6B
+
+`SR-Rank-0.6B` is a fine-tuned cross-encoder reranker for skill routing. It is designed to score a small candidate set of retrieved skills against a task query and select the single most relevant skill for an LLM agent.
+
+## Model Summary
+
+- Base model: `Qwen/Qwen3-Reranker-0.6B`
+- Architecture: causal-LM-style cross-encoder reranker
+- Input: query + candidate skill text, formatted into the Qwen reranker prompt template
+- Output: scalar relevance score computed as `logit(yes) - logit(no)`
+- Intended use: rerank top-K candidates from a first-stage retriever such as `SR-Emb-0.6B`
+
+This model should be used on a candidate list, not as a standalone retriever over an 80K-scale corpus.
+
+## Intended Uses
+
+Use `SR-Rank-0.6B` after a first-stage retriever has already narrowed a large corpus to a candidate set, for example:
+
+1. Retrieve top-20 skills with `pipizhao/SkillRouter-Embedding-0.6B`.
+2. Score each candidate with `pipizhao/SkillRouter-Reranker-0.6B`.
+3. Sort by `yes - no` score.
+4. Take the top-1 or top-N skills for downstream planning or execution.
+
+This model is not intended for free-form generation, chat, or long-document retrieval without candidate pruning.
+
+## How to Use
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_ID = "pipizhao/SkillRouter-Reranker-0.6B"
+
+
+def format_rerank_prompt(name, desc, body, query_text, desc_max=500, body_max=2000):
+    instruction = (
+        "Given a task description, judge whether the skill document "
+        "is relevant and useful for completing the task"
+    )
+    doc_text = f"{name} | {desc[:desc_max]} | {body[:body_max]}"
+    return (
+        f"<Instruct>: {instruction}\n\n"
+        f"<Query>: {query_text}\n\n"
+        f"<Document>: {doc_text}"
+    )
+
+
+def build_qwen_reranker_inputs(tokenizer, prompt, max_length=4096):
+    prefix = (
+        '<|im_start|>system\nJudge whether the Document meets the requirements '
+        'based on the Query and the Instruct provided. Note that the answer can '
+        'only be "yes" or "no".<|im_end|>\n<|im_start|>user\n'
+    )
+    suffix = '<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n'
+    prefix_tokens = tokenizer.encode(prefix, add_special_tokens=False)
+    suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)
+    tokens = tokenizer(
+        prompt,
+        padding=False,
+        truncation=True,
+        max_length=max_length - len(prefix_tokens) - len(suffix_tokens),
+        return_attention_mask=False,
+    )["input_ids"]
+    input_ids = prefix_tokens + tokens + suffix_tokens
+    return input_ids
+
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, padding_side="left")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+)
+model = model.eval().to("cuda" if torch.cuda.is_available() else "cpu")
+
+token_yes = tokenizer.convert_tokens_to_ids("yes")
+token_no = tokenizer.convert_tokens_to_ids("no")
+
+query = "Implement a feature branch workflow with PR checks."
+candidates = [
+    {
+        "name": "moai-foundation-git",
+        "desc": "Git workflow conventions",
+        "body": "# Git Workflow ...",
+    },
+    {
+        "name": "concurrency-control",
+        "desc": "Mutex patterns for CI",
+        "body": "# Concurrency Control ...",
+    },
+]
+
+scores = []
+for cand in candidates:
+    prompt = format_rerank_prompt(cand["name"], cand["desc"], cand["body"], query)
+    input_ids = build_qwen_reranker_inputs(tokenizer, prompt)
+    input_ids = torch.tensor([input_ids], device=model.device)
+    attention_mask = torch.ones_like(input_ids)
+    with torch.no_grad():
+        logits = model(input_ids=input_ids, attention_mask=attention_mask).logits[:, -1, :]
+    score = (logits[:, token_yes] - logits[:, token_no]).item()
+    scores.append(score)
+
+best_idx = max(range(len(scores)), key=lambda i: scores[i])
+print(best_idx, scores)
+```
+
+## Citation
+
+If you use this model, please cite the SkillRouter paper once the preprint is public.
+
+```bibtex
+@misc{zheng2026skillrouterskillroutingllm,
+      title={SkillRouter: Skill Routing for LLM Agents at Scale}, 
+      author={YanZhao Zheng and ZhenTao Zhang and Chao Ma and YuanQiang Yu and JiHuai Zhu and Yong Wu and Tianze Xu and Baohua Dong and Hangcheng Zhu and Ruohui Huang and Gang Yu},
+      year={2026},
+      eprint={2603.22455},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2603.22455}, 
+}
+```

added_tokens.json

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config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
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+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 40960,
+  "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,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151669
+}

generation_config.json

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+{
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+  "do_sample": true,
+  "eos_token_id": [
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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.51.2"
+}

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special_tokens_map.json

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+    "<|im_end|>",
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+  }
+}

tokenizer.json

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tokenizer_config.json

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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
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+}