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Arch-Router-1.5B-1.2.0.rkllm

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README.md

@@ -0,0 +1,172 @@
+---
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+language:
+- en
+library_name: transformers
+license: other
+license_name: katanemo-research
+license_link: https://huggingface.co/katanemo/Arch-Router-1.5B/blob/main/LICENSE
+pipeline_tag: text-generation
+tags:
+- routing
+- preference
+- arxiv:2506.16655
+- llm
+paper: https://arxiv.org/abs/2506.16655
+---
+
+# katanemo/Arch-Router-1.5B
+
+## Overview
+With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to operationalize the use of different models. However, existing LLM routing approaches are limited in two key ways: they evaluate performance using benchmarks that often fail to capture human preferences driven by subjective evaluation criteria, and they typically select from a limited pool of models. 
+
+We introduce a preference-aligned routing framework that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) -- offering a practical mechanism to encode preferences in routing decisions. Specifically, we introduce Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions. Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models. 
+
+This model is described in the paper: https://arxiv.org/abs/2506.16655, and powers [Arch](https://github.com/katanemo/arch) the open-source AI-native proxy for agents to enable preference-based routing in a seamless way.
+
+### How It Works
+
+To support effective routing, Arch-Router introduces two key concepts:
+- **Domain** – the high-level thematic category or subject matter of a request (e.g., legal, healthcare, programming).
+- **Action** – the specific type of operation the user wants performed (e.g., summarization, code generation, booking appointment, translation).
+
+Both domain and action configs are associated with preferred models or model variants. At inference time, Arch-Router analyzes the incoming prompt to infer its domain and action using semantic similarity, task indicators, and contextual cues. It then applies the user-defined routing preferences to select the model best suited to handle the request.
+
+### Key Features
+
+- **Structured Preference Routing**: Aligns prompt request with model strengths using explicit domain–action mappings.
+- **Transparent and Controllable**: Makes routing decisions transparent and configurable, empowering users to customize system behavior.
+- **Flexible and Adaptive**: Supports evolving user needs, model updates, and new domains/actions without retraining the router.
+- **Production-Ready Performance**: Optimized for low-latency, high-throughput applications in multi-model environments.
+
+# Requirements
+The code of Arch-Router-1.5B has been in the Hugging Face `transformers` library and we advise you to install latest version:
+```bash
+pip install transformers>=4.37.0
+```
+
+# How to use
+We use the following example to illustrate how to use our model to perform routing tasks. Please note that, our model works best with our provided prompt format. 
+### Quickstart
+````python
+import json
+from typing import Any, Dict, List
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "katanemo/Arch-Router-1.5B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Please use our provided prompt for best performance
+TASK_INSTRUCTION = """
+You are a helpful assistant designed to find the best suited route.
+You are provided with route description within <routes></routes> XML tags:
+<routes>
+
+{routes}
+
+</routes>
+
+<conversation>
+
+{conversation}
+
+</conversation>
+"""
+
+FORMAT_PROMPT = """
+Your task is to decide which route is best suit with user intent on the conversation in <conversation></conversation> XML tags.  Follow the instruction:
+1. If the latest intent from user is irrelevant or user intent is full filled, response with other route {"route": "other"}.
+2. You must analyze the route descriptions and find the best match route for user latest intent. 
+3. You only response the name of the route that best matches the user's request, use the exact name in the <routes></routes>.
+
+Based on your analysis, provide your response in the following JSON formats if you decide to match any route:
+{"route": "route_name"} 
+"""
+
+# Define route config
+route_config = [
+    {
+        "name": "code_generation",
+        "description": "Generating new code snippets, functions, or boilerplate based on user prompts or requirements",
+    },
+    {
+        "name": "bug_fixing",
+        "description": "Identifying and fixing errors or bugs in the provided code across different programming languages",
+    },
+    {
+        "name": "performance_optimization",
+        "description": "Suggesting improvements to make code more efficient, readable, or scalable",
+    },
+    {
+        "name": "api_help",
+        "description": "Assisting with understanding or integrating external APIs and libraries",
+    },
+    {
+        "name": "programming",
+        "description": "Answering general programming questions, theory, or best practices",
+    },
+]
+
+# Helper function to create the system prompt for our model
+def format_prompt(
+    route_config: List[Dict[str, Any]], conversation: List[Dict[str, Any]]
+):
+    return (
+        TASK_INSTRUCTION.format(
+            routes=json.dumps(route_config), conversation=json.dumps(conversation)
+        )
+        + FORMAT_PROMPT
+    )
+
+# Define conversations
+
+conversation = [
+    {
+        "role": "user",
+        "content": "fix this module 'torch.utils._pytree' has no attribute 'register_pytree_node'. did you mean: '_register_pytree_node'?",
+    }
+]
+
+route_prompt = format_prompt(route_config, conversation)
+
+messages = [
+    {"role": "user", "content": route_prompt},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+
+# 2. Generate
+generated_ids = model.generate(
+    input_ids=input_ids,  # or just positional: model.generate(input_ids, …)
+    max_new_tokens=32768,
+)
+
+# 3. Strip the prompt from each sequence
+prompt_lengths = input_ids.shape[1]  # same length for every row here
+generated_only = [
+    output_ids[prompt_lengths:]  # slice off the prompt tokens
+    for output_ids in generated_ids
+]
+
+# 4. Decode if you want text
+response = tokenizer.batch_decode(generated_only, skip_special_tokens=True)[0]
+print(response)
+````
+
+Then you should be able to see the following output string in JSON format:
+````python
+{"route": "bug_fixing"}
+````
+
+To better understand how to create the route descriptions, please take a look at our [Katanemo API](https://docs.archgw.com/guides/llm_router.html).
+
+# License
+Katanemo Arch-Router model is distributed under the [Katanemo license](https://huggingface.co/katanemo/Arch-Router-1.5B/blob/main/LICENSE).
+
+GitHub: https://github.com/katanemo/arch

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.51.3"
+}

tokenizer.json

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tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+      "lstrip": false,
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+      "single_word": false,
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+    }
+  },
+  "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,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\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    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) 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{%- endif %}\n",
+  "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
+}