初始化项目，由ModelHub XC社区提供模型
Model: jarminraws/hotel-llm-search Source: Original Platform
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ModelHub XC
36
.gitattributes
vendored
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tar filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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23
README.md
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23
README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-1.7B
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tags: [hotel-search, entity-extraction, lora-merged, qwen3]
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---
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# Hotel Entity Extractor (Qwen3-1.7B, LoRA merged)
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Fine-tuned to extract structured hotel-search params from natural-language queries
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and return strict JSON. Trained filters-free of codes: `filters` is emitted as
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human-readable PHRASES (e.g. `"swimming pool, pet friendly"`); a downstream matcher
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resolves phrases -> production codes.
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- **Input**: a hotel-search query + `today` date (DDMMYYYY date math).
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- **Output**: JSON with destination, locality, hotelName, checkin/checkoutDate,
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adult/room/child/childAges/infantCount, sortCriteria, min/maxPrice, filters,
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deepSearch, isNearMe, resetAction.
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- **Prompt/schema contract**: bundled as `contract.py` in this repo. The serving
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layer MUST use this exact prompt (it verifies byte-parity at startup).
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Serve with vLLM (see the entity-extraction-serving repo). Raw model output is
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exposed by the API; date validation + filter phrase->code resolution happen in
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the calling service.
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89
chat_template.jinja
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89
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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63
config.json
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63
config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.8.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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127
contract.py
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127
contract.py
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"""SHARED CONTRACT — the prompt + output schema the model is trained and served on.
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This file is the single source of truth for:
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- FIELD_ORDER : canonical key order of the extracted JSON
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- build_prompt() : the EXACT prompt string used at train AND inference time
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- format_completion() : training target serialization (also used by eval)
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- extract_json() : brace-matched JSON extraction from raw model text
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- HotelExtraction : Pydantic schema for validation
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- validate() : parse+validate -> clean dict (or None)
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It is intentionally DEPENDENCY-LIGHT (only json + pydantic) so both the heavy
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training env and the lean serving env can import it. A COPY of this file lives in
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BOTH training/ and deployment/; tests/test_contract_parity.py asserts they are
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byte-identical, so the prompt can never silently drift between train and serve.
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DO NOT EDIT one copy without the other. Edit the source, re-sync, re-run the test.
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"""
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from __future__ import annotations
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import json
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from typing import Any, Literal, Optional
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from pydantic import BaseModel, ConfigDict, Field, ValidationError
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DEFAULT_BASE_MODEL = "Qwen/Qwen3-1.7B"
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# `filters` is emitted as a comma-separated string of NATURAL PHRASES
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# (e.g. "swimming pool, pet friendly"), NOT production codes. A downstream matcher
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# resolves phrases -> codes (FL_HF_29, ...), keeping the model taxonomy-agnostic.
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FIELD_ORDER = [
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"destination", "locality", "hotelName", "checkinDate", "checkoutDate",
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"adultCount", "roomCount", "childCount", "childAges", "infantCount",
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"sortCriteria", "minPrice", "maxPrice", "filters", "deepSearch", "isNearMe", "resetAction",
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]
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def build_prompt(query: str, today: str) -> str:
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"""The exact user-message content used for BOTH training and inference."""
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return (
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"Extract hotel-search entities.\n"
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"Return strict JSON only.\n"
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"Schema:\n"
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"destination, locality, hotelName, checkinDate, checkoutDate, "
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"adultCount, roomCount, childCount, childAges, infantCount, "
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"sortCriteria, minPrice, maxPrice, filters, deepSearch, isNearMe, resetAction.\n\n"
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"Rules:\n"
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"- Dates are DDMMYYYY.\n"
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'- deepSearch and isNearMe must be "true" or "false".\n'
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"- filters is a comma-separated list of amenity/type phrases (e.g. "
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'"swimming pool, pet friendly"); prefix removals with "no ".\n'
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"- Omit unknown optional fields.\n\n"
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f"today={today}\n"
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f"query={query}"
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)
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def format_completion(expected: dict[str, Any]) -> str:
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"""Stable-key-order minified JSON — the training target / gold serialization."""
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ordered = {f: expected[f] for f in FIELD_ORDER if f in expected}
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for k in expected:
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if k not in ordered:
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ordered[k] = expected[k]
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return json.dumps(ordered, ensure_ascii=False, separators=(",", ":"))
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def extract_json(text: str) -> dict | None:
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"""Pull the first balanced {...} object out of raw model text."""
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start = text.find("{")
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if start < 0:
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return None
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depth = 0
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in_str = esc = False
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for i in range(start, len(text)):
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c = text[i]
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if esc:
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esc = False
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continue
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if c == "\\":
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esc = True
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continue
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if c == '"':
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in_str = not in_str
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continue
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if in_str:
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continue
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if c == "{":
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depth += 1
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elif c == "}":
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depth -= 1
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if depth == 0:
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try:
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return json.loads(text[start:i + 1])
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except json.JSONDecodeError:
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return None
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return None
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class HotelExtraction(BaseModel):
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"""Output contract. `filters` is a comma-separated PHRASE string (e.g.
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"swimming pool, pet friendly"); a downstream matcher resolves it to codes."""
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model_config = ConfigDict(extra="forbid")
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deepSearch: Literal["true", "false"]
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isNearMe: Literal["true", "false"]
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destination: Optional[str] = Field(default=None, min_length=1)
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locality: Optional[str] = Field(default=None, min_length=1)
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hotelName: Optional[str] = Field(default=None, min_length=1)
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checkinDate: Optional[str] = Field(default=None, pattern=r"^\d{8}$")
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checkoutDate: Optional[str] = Field(default=None, pattern=r"^\d{8}$")
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adultCount: Optional[int] = Field(default=None, ge=0, le=20)
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roomCount: Optional[int] = Field(default=None, ge=0, le=20)
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childCount: Optional[int] = Field(default=None, ge=0, le=20)
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childAges: Optional[list[int]] = None
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infantCount: Optional[int] = Field(default=None, ge=0, le=20)
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sortCriteria: Optional[Literal["SC_P_LH", "SC_P_HL", "SC_UR", "SC_P", "SC_DIST"]] = None
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minPrice: Optional[int] = Field(default=None, ge=0, le=10_000_000)
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maxPrice: Optional[int] = Field(default=None, ge=0, le=10_000_000)
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filters: Optional[str] = Field(default=None, min_length=1)
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resetAction: Optional[Literal["filters", "guests", "dates", "all"]] = None
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def validate(d: dict) -> dict | None:
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"""Validate a parsed dict against the schema; return clean dict or None."""
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try:
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return HotelExtraction.model_validate(d).model_dump(exclude_none=True)
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except ValidationError:
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return None
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13
generation_config.json
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13
generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "5.8.1"
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}
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3
model.safetensors
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3
model.safetensors
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version https://git-lfs.github.com/spec/v1
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||||
oid sha256:081680104296e847046cb301f532cbce1f0116105fa07050a0cf48f7f350a98c
|
||||
size 3441185608
|
||||
3
tokenizer.json
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3
tokenizer.json
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@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||
size 11422650
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||||
30
tokenizer_config.json
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30
tokenizer_config.json
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{
|
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"add_prefix_space": false,
|
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"backend": "tokenizers",
|
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"bos_token": null,
|
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"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
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"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,
|
||||
"local_files_only": false,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user