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Model: jarminraws/hotel-llm-search
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+tags: [hotel-search, entity-extraction, lora-merged, qwen3]
+---
+
+# Hotel Entity Extractor (Qwen3-1.7B, LoRA merged)
+
+Fine-tuned to extract structured hotel-search params from natural-language queries
+and return strict JSON. Trained filters-free of codes: `filters` is emitted as
+human-readable PHRASES (e.g. `"swimming pool, pet friendly"`); a downstream matcher
+resolves phrases -> production codes.
+
+- **Input**: a hotel-search query + `today` date (DDMMYYYY date math).
+- **Output**: JSON with destination, locality, hotelName, checkin/checkoutDate,
+  adult/room/child/childAges/infantCount, sortCriteria, min/maxPrice, filters,
+  deepSearch, isNearMe, resetAction.
+- **Prompt/schema contract**: bundled as `contract.py` in this repo. The serving
+  layer MUST use this exact prompt (it verifies byte-parity at startup).
+
+Serve with vLLM (see the entity-extraction-serving repo). Raw model output is
+exposed by the API; date validation + filter phrase->code resolution happen in
+the calling service.

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 message.content is string 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.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.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' }}
+        {{- 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

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "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": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.8.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

contract.py

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+"""SHARED CONTRACT — the prompt + output schema the model is trained and served on.
+
+This file is the single source of truth for:
+  - FIELD_ORDER          : canonical key order of the extracted JSON
+  - build_prompt()       : the EXACT prompt string used at train AND inference time
+  - format_completion()  : training target serialization (also used by eval)
+  - extract_json()       : brace-matched JSON extraction from raw model text
+  - HotelExtraction      : Pydantic schema for validation
+  - validate()           : parse+validate -> clean dict (or None)
+
+It is intentionally DEPENDENCY-LIGHT (only json + pydantic) so both the heavy
+training env and the lean serving env can import it. A COPY of this file lives in
+BOTH training/ and deployment/; tests/test_contract_parity.py asserts they are
+byte-identical, so the prompt can never silently drift between train and serve.
+
+DO NOT EDIT one copy without the other. Edit the source, re-sync, re-run the test.
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any, Literal, Optional
+
+from pydantic import BaseModel, ConfigDict, Field, ValidationError
+
+DEFAULT_BASE_MODEL = "Qwen/Qwen3-1.7B"
+
+# `filters` is emitted as a comma-separated string of NATURAL PHRASES
+# (e.g. "swimming pool, pet friendly"), NOT production codes. A downstream matcher
+# resolves phrases -> codes (FL_HF_29, ...), keeping the model taxonomy-agnostic.
+FIELD_ORDER = [
+    "destination", "locality", "hotelName", "checkinDate", "checkoutDate",
+    "adultCount", "roomCount", "childCount", "childAges", "infantCount",
+    "sortCriteria", "minPrice", "maxPrice", "filters", "deepSearch", "isNearMe", "resetAction",
+]
+
+
+def build_prompt(query: str, today: str) -> str:
+    """The exact user-message content used for BOTH training and inference."""
+    return (
+        "Extract hotel-search entities.\n"
+        "Return strict JSON only.\n"
+        "Schema:\n"
+        "destination, locality, hotelName, checkinDate, checkoutDate, "
+        "adultCount, roomCount, childCount, childAges, infantCount, "
+        "sortCriteria, minPrice, maxPrice, filters, deepSearch, isNearMe, resetAction.\n\n"
+        "Rules:\n"
+        "- Dates are DDMMYYYY.\n"
+        '- deepSearch and isNearMe must be "true" or "false".\n'
+        "- filters is a comma-separated list of amenity/type phrases (e.g. "
+        '"swimming pool, pet friendly"); prefix removals with "no ".\n'
+        "- Omit unknown optional fields.\n\n"
+        f"today={today}\n"
+        f"query={query}"
+    )
+
+
+def format_completion(expected: dict[str, Any]) -> str:
+    """Stable-key-order minified JSON — the training target / gold serialization."""
+    ordered = {f: expected[f] for f in FIELD_ORDER if f in expected}
+    for k in expected:
+        if k not in ordered:
+            ordered[k] = expected[k]
+    return json.dumps(ordered, ensure_ascii=False, separators=(",", ":"))
+
+
+def extract_json(text: str) -> dict | None:
+    """Pull the first balanced {...} object out of raw model text."""
+    start = text.find("{")
+    if start < 0:
+        return None
+    depth = 0
+    in_str = esc = False
+    for i in range(start, len(text)):
+        c = text[i]
+        if esc:
+            esc = False
+            continue
+        if c == "\\":
+            esc = True
+            continue
+        if c == '"':
+            in_str = not in_str
+            continue
+        if in_str:
+            continue
+        if c == "{":
+            depth += 1
+        elif c == "}":
+            depth -= 1
+            if depth == 0:
+                try:
+                    return json.loads(text[start:i + 1])
+                except json.JSONDecodeError:
+                    return None
+    return None
+
+
+class HotelExtraction(BaseModel):
+    """Output contract. `filters` is a comma-separated PHRASE string (e.g.
+    "swimming pool, pet friendly"); a downstream matcher resolves it to codes."""
+    model_config = ConfigDict(extra="forbid")
+    deepSearch: Literal["true", "false"]
+    isNearMe: Literal["true", "false"]
+    destination: Optional[str] = Field(default=None, min_length=1)
+    locality: Optional[str] = Field(default=None, min_length=1)
+    hotelName: Optional[str] = Field(default=None, min_length=1)
+    checkinDate: Optional[str] = Field(default=None, pattern=r"^\d{8}$")
+    checkoutDate: Optional[str] = Field(default=None, pattern=r"^\d{8}$")
+    adultCount: Optional[int] = Field(default=None, ge=0, le=20)
+    roomCount: Optional[int] = Field(default=None, ge=0, le=20)
+    childCount: Optional[int] = Field(default=None, ge=0, le=20)
+    childAges: Optional[list[int]] = None
+    infantCount: Optional[int] = Field(default=None, ge=0, le=20)
+    sortCriteria: Optional[Literal["SC_P_LH", "SC_P_HL", "SC_UR", "SC_P", "SC_DIST"]] = None
+    minPrice: Optional[int] = Field(default=None, ge=0, le=10_000_000)
+    maxPrice: Optional[int] = Field(default=None, ge=0, le=10_000_000)
+    filters: Optional[str] = Field(default=None, min_length=1)
+    resetAction: Optional[Literal["filters", "guests", "dates", "all"]] = None
+
+
+def validate(d: dict) -> dict | None:
+    """Validate a parsed dict against the schema; return clean dict or None."""
+    try:
+        return HotelExtraction.model_validate(d).model_dump(exclude_none=True)
+    except ValidationError:
+        return None

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.8.1"
+}

model.safetensors

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+size 3441185608

tokenizer.json

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+size 11422650

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "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
+}