llama-cmd-gen/db.py

#!/usr/bin/env python3
"""Database management for llama.cpp command generator."""

import sqlite3
import json
import os

DB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data.db')


def get_db():
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    conn.execute("PRAGMA foreign_keys = ON")
    return conn


def init_db():
    conn = get_db()
    c = conn.cursor()

    # GPU table
    c.execute('''
        CREATE TABLE IF NOT EXISTS gpus (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL UNIQUE,
            vram_mb INTEGER NOT NULL,
            compute_capability TEXT,
            description TEXT,
            sort_order INTEGER DEFAULT 0
        )
    ''')

    # llama.cpp versions table
    c.execute('''
        CREATE TABLE IF NOT EXISTS llama_versions (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            version_tag TEXT NOT NULL UNIQUE,
            description TEXT,
            release_date TEXT,
            is_active INTEGER DEFAULT 1,
            sort_order INTEGER DEFAULT 0
        )
    ''')

    # Parameters table (per version)
    c.execute('''
        CREATE TABLE IF NOT EXISTS params (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            version_id INTEGER NOT NULL,
            param_key TEXT NOT NULL,
            short_flag TEXT,
            long_flag TEXT NOT NULL,
            description TEXT,
            category TEXT NOT NULL DEFAULT 'common',
            param_type TEXT NOT NULL DEFAULT 'string',
            default_value TEXT,
            options TEXT,
            min_value REAL,
            max_value REAL,
            step REAL,
            unit TEXT,
            is_important INTEGER DEFAULT 0,
            affects_vram INTEGER DEFAULT 0,
            sort_order INTEGER DEFAULT 0,
            FOREIGN KEY (version_id) REFERENCES llama_versions(id) ON DELETE CASCADE,
            UNIQUE(version_id, param_key)
        )
    ''')

    # Settings table (key-value for app config)
    c.execute('''
        CREATE TABLE IF NOT EXISTS settings (
            key TEXT PRIMARY KEY,
            value TEXT
        )
    ''')

    # Models table
    c.execute('''
        CREATE TABLE IF NOT EXISTS models (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            base_model TEXT NOT NULL,
            name TEXT NOT NULL,
            size_gb REAL NOT NULL,
            layers INTEGER NOT NULL,
            embd INTEGER NOT NULL,
            kv_heads INTEGER NOT NULL,
            head_dim INTEGER NOT NULL,
            attention_heads INTEGER NOT NULL,
            default_ctx INTEGER DEFAULT 0,
            quant TEXT DEFAULT '',
            description TEXT DEFAULT '',
            sort_order INTEGER DEFAULT 0
        )
    ''')

    conn.commit()

    # Insert default data
    insert_default_data(conn)

    conn.close()


def insert_default_data(conn):
    c = conn.cursor()

    # Check if data already exists
    c.execute("SELECT COUNT(*) as cnt FROM gpus")
    if c.fetchone()['cnt'] > 0:
        return

    # ===== Default GPUs =====
    default_gpus = [
        ("RTX 3090", 24576, "8.6", "NVIDIA GeForce RTX 3090, 24GB VRAM", 1),
        ("RTX 4090", 24576, "8.9", "NVIDIA GeForce RTX 4090, 24GB VRAM", 2),
        ("RTX 4080", 16384, "8.9", "NVIDIA GeForce RTX 4080, 16GB VRAM", 3),
        ("RTX 3080", 10240, "8.6", "NVIDIA GeForce RTX 3080, 10GB VRAM", 4),
        ("RTX 3090 Ti", 24576, "8.6", "NVIDIA GeForce RTX 3090 Ti, 24GB VRAM", 5),
        ("RTX 5090", 32768, "12.0", "NVIDIA GeForce RTX 5090, 32GB VRAM", 6),
        ("RTX 4070 Ti", 12288, "8.9", "NVIDIA GeForce RTX 4070 Ti, 12GB VRAM", 7),
        ("RTX 4060", 8192, "8.9", "NVIDIA GeForce RTX 4060, 8GB VRAM", 8),
        ("A100 80GB", 81920, "8.0", "NVIDIA A100 80GB", 9),
        ("A100 40GB", 40960, "8.0", "NVIDIA A100 40GB", 10),
        ("H100 80GB", 81920, "9.0", "NVIDIA H100 80GB", 11),
        ("V100 32GB", 32768, "7.0", "NVIDIA V100 32GB", 12),
        ("RTX A6000", 49152, "8.6", "NVIDIA RTX A6000, 48GB VRAM", 13),
        ("RTX A5000", 24576, "8.6", "NVIDIA RTX A5000, 24GB VRAM", 14),
        ("RX 7900 XTX", 24576, "N/A", "AMD Radeon RX 7900 XTX, 24GB VRAM", 15),
    ]

    for g in default_gpus:
        c.execute('''INSERT INTO gpus (name, vram_mb, compute_capability, description, sort_order)
                     VALUES (?, ?, ?, ?, ?)''', g)

    # ===== Default llama.cpp versions =====
    # Version 1: b6310 (mid 2025)
    c.execute('''INSERT INTO llama_versions (version_tag, description, release_date, is_active, sort_order)
                 VALUES (?, ?, ?, ?, ?)''',
              ('b6310', 'llama.cpp build 6310 (2025年中)', '2025-06-01', 1, 1))
    v1_id = c.lastrowid

    # Version 2: b10068 (latest 2026)
    c.execute('''INSERT INTO llama_versions (version_tag, description, release_date, is_active, sort_order)
                 VALUES (?, ?, ?, ?, ?)''',
              ('b10068', 'llama.cpp build 10068 (2026年最新)', '2026-07-01', 1, 2))
    v2_id = c.lastrowid

    # ===== Parameters for b6310 =====
    params_b6310 = [
        # === Common params ===
        # Important
        ("model", "-m", "--model", "模型文件路径", "common", "string", "", None, None, None, None, None, 1, 0, 1),
        ("ctx_size", "-c", "--ctx-size", "上下文窗口大小 (0=从模型加载)", "common", "number", "0", None, 0, 131072, 512, "tokens", 1, 1, 2),
        ("n_gpu_layers", "-ngl", "--n-gpu-layers", "存储在VRAM中的最大层数 (0=不卸载, -1=全部)", "common", "number", "0", None, -1, 999, 1, "layers", 1, 1, 3),
        ("threads", "-t", "--threads", "生成期间使用的CPU线程数", "common", "number", "-1", None, -1, 128, 1, "threads", 1, 0, 4),
        ("batch_size", "-b", "--batch-size", "逻辑最大批处理大小", "common", "number", "2048", None, 1, 8192, 128, "tokens", 1, 1, 5),
        ("ubatch_size", "-ub", "--ubatch-size", "物理最大批处理大小", "common", "number", "512", None, 1, 4096, 64, "tokens", 0, 1, 6),
        ("flash_attn", "-fa", "--flash-attn", "Flash Attention (on/off)", "common", "select", "off", json.dumps(["on", "off"]), None, None, None, None, 1, 1, 7),

        # Less important
        ("predict", "-n", "--predict", "要预测的token数量 (-1=无限)", "common", "number", "-1", None, -1, 999999, 1, "tokens", 0, 0, 10),
        ("keep", "", "--keep", "从初始提示中保留的token数", "common", "number", "0", None, -1, 999999, 1, "tokens", 0, 0, 11),
        ("cache_type_k", "-ctk", "--cache-type-k", "KV缓存K的数据类型", "common", "select", "f16", json.dumps(["f32", "f16", "bf16", "q8_0", "q4_0", "q4_1", "iq4_nl", "q5_0", "q5_1"]), None, None, None, None, 0, 1, 12),
        ("cache_type_v", "-ctv", "--cache-type-v", "KV缓存V的数据类型", "common", "select", "f16", json.dumps(["f32", "f16", "bf16", "q8_0", "q4_0", "q4_1", "iq4_nl", "q5_0", "q5_1"]), None, None, None, None, 0, 1, 13),
        ("split_mode", "-sm", "--split-mode", "跨多个GPU的分割模式", "common", "select", "layer", json.dumps(["none", "layer", "row"]), None, None, None, None, 0, 0, 14),
        ("tensor_split", "-ts", "--tensor-split", "每个GPU卸载的模型比例, 逗号分隔", "common", "string", "", None, None, None, None, None, 0, 0, 15),
        ("main_gpu", "-mg", "--main-gpu", "用于模型的主GPU索引", "common", "number", "0", None, 0, 7, 1, None, 0, 0, 16),
        ("mlock", "", "--mlock", "强制系统将模型保留在RAM中", "common", "boolean", "false", None, None, None, None, None, 0, 0, 17),
        ("mmap", "", "--mmap", "内存映射模型文件", "common", "boolean", "true", None, None, None, None, None, 0, 0, 18),
        ("numa", "", "--numa", "NUMA优化", "common", "select", "", json.dumps(["", "distribute", "isolate", "numactl"]), None, None, None, None, 0, 0, 19),
        ("cpu_moe", "-cmoe", "--cpu-moe", "将所有MoE权重保留在CPU", "common", "boolean", "false", None, None, None, None, None, 0, 0, 20),
        ("rope_scaling", "", "--rope-scaling", "RoPE频率缩放方法", "common", "select", "linear", json.dumps(["none", "linear", "yarn"]), None, None, None, None, 0, 0, 21),
        ("rope_freq_base", "", "--rope-freq-base", "RoPE基础频率", "common", "number", "10000", None, 1, 1000000, 1, None, 0, 0, 22),
        ("rope_freq_scale", "", "--rope-freq-scale", "RoPE频率缩放因子", "common", "number", "1.0", None, 0.01, 10, 0.01, None, 0, 0, 23),
        ("yarn_orig_ctx", "", "--yarn-orig-ctx", "YaRN原始上下文大小", "common", "number", "0", None, 0, 131072, 512, "tokens", 0, 0, 24),
        ("yarn_ext_factor", "", "--yarn-ext-factor", "YaRN外推混合因子", "common", "number", "-1.0", None, -1, 1, 0.1, None, 0, 0, 25),
        ("yarn_attn_factor", "", "--yarn-attn-factor", "YaRN注意力缩放因子", "common", "number", "-1.0", None, -1, 10, 0.1, None, 0, 0, 26),
        ("yarn_beta_slow", "", "--yarn-beta-slow", "YaRN高修正维度", "common", "number", "-1.0", None, -1, 10, 0.1, None, 0, 0, 27),
        ("yarn_beta_fast", "", "--yarn-beta-fast", "YaRN低修正维度", "common", "number", "-1.0", None, -1, 10, 0.1, None, 0, 0, 28),
        ("kv_offload", "-kvo", "--kv-offload", "启用KV缓存卸载", "common", "boolean", "true", None, None, None, None, None, 0, 0, 29),
        ("device", "-dev", "--device", "用于卸载的设备列表", "common", "string", "", None, None, None, None, None, 0, 0, 30),
        ("override_tensor", "-ot", "--override-tensor", "覆盖张量缓冲区类型", "common", "string", "", None, None, None, None, None, 0, 0, 31),

        # === Sampling params ===
        ("temperature", "", "--temp", "温度 (创造力)", "sampling", "number", "0.8", None, 0.01, 2.0, 0.05, None, 1, 0, 1),
        ("seed", "-s", "--seed", "随机种子 (-1=随机)", "sampling", "number", "-1", None, -1, 999999, 1, None, 0, 0, 2),
        ("top_k", "", "--top-k", "Top-K采样", "sampling", "number", "40", None, 0, 200, 1, None, 1, 0, 3),
        ("top_p", "", "--top-p", "Top-P (核) 采样", "sampling", "number", "0.95", None, 0.0, 1.0, 0.05, None, 1, 0, 4),
        ("min_p", "", "--min-p", "Min-P采样", "sampling", "number", "0.05", None, 0.0, 1.0, 0.01, None, 0, 0, 5),
        ("typical", "", "--typical", "局部典型采样", "sampling", "number", "1.0", None, 0.0, 1.0, 0.05, None, 0, 0, 6),
        ("repeat_penalty", "", "--repeat-penalty", "重复惩罚", "sampling", "number", "1.0", None, 0.5, 2.0, 0.05, None, 0, 0, 7),
        ("repeat_last_n", "", "--repeat-last-n", "惩罚考虑的最后n个token", "sampling", "number", "64", None, 0, 999999, 1, "tokens", 0, 0, 8),
        ("presence_penalty", "", "--presence-penalty", "存在惩罚", "sampling", "number", "0.0", None, -2.0, 2.0, 0.1, None, 0, 0, 9),
        ("frequency_penalty", "", "--frequency-penalty", "频率惩罚", "sampling", "number", "0.0", None, -2.0, 2.0, 0.1, None, 0, 0, 10),
        ("mirostat", "", "--mirostat", "Mirostat采样模式", "sampling", "select", "0", json.dumps(["0", "1", "2"]), None, None, None, None, 0, 0, 11),
        ("mirostat_lr", "", "--mirostat-lr", "Mirostat学习率", "sampling", "number", "0.1", None, 0.01, 1.0, 0.01, None, 0, 0, 12),
        ("mirostat_ent", "", "--mirostat-ent", "Mirostat目标熵", "sampling", "number", "5.0", None, 1.0, 10.0, 0.1, None, 0, 0, 13),
        ("ignore_eos", "", "--ignore-eos", "忽略结束流token", "sampling", "boolean", "false", None, None, None, None, None, 0, 0, 14),
        ("samplers", "", "--samplers", "采样器序列", "sampling", "string", "penalties;dry;top_n_sigma;top_k;typ_p;top_p;min_p;xtc;temperature", None, None, None, None, None, 0, 0, 15),

        # === Server params ===
        ("port", "", "--port", "服务器监听端口", "server", "number", "8080", None, 1, 65535, 1, None, 1, 0, 1),
        ("host", "", "--host", "服务器监听地址", "server", "string", "0.0.0.0", None, None, None, None, None, 1, 0, 2),
        ("parallel", "-np", "--parallel", "服务器槽位数", "server", "number", "-1", None, -1, 64, 1, "slots", 0, 1, 3),
        ("cont_batching", "-cb", "--cont-batching", "连续批处理", "server", "boolean", "true", None, None, None, None, None, 0, 0, 4),
        ("context_shift", "", "--context-shift", "上下文移位", "server", "boolean", "false", None, None, None, None, None, 0, 0, 5),
        ("special", "-sp", "--special", "特殊token输出", "server", "boolean", "false", None, None, None, None, None, 0, 0, 6),
        ("warmup", "", "--warmup", "预热运行", "server", "boolean", "true", None, None, None, None, None, 0, 0, 7),
        ("pooling", "", "--pooling", "嵌入池化类型", "server", "select", "", json.dumps(["", "none", "mean", "cls", "last", "rank"]), None, None, None, None, 0, 0, 8),
        ("mmproj", "-mm", "--mmproj", "多模态投影文件路径", "server", "string", "", None, None, None, None, None, 0, 0, 9),

        # === Model download ===
        ("hf_repo", "-hf", "--hf-repo", "Hugging Face模型仓库", "model_source", "string", "", None, None, None, None, None, 0, 0, 1),
        ("hf_file", "-hff", "--hf-file", "Hugging Face模型文件", "model_source", "string", "", None, None, None, None, None, 0, 0, 2),
        ("hf_token", "-hft", "--hf-token", "Hugging Face访问令牌", "model_source", "string", "", None, None, None, None, None, 0, 0, 3),
        ("model_url", "-mu", "--model-url", "模型下载URL", "model_source", "string", "", None, None, None, None, None, 0, 0, 4),
        ("docker_repo", "-dr", "--docker-repo", "Docker Hub模型仓库", "model_source", "string", "", None, None, None, None, None, 0, 0, 5),

        # === Logging ===
        ("verbose", "-v", "--verbose", "详细日志输出", "logging", "boolean", "false", None, None, None, None, None, 0, 0, 1),
        ("log_file", "", "--log-file", "日志文件路径", "logging", "string", "", None, None, None, None, None, 0, 0, 2),
        ("log_colors", "", "--log-colors", "彩色日志", "logging", "select", "auto", json.dumps(["on", "off", "auto"]), None, None, None, None, 0, 0, 3),
        ("log_verbosity", "-lv", "--verbosity", "日志详细级别", "logging", "select", "3", json.dumps(["0", "1", "2", "3", "4", "5"]), None, None, None, None, 0, 0, 4),

        # === LoRA ===
        ("lora", "", "--lora", "LoRA适配器路径", "lora", "string", "", None, None, None, None, None, 0, 0, 1),
        ("lora_scaled", "", "--lora-scaled", "带缩放的LoRA适配器", "lora", "string", "", None, None, None, None, None, 0, 0, 2),

        # === Advanced ===
        ("cpu_mask", "-C", "--cpu-mask", "CPU亲和性掩码", "advanced", "string", "", None, None, None, None, None, 0, 0, 1),
        ("cpu_range", "-Cr", "--cpu-range", "CPU亲和性范围", "advanced", "string", "", None, None, None, None, None, 0, 0, 2),
        ("cpu_strict", "", "--cpu-strict", "严格CPU放置", "advanced", "boolean", "false", None, None, None, None, None, 0, 0, 3),
        ("prio", "", "--prio", "进程/线程优先级", "advanced", "number", "0", None, -1, 3, 1, None, 0, 0, 4),
        ("poll", "", "--poll", "轮询级别", "advanced", "number", "50", None, 0, 100, 1, None, 0, 0, 5),
        ("threads_batch", "-tb", "--threads-batch", "批处理和提示处理线程数", "advanced", "number", "-1", None, -1, 128, 1, None, 0, 0, 6),
        ("rope_scale", "", "--rope-scale", "RoPE上下文缩放因子", "advanced", "number", "1.0", None, 0.1, 10, 0.1, None, 0, 0, 7),
        ("op_offload", "", "--op-offload", "卸载主机张量操作到设备", "advanced", "boolean", "true", None, None, None, None, None, 0, 0, 8),
        ("check_tensors", "", "--check-tensors", "检查模型张量数据", "advanced", "boolean", "false", None, None, None, None, None, 0, 0, 9),
        ("override_kv", "", "--override-kv", "覆盖模型元数据", "advanced", "string", "", None, None, None, None, None, 0, 0, 10),
    ]

    for p in params_b6310:
        c.execute('''INSERT INTO params
            (version_id, param_key, short_flag, long_flag, description, category, param_type,
             default_value, options, min_value, max_value, step, unit, is_important,
             affects_vram, sort_order)
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''',
            (v1_id,) + p)

    # ===== Parameters for b10068 (latest) =====
    params_b10068 = list(params_b6310)  # Copy all from b6310

    # Modify defaults that changed
    # n_gpu_layers default changed from "0" to "auto"
    params_b10068 = [(p[0], p[1], p[2], p[3], p[4], p[5],
                      "auto" if p[0] == "n_gpu_layers" else p[6],
                      p[7], p[8], p[9], p[10], p[11], p[12], p[13], p[14])
                     if p[0] == "n_gpu_layers" else p for p in params_b10068]

    # flash_attn: default changed from "off" to "auto", and options include "auto"
    params_b10068 = [(p[0], p[1], p[2], p[3], p[4], p[5],
                      "auto" if p[0] == "flash_attn" else p[6],
                      '["on", "off", "auto"]' if p[0] == "flash_attn" else p[7],
                      p[8], p[9], p[10], p[11], p[12], p[13], p[14])
                     if p[0] == "flash_attn" else p for p in params_b10068]

    # Add new parameters for b10068
    new_params_b10068 = [
        # New in b10068
        ("fit", "-fit", "--fit", "自动调整参数以适应设备内存", "common", "select", "on", json.dumps(["on", "off"]), None, None, None, None, 1, 1, 8),
        ("fit_target", "-fitt", "--fit-target", "每个设备的目标余量(MiB)", "common", "string", "1024", None, None, None, None, "MiB", 0, 1, 9),
        ("fit_ctx", "-fitc", "--fit-ctx", "fit选项可设置的最小ctx大小", "common", "number", "4096", None, 1024, 131072, 512, "tokens", 0, 1, 10),
        ("cache_ram", "-cram", "--cache-ram", "最大缓存大小(MiB)", "server", "number", "8192", None, -1, 999999, 512, "MiB", 0, 1, 10),
        ("kv_unified", "-kvu", "--kv-unified", "使用统一KV缓冲区", "server", "boolean", "false", None, None, None, None, None, 0, 1, 11),
        ("cache_idle_slots", "", "--cache-idle-slots", "缓存空闲槽位", "server", "boolean", "true", None, None, None, None, None, 0, 0, 12),
        ("ctx_checkpoints", "-ctxcp", "--ctx-checkpoints", "每槽最大上下文检查点数", "server", "number", "32", None, 1, 999, 1, None, 0, 0, 13),
        ("checkpoint_min_step", "-cms", "--checkpoint-min-step", "检查点最小间距", "server", "number", "8192", None, 0, 999999, 512, "tokens", 0, 0, 14),
        ("swa_full", "", "--swa-full", "使用全尺寸SWA缓存", "common", "boolean", "false", None, None, None, None, None, 0, 1, 32),
        ("perf", "", "--perf", "启用性能计时", "common", "boolean", "false", None, None, None, None, None, 0, 0, 33),
        ("repack", "", "--repack", "权重重打包", "common", "boolean", "true", None, None, None, None, None, 0, 0, 34),
        ("no_host", "", "--no-host", "绕过主机缓冲区", "common", "boolean", "false", None, None, None, None, None, 0, 0, 35),
        ("n_cpu_moe", "-ncmoe", "--n-cpu-moe", "前N层MoE权重保留在CPU", "common", "number", "0", None, 0, 999, 1, "layers", 0, 0, 36),
        ("direct_io", "-dio", "--direct-io", "使用DirectIO", "common", "boolean", "false", None, None, None, None, None, 0, 0, 37),
        ("offline", "", "--offline", "离线模式", "common", "boolean", "false", None, None, None, None, None, 0, 0, 38),
        ("spec_draft_cache_type_k", "-ctkd", "--cache-type-k-draft", "草稿模型KV缓存K类型", "advanced", "select", "f16", json.dumps(["f32", "f16", "bf16", "q8_0", "q4_0", "q4_1", "iq4_nl", "q5_0", "q5_1"]), None, None, None, None, 0, 0, 11),
        ("spec_draft_cache_type_v", "-ctvd", "--cache-type-v-draft", "草稿模型KV缓存V类型", "advanced", "select", "f16", json.dumps(["f32", "f16", "bf16", "q8_0", "q4_0", "q4_1", "iq4_nl", "q5_0", "q5_1"]), None, None, None, None, 0, 0, 12),
        ("adaptive_target", "", "--adaptive-target", "adaptive-p目标概率", "sampling", "number", "-1.0", None, -1.0, 1.0, 0.05, None, 0, 0, 16),
        ("adaptive_decay", "", "--adaptive-decay", "adaptive-p衰减率", "sampling", "number", "0.9", None, 0.0, 0.99, 0.01, None, 0, 0, 17),
        ("dynatemp_range", "", "--dynatemp-range", "动态温度范围", "sampling", "number", "0.0", None, 0.0, 2.0, 0.05, None, 0, 0, 18),
        ("dynatemp_exp", "", "--dynatemp-exp", "动态温度指数", "sampling", "number", "1.0", None, 0.1, 5.0, 0.1, None, 0, 0, 19),
        ("top_n_sigma", "--top-nsigma", "--top-n-sigma", "Top-n-sigma采样", "sampling", "number", "-1.0", None, -1.0, 10.0, 0.1, None, 0, 0, 20),
        ("xtc_probability", "", "--xtc-probability", "XTC概率", "sampling", "number", "0.0", None, 0.0, 1.0, 0.01, None, 0, 0, 21),
        ("xtc_threshold", "", "--xtc-threshold", "XTC阈值", "sampling", "number", "0.1", None, 0.0, 1.0, 0.01, None, 0, 0, 22),
        ("dry_multiplier", "", "--dry-multiplier", "DRY采样乘数", "sampling", "number", "0.0", None, 0.0, 5.0, 0.1, None, 0, 0, 23),
        ("dry_base", "", "--dry-base", "DRY采样基础值", "sampling", "number", "1.75", None, 1.0, 3.0, 0.05, None, 0, 0, 24),
        ("dry_allowed_length", "", "--dry-allowed-length", "DRY允许长度", "sampling", "number", "2", None, 1, 20, 1, None, 0, 0, 25),
        ("dry_penalty_last_n", "", "--dry-penalty-last-n", "DRY惩罚最后n个token", "sampling", "number", "-1", None, -1, 999999, 1, "tokens", 0, 0, 26),
        ("sampler_seq", "", "--sampler-seq", "简化采样器序列", "sampling", "string", "edskypmxt", None, None, None, None, None, 0, 0, 27),
        ("backend_sampling", "-bs", "--backend-sampling", "后端采样(实验性)", "sampling", "boolean", "false", None, None, None, None, None, 0, 0, 28),
        ("hf_repo_v", "-hfv", "--hf-repo-v", "vocoder模型HF仓库", "model_source", "string", "", None, None, None, None, None, 0, 0, 6),
        ("hf_file_v", "-hffv", "--hf-file-v", "vocoder模型HF文件", "model_source", "string", "", None, None, None, None, None, 0, 0, 7),
    ]

    all_params_b10068 = list(params_b10068) + list(new_params_b10068)

    for p in all_params_b10068:
        c.execute('''INSERT INTO params
            (version_id, param_key, short_flag, long_flag, description, category, param_type,
             default_value, options, min_value, max_value, step, unit, is_important,
             affects_vram, sort_order)
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''',
            (v2_id,) + p)

    # ===== Default models =====
    # (base_model, name, size_gb, layers, embd, kv_heads, head_dim, attention_heads, default_ctx, quant, description, sort_order)
    default_models = [
        # Llama-3-8B-Instruct (ctx 8192)
        ("Llama-3-8B-Instruct", "Llama-3-8B-Instruct (Q4_K_M)", 4.9, 32, 4096, 8, 128, 32, 8192, "Q4_K_M", "Meta Llama 3 8B Instruct, Q4_K_M 量化", 1),
        ("Llama-3-8B-Instruct", "Llama-3-8B-Instruct (Q8_0)", 8.5, 32, 4096, 8, 128, 32, 8192, "Q8_0", "Meta Llama 3 8B Instruct, Q8_0 量化", 2),
        ("Llama-3-8B-Instruct", "Llama-3-8B-Instruct (FP16)", 15.5, 32, 4096, 8, 128, 32, 8192, "FP16", "Meta Llama 3 8B Instruct, FP16", 3),
        # Llama-3-70B-Instruct (ctx 8192)
        ("Llama-3-70B-Instruct", "Llama-3-70B-Instruct (Q4_K_M)", 38.5, 80, 8192, 8, 128, 64, 8192, "Q4_K_M", "Meta Llama 3 70B Instruct, Q4_K_M 量化", 4),
        ("Llama-3-70B-Instruct", "Llama-3-70B-Instruct (Q8_0)", 74.0, 80, 8192, 8, 128, 64, 8192, "Q8_0", "Meta Llama 3 70B Instruct, Q8_0 量化", 5),
        ("Llama-3-70B-Instruct", "Llama-3-70B-Instruct (FP16)", 138.0, 80, 8192, 8, 128, 64, 8192, "FP16", "Meta Llama 3 70B Instruct, FP16", 6),
        # Llama-3.1-8B-Instruct (ctx 131072)
        ("Llama-3.1-8B-Instruct", "Llama-3.1-8B-Instruct (Q4_K_M)", 4.9, 32, 4096, 8, 128, 32, 131072, "Q4_K_M", "Meta Llama 3.1 8B Instruct, Q4_K_M 量化", 7),
        ("Llama-3.1-8B-Instruct", "Llama-3.1-8B-Instruct (Q8_0)", 8.5, 32, 4096, 8, 128, 32, 131072, "Q8_0", "Meta Llama 3.1 8B Instruct, Q8_0 量化", 8),
        # Llama-3.1-70B-Instruct (ctx 131072)
        ("Llama-3.1-70B-Instruct", "Llama-3.1-70B-Instruct (Q4_K_M)", 38.5, 80, 8192, 8, 128, 64, 131072, "Q4_K_M", "Meta Llama 3.1 70B Instruct, Q4_K_M 量化", 9),
        ("Llama-3.1-70B-Instruct", "Llama-3.1-70B-Instruct (Q8_0)", 74.0, 80, 8192, 8, 128, 64, 131072, "Q8_0", "Meta Llama 3.1 70B Instruct, Q8_0 量化", 10),
        # Llama-3.1-405B-Instruct (ctx 131072)
        ("Llama-3.1-405B-Instruct", "Llama-3.1-405B-Instruct (Q4_K_M)", 226.0, 126, 16384, 8, 128, 128, 131072, "Q4_K_M", "Meta Llama 3.1 405B Instruct, Q4_K_M 量化", 11),
        # Qwen2.5-7B-Instruct (ctx 32768)
        ("Qwen2.5-7B-Instruct", "Qwen2.5-7B-Instruct (Q4_K_M)", 4.7, 28, 3584, 4, 128, 28, 32768, "Q4_K_M", "Qwen2.5 7B Instruct, Q4_K_M 量化", 12),
        ("Qwen2.5-7B-Instruct", "Qwen2.5-7B-Instruct (Q8_0)", 7.6, 28, 3584, 4, 128, 28, 32768, "Q8_0", "Qwen2.5 7B Instruct, Q8_0 量化", 13),
        # Qwen2.5-14B-Instruct (ctx 32768)
        ("Qwen2.5-14B-Instruct", "Qwen2.5-14B-Instruct (Q4_K_M)", 8.7, 40, 5120, 8, 128, 40, 32768, "Q4_K_M", "Qwen2.5 14B Instruct, Q4_K_M 量化", 14),
        # Qwen2.5-32B-Instruct (ctx 32768)
        ("Qwen2.5-32B-Instruct", "Qwen2.5-32B-Instruct (Q4_K_M)", 19.5, 64, 5120, 8, 128, 64, 32768, "Q4_K_M", "Qwen2.5 32B Instruct, Q4_K_M 量化", 15),
        ("Qwen2.5-32B-Instruct", "Qwen2.5-32B-Instruct (Q8_0)", 32.0, 64, 5120, 8, 128, 64, 32768, "Q8_0", "Qwen2.5 32B Instruct, Q8_0 量化", 16),
        # Qwen2.5-72B-Instruct (ctx 32768)
        ("Qwen2.5-72B-Instruct", "Qwen2.5-72B-Instruct (Q4_K_M)", 42.0, 80, 8192, 8, 128, 64, 32768, "Q4_K_M", "Qwen2.5 72B Instruct, Q4_K_M 量化", 17),
        ("Qwen2.5-72B-Instruct", "Qwen2.5-72B-Instruct (Q8_0)", 75.0, 80, 8192, 8, 128, 64, 32768, "Q8_0", "Qwen2.5 72B Instruct, Q8_0 量化", 18),
        # DeepSeek-V2-Chat (ctx 4096)
        ("DeepSeek-V2-Chat", "DeepSeek-V2-Chat (Q4_K_M)", 23.0, 60, 5120, 8, 128, 60, 4096, "Q4_K_M", "DeepSeek V2 Chat, Q4_K_M 量化", 19),
        # DeepSeek-V2.5-Chat (ctx 4096)
        ("DeepSeek-V2.5-Chat", "DeepSeek-V2.5-Chat (Q4_K_M)", 23.0, 60, 5120, 8, 128, 60, 4096, "Q4_K_M", "DeepSeek V2.5 Chat, Q4_K_M 量化", 20),
        # DeepSeek-R1-Distill-Qwen-32B (ctx 131072)
        ("DeepSeek-R1-Distill-Qwen-32B", "DeepSeek-R1-Distill-Qwen-32B (Q4_K_M)", 19.5, 64, 5120, 8, 128, 64, 131072, "Q4_K_M", "DeepSeek R1 Distill Qwen 32B, Q4_K_M 量化", 21),
        ("DeepSeek-R1-Distill-Qwen-32B", "DeepSeek-R1-Distill-Qwen-32B (Q8_0)", 32.0, 64, 5120, 8, 128, 64, 131072, "Q8_0", "DeepSeek R1 Distill Qwen 32B, Q8_0 量化", 22),
        # DeepSeek-R1-Distill-Llama-70B (ctx 131072)
        ("DeepSeek-R1-Distill-Llama-70B", "DeepSeek-R1-Distill-Llama-70B (Q4_K_M)", 42.0, 80, 8192, 8, 128, 64, 131072, "Q4_K_M", "DeepSeek R1 Distill Llama 70B, Q4_K_M 量化", 23),
        ("DeepSeek-R1-Distill-Llama-70B", "DeepSeek-R1-Distill-Llama-70B (Q8_0)", 75.0, 80, 8192, 8, 128, 64, 131072, "Q8_0", "DeepSeek R1 Distill Llama 70B, Q8_0 量化", 24),
        # Mistral-7B-Instruct-v0.3 (ctx 32768)
        ("Mistral-7B-Instruct-v0.3", "Mistral-7B-Instruct-v0.3 (Q4_K_M)", 4.4, 32, 4096, 8, 128, 32, 32768, "Q4_K_M", "Mistral 7B Instruct v0.3, Q4_K_M 量化", 25),
        ("Mistral-7B-Instruct-v0.3", "Mistral-7B-Instruct-v0.3 (Q8_0)", 7.5, 32, 4096, 8, 128, 32, 32768, "Q8_0", "Mistral 7B Instruct v0.3, Q8_0 量化", 26),
        # Mixtral-8x7B-Instruct (ctx 32768)
        ("Mixtral-8x7B-Instruct", "Mixtral-8x7B-Instruct (Q4_K_M)", 26.0, 32, 4096, 8, 128, 32, 32768, "Q4_K_M", "Mixtral 8x7B Instruct, Q4_K_M 量化", 27),
        # Gemma-2-9B-It (ctx 8192)
        ("Gemma-2-9B-It", "Gemma-2-9B-It (Q4_K_M)", 5.4, 42, 3584, 4, 256, 14, 8192, "Q4_K_M", "Google Gemma 2 9B It, Q4_K_M 量化", 28),
        # Gemma-2-27B-It (ctx 8192)
        ("Gemma-2-27B-It", "Gemma-2-27B-It (Q4_K_M)", 16.5, 46, 4608, 4, 128, 36, 8192, "Q4_K_M", "Google Gemma 2 27B It, Q4_K_M 量化", 29),
        # Phi-3-Mini-4K-Instruct (ctx 4096)
        ("Phi-3-Mini-4K-Instruct", "Phi-3-Mini-4K-Instruct (Q4_K_M)", 2.5, 32, 3072, 32, 96, 32, 4096, "Q4_K_M", "Microsoft Phi-3 Mini 4K Instruct, Q4_K_M 量化", 30),
        # Phi-3-Medium-14B-Instruct (ctx 14336)
        ("Phi-3-Medium-14B-Instruct", "Phi-3-Medium-14B-Instruct (Q4_K_M)", 8.4, 40, 5120, 10, 128, 40, 14336, "Q4_K_M", "Microsoft Phi-3 Medium 14B Instruct, Q4_K_M 量化", 31),
        # GLM-4-9B-Chat (ctx 131072)
        ("GLM-4-9B-Chat", "GLM-4-9B-Chat (Q4_K_M)", 5.5, 40, 4096, 4, 128, 40, 131072, "Q4_K_M", "Zhipu GLM-4 9B Chat, Q4_K_M 量化", 32),
        ("GLM-4-9B-Chat", "GLM-4-9B-Chat (Q8_0)", 9.0, 40, 4096, 4, 128, 40, 131072, "Q8_0", "Zhipu GLM-4 9B Chat, Q8_0 量化", 33),
    ]

    for m in default_models:
        c.execute('''INSERT INTO models (base_model, name, size_gb, layers, embd, kv_heads, head_dim, attention_heads, default_ctx, quant, description, sort_order)
                     VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', m)

    # ===== Default settings =====
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("admin_password", "admin123"))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("default_gpu", "RTX 3090"))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("default_version", "b10068"))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("default_mode", "gpu"))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("default_quant", "Q4_K_M"))
    # LLM API settings for natural language parsing
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("llm_enabled", "false"))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("llm_api_url", ""))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("llm_api_key", ""))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("llm_api_model", ""))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("nl_default_text", "用自然语言描述你想要的配置，支持多行输入。\n例如：\n用RTX 4090跑Llama-3-70B，上下文8192\n温度0.7，开启flash attention\n端口设为8080"))
    c.execute("INSERT OR IGNORE INTO settings (key, value) VALUES (?, ?)", ("show_nl_section", "true"))

    conn.commit()


if __name__ == '__main__':
    init_db()
    print(f"Database initialized at {DB_PATH}")