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Limour/llama-python-streamingllm

sourceHugging Facegpl-3.0updated 2y agoView on Hugging Face
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load_cache.py72 linesDownload Raw Back to mods
1import os2 3 4def init(cfg):5    print(cfg['setting_cache_path'].value)6    if os.path.exists(cfg['setting_cache_path'].value):7        # ========== 加载角色卡-缓存 ==========8        tmp = cfg['model'].load_session(cfg['setting_cache_path'].value)9        print(f"load cache from {cfg['setting_cache_path'].value} {tmp}")10        tmp = cfg['chat_template']('system',11                                   cfg['text_format'](cfg['role_char_d'].value,12                                                      char=cfg['role_char'].value,13                                                      user=cfg['role_usr'].value))14        cfg['setting_n_keep'].value = len(tmp)15        tmp = cfg['chat_template'](cfg['role_char'].value,16                                   cfg['text_format'](cfg['role_chat_style'].value,17                                                      char=cfg['role_char'].value,18                                                      user=cfg['role_usr'].value))19        cfg['setting_n_keep'].value += len(tmp)20        # ========== 加载角色卡-第一条消息 ==========21        cfg['chatbot'] = []22        for one in cfg["role_char_first"]:23            one['name'] = cfg['text_format'](one['name'],24                                             char=cfg['role_char'].value,25                                             user=cfg['role_usr'].value)26            one['value'] = cfg['text_format'](one['value'],27                                              char=cfg['role_char'].value,28                                              user=cfg['role_usr'].value)29            if one['name'] == cfg['role_char'].value:30                cfg['chatbot'].append((None, cfg['chat_display_format'](one['value'])))31            print(one)32    else:33        # ========== 加载角色卡-角色描述 ==========34        tmp = cfg['chat_template']('system',35                                   cfg['text_format'](cfg['role_char_d'].value,36                                                      char=cfg['role_char'].value,37                                                      user=cfg['role_usr'].value))38        cfg['setting_n_keep'].value = cfg['model'].eval_t(tmp)  # 此内容永久存在39 40        # ========== 加载角色卡-回复示例 ==========41        tmp = cfg['chat_template'](cfg['role_char'].value,42                                   cfg['text_format'](cfg['role_chat_style'].value,43                                                      char=cfg['role_char'].value,44                                                      user=cfg['role_usr'].value))45        cfg['setting_n_keep'].value = cfg['model'].eval_t(tmp)  # 此内容永久存在46 47        # ========== 加载角色卡-第一条消息 ==========48        cfg['chatbot'] = []49        for one in cfg["role_char_first"]:50            one['name'] = cfg['text_format'](one['name'],51                                             char=cfg['role_char'].value,52                                             user=cfg['role_usr'].value)53            one['value'] = cfg['text_format'](one['value'],54                                              char=cfg['role_char'].value,55                                              user=cfg['role_usr'].value)56            if one['name'] == cfg['role_char'].value:57                cfg['chatbot'].append((None, cfg['chat_display_format'](one['value'])))58            print(one)59            tmp = cfg['chat_template'](one['name'], one['value'])60            cfg['model'].eval_t(tmp)  # 此内容随上下文增加将被丢弃61 62        # ========== 保存角色卡-缓存 ==========63        with open(cfg['setting_cache_path'].value, 'wb') as f:64            pass65        tmp = cfg['model'].save_session(cfg['setting_cache_path'].value)66        print(f'save cache {tmp}')67        # ========== 上传缓存 ==========68        if os.environ.get("HF_TOKEN"):69            from huggingface_hub import login, CommitScheduler70            login(token=os.environ.get("HF_TOKEN"), write_permission=True)71            CommitScheduler(repo_id='Limour/llama-python-streamingllm-cache', repo_type='dataset', folder_path='cache')72