# Using Intel® Extension for Transformers to Inference the GLM-4-9B-Chat Model This example introduces how to use Intel® Extension for Transformers to inference the GLM-4-9B-Chat model. ## Device and Dependency Check ### Relevant Inference Test Data **The data in this document is tested on the following hardware environment. The actual running environment requirements and memory usage may vary slightly. Please refer to the actual running environment.** Test hardware information: + OS: Ubuntu 22.04 (This tutorial must be executed in a Linux environment) + Memory: 512GB + Python: 3.10.12 + CPU: Intel(R) Xeon(R) Platinum 8358 CPU / 12th Gen Intel i5-12400 ## Installing Dependencies Before starting the inference, please install the dependencies in `basic_demo`, and you need to install the dependencies in this directory: ```shell pip install -r requirements.txt ``` ## Running Model Inference ```shell python itrex_cli_demo.py ``` If this is your first inference, there will be a process of converting model weights. The converted model weights are stored in the `runtime_outputs` folder, which will consume about `60G` of disk space. After the conversion is completed, there are two files in the folder: + ne_chatglm2_f32.bin 52G (If you do not use FP32 for inference, you can delete this file) + ne_chatglm2_q_nf4_bestla_cfp32_sym_sfp32_g32.bin 8.1G If this is not your first inference, this step will be skipped, and you will directly start the conversation. The inference result is as follows: ```shell Welcome to the CLI chat. Type your messages below. User: Hello AVX:1 AVX2:1 AVX512F:1 AVX512BW:1 AVX_VNNI:0 AVX512_VNNI:1 AMX_INT8:0 AMX_BF16:0 AVX512_BF16:0 AVX512_FP16:0 beam_size: 1, do_sample: 1, top_k: 40, top_p: 0.900, continuous_batching: 0, max_request_num: 1, early_stopping: 0, scratch_size_ratio: 1.000 model_file_loader: loading model from runtime_outs/ne_chatglm2_q_nf4_bestla_cfp32_sym_sfp32_g32.bin Loading the bin file with NE format... load_ne_hparams 0.hparams.n_vocab = 151552 load_ne_hparams 1.hparams.n_embd = 4096 load_ne_hparams 2.hparams.n_mult = 0 load_ne_hparams 3.hparams.n_head = 32 load_ne_hparams 4.hparams.n_head_kv = 0 load_ne_hparams 5.hparams.n_layer = 40 load_ne_hparams 6.hparams.n_rot = 0 load_ne_hparams 7.hparams.ftype = 0 load_ne_hparams 8.hparams.max_seq_len = 131072 load_ne_hparams 9.hparams.alibi_bias_max = 0.000 load_ne_hparams 10.hparams.clip_qkv = 0.000 load_ne_hparams 11.hparams.multi_query_group_num = 2 load_ne_hparams 12.hparams.ffn_hidden_size = 13696 load_ne_hparams 13.hparams.inner_hidden_size = 0 load_ne_hparams 14.hparams.n_experts = 0 load_ne_hparams 15.hparams.n_experts_used = 0 load_ne_hparams 16.hparams.n_embd_head_k = 0 load_ne_hparams 17.hparams.norm_eps = 0.000000 load_ne_hparams 18.hparams.freq_base = 5000000.000 load_ne_hparams 19.hparams.freq_scale = 1.000 load_ne_hparams 20.hparams.rope_scaling_factor = 0.000 load_ne_hparams 21.hparams.original_max_position_embeddings = 0 load_ne_hparams 22.hparams.use_yarn = 0 load_ne_vocab 23.vocab.bos_token_id = 1 load_ne_vocab 24.vocab.eos_token_id = 151329 load_ne_vocab 25.vocab.pad_token_id = 151329 load_ne_vocab 26.vocab.sep_token_id = -1 init: hparams.n_vocab = 151552 init: hparams.n_embd = 4096 init: hparams.n_mult = 0 init: hparams.n_head = 32 init: hparams.n_layer = 40 init: hparams.n_rot = 0 init: hparams.ffn_hidden_size = 13696 init: n_parts = 1 load: ctx size = 16528.38 MB load: layers[0].ffn_fusion = 1 load: scratch0 = 4096.00 MB load: scratch1 = 2048.00 MB load: scratch2 = 4096.00 MB load: mem required = 26768.38 MB (+ memory per state) ............................................................................................. model_init_from_file: support_bestla_kv = 1 kv_cache_init: run_mha_reordered = 1 model_init_from_file: kv self size = 690.00 MB Assistant: Hello👋! I am an AI assistant. How can I help you today? ```