Reminder I have read the README and searched the existing issues. System Info llama factory版本:0.8.1 transformers:4.41.2 flash-attn:2.5.7 Reproduction src/train.py \ --stage sft \ --model_name_or_path ZhipuAI/glm-4-9b-chat \ --do_train \ ...
# MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/glm-9b-chat') os.environ.setdefault('USE_FLASH_ATTENTION', '0') def file_exist_check(record_dir, file_name): non_exist = False try: open('/'.join([record_dir, file_name]), 'r').readlines() except FileNotFoundError: non_ex...
FlashAttention团队最近推出了一项名为Flash-Decoding的新方法,旨在加速大型Transformer架构的推理过程,特别是在处理长上下文LLM模型时。这项方法已经通过了64k长度的CodeLlama-34B的验证得到了PyTorch官方的认可。这个新方法的推出为深度学习领域带来了更多的创新和性能提升。 LLM 1年前 三星等减产威力巨大!NAND Flash涨幅...
print("value_layer: ", value_layer.dtype) 出现core attention模块中query_layer和value_layer的datatype不一致的情况 执行HF_ENDPOINT=https://hf-mirror.comllamafactory-cli train sft.yaml sft.yaml中的内容为 ` model_name_or_path: ./glm-4-9b stage: sft do_train: true finetuning_type: lora lo...
"attention_mask": attention_mask, "labels": labels } 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. GLM4-9B-chat采用的Prompt Template格式如下: [gMASK]<sop><|system|> 假设你是皇帝身边的女人--甄嬛。<|user|> ...
【大模型部署】vllm部署glm4及paged attention介绍 胖虎遛二狗· 6-22 28560 38:47 GLM使用指南:入门GLMAPI(一) ChatGLM· 5-23 64732 19:59 【ollama】(3):在linux搭建环境中,安装ollama工具,并且完成启动下载gemma:7b和qwen:1.8b运行速度飞快,支持http接口和命令行 ...
"num_attention_heads": 16, "num_hidden_layers": 24, "onnx_safe": null, "rotary_emb_base": 10000, "rotary_pct": 1.0, "scale_attn_weights": true, "seq_length": 8192, "softmax_in_fp32": false, "tie_word_embeddings": false, "tokenizer_class": "QWenTokenizer", "transformers_vers...
llama:use F32 precision in GLM4 attention and no FA#9130 Merged ngxsonmentioned this pull requestAug 27, 2024 Feature Request: Add support for chatglm3 in example server.#9164 Open Sign up for freeto join this conversation on GitHub. Already have an account?Sign in to comment ...
import ollama import pandas as pd model = 'glm4' #glm4 def LLM_Process(model, sys_prom, usr_prom): messages = [ {'role': 'user', 'content': usr_prom}, {'role': 'system', 'content': sys_prom} ] resp = ollama.chat(model, messages) ...
Initial Flash-Attention support: ggerganov#5021 BPE pre-tokenization support has been added: ggerganov#6920 MoE memory layout has been updated - reconvert models for mmap support and regenerate imatrix ggerganov#6387 Model sharding instructions using gguf-split ggerganov#6404 Fix major bug in ...