fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1 -mg i, --main-gpu i the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) -m FNAME, --model FNAME model path (default:...
# 本地加载并卸载到 GPU llm = Llama( model_path=model_path, n_gpu_layers=-1 # 将所有层卸载到 GPU verbose=False, # 禁用详细日志输出 ) # 或者,自动下载并卸载到 GPU llm = Llama.from_pretrained( repo_id=repo_id, filename=filename, n_gpu_layers=-1 # 将所有层卸载到 GPU verbose=False...
python qwen2_vl_surgery.py"./model-dir" 在当前目录会生成qwen2vl-vision.gguf文件。 7. 使用上面生成的两个gguf文件: CUDA_VISIBLE_DEVICES=0./llama-qwen2vl-cli -m Qwen2-VL-7B-Instruct-7.6B-Q4_K_M.gguf --mmproj qwen2vl-vision.gguf -p"Describe the image"--image"PATH/TO/IMAGE" 后...
# CUDA: 多卡推理(以双卡为例),-ts等参数含义详见 https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md ./llama-cli -m /model_path/Qwen/Qwen-2.7B-Instruct/ggml-model-Q4_K_M.gguf -cnv -p "You are a helpful assistant" -ngl 9999 -ts 1,1 注: ngl可以灵活调...
python convert_hf_to_gguf.py ./model_path model_path目录下会生成Qwen2.5-7B-Instruct-7.6B-F16.gguf文件。 5. (量化,可选)如果电脑性能不够,可以执行量化选项: ./llama-quantize ./model_path/Qwen2.5-7B-Instruct-7.6B-F16.gguf Qwen2.5-7B-Instruct-7.6B-Q4_K_M.gguf Q4_K ...
(self, model_path, n_ctx, n_parts, n_gpu_layers,seed, f16_kv, logits_all, vocab_only, use_mmap, use_mlock, embedding, n_threads, n_batch, last_n_tokens_size, lora_base, lora_path, low_vram, tensor_split, rope_freq_base, rope_freq_scale, n_gqa, rms_norm_eps, mul_mat_q,...
目前的使用方法,build以后,运行llama.cpp并跳出web gui: ./server -m model_path/model_name -t 16 -ngl 1 在写代码方面,codellama比llama2_70b的效果好。目前llava(视觉多模态)能力处于“能用但是不好用”的阶段。清华的tlm70b还没有尝试,待尝试。
path-添加环境变量,我的cmake路径是 E:\soft\cmake-3.29.2-windows-x86_64\bin,设置完成后。关闭环境变量设置窗口。在cmd窗口测试一下cmake是否生效。 出现以上画面,说明cmake 安装及环境配置设置完成。 3.2 克隆和编译llama.cpp 下载llama.cpp项目
model_n_gpu_layers = os.environ.get('MODEL_N_GPU_LAYERS') underneath 'model_n_batch = int(os.environ.get('MODEL_N_BATCH',8))' line also modify privateGPT.py to include the gpu option: llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, n_batch=model_n_batch, callbacks=...