ggml_model_path = "https://huggingface.co/CRD716/ggml-vicuna-1.1-quantized/resolve/main/ggml-vicuna-7b-1.1-q4_1.bin" filename = "ggml-vicuna-7b-1.1-q4_1.bin" download_file(ggml_model_path, filename) 下一步是加载模型: from llama_cpp import Llama llm = Llama(model_path="ggml-v...
>>> llm = Llama( model_path="./models/7B/llama-model.gguf", # n_gpu_layers=-1, # Uncomment to use GPU acceleration # seed=1337, # Uncomment to set a specific seed # n_ctx=2048, # Uncomment to increase the context window ) >>> output = llm( "Q: Name the planets in the s...
4. 在llama.cpp工程下找到convert_hf_to_gguf.py,执行 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...
from llama_cpp import Llama model = Llama( model_path='your_gguf_file.gguf', n_gpu_layers=32, # Uncomment to use GPU acceleration n_ctx=2048, # Uncomment to increase the context window ) output = model('your_input', max_tokens=32, stop=["Q:", "\n"]) output = output['choices...
(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,...
./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 # 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 ...
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。
--model-path MODEL_PATH: 模型权重路径(必选)。 可选键 --tokenizer-path TOKENIZER_PATH: 分词器路径。 --host HOST: 主机名(默认可以是 localhost)。 --port PORT: 服务器端口(默认可以是 8000 或其他常用端口)。 --tokenizer-mode {auto,slow}: 默认通常是 auto。 --load-format {auto,pt,safetenso...
通过gpt_params初始化llama_model_params structllama_model_params llama_model_params_from_gpt_params (constgpt_params ¶ms); 创建大模型指针 LLAMA_APIstructllama_model *llama_load_model_from_file(constchar*path_model,structllama_model_paramsparams); ...
model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin", temperature=0.75, max_tokens=2000, top_p=1, callback_manager=callback_manager, verbose=True,# 这里需要将 verbose 参数传递给回调管理器)