Requests will be processed in hours.įor GPTQ models like TheBloke/Llama-2-7b-Chat-GPTQ, you can directly download without requesting access.įor GGML models like TheBloke/Llama-2-7B-Chat-GGML, you can directly download without requesting access. Git clone download Llama 2 models, you need to request access from and also enable access on repos like meta-llama/Llama-2-7b-chat-hf. # Make sure you have git-lfs installed () There is also a list of other 2, 3, 4, 5, 6, 8-bit GGML models that can be used from TheBloke/Llama-2-7B-Chat-GGML. Running 4-bit model 4_0.bin needs CPU with 6GB RAM. Running 4-bit model Llama-2-7b-Chat-GPTQ needs GPU with 6GB VRAM. GPTQ 4-bit Llama-2 model require less GPU VRAM to run it. Llama-2-7b-Chat-GPTQ is the GPTQ model files for Meta's Llama 2 7b Chat. Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This colab example also show you how to benchmark gptq model on free Google Colab T4 GPU.Ĭheck/contribute the performance of your device in the full performance doc. You can set it to whatever value you want, but please only report results with at least 5 iterations. Python benchmark.py -iter NB_OF_ITERATIONS -backend_type gptqīy default, the number of iterations is 5, but if you want a faster result or a more accurate one
Supporting model backends: tranformers, bitsandbytes(8-bit inference), AutoGPTQ(4-bit inference), llama.cpp.Supporting models: Llama-2-7b/ 13b/ 70b, Llama-2-GPTQ, Llama-2-GGML, CodeLlama.Run OpenAI Compatible API on Llama2 models.Use llama2-wrapper as your local llama2 backend for Generative Agents/Apps colab example.Supporting all Llama 2 models (7B, 13B, 70B, GPTQ, GGML, CodeLlama) with 8-bit, 4-bit mode.Running Llama 2 with gradio web UI on GPU or CPU from anywhere (Linux/Windows/Mac).
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