cargo build --release --features metal ./target/release/mistralrs-server -i --throughput --paged-attn --pa-gpu-mem 4096 gguf --dtype bf16 -m /Users/Downloads/ -f Phi-3.5-mini-instruct-Q4_K_M.gguf OpenAI HTTP server You can an HTTP server ./mistralrs-server --port 1234 plain ...
Quantized filename, only applicable if `quantized` is set [default: mistral-7b-instruct-v0.1.Q4_K_M.gguf] --repeat-last-n <REPEAT_LAST_N> Control the application of repeat penalty for the last n tokens [default: 64] -h, --help Print help ``` ## For X-LoRA and quantized models...
下载GGUF模型 使用HuggingFace的镜像https://hf-mirror.com/ 方式一: pip install -U huggingface_hubexportHF_ENDPOINT=https://hf-mirror.com huggingface-cli download --resume-download MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF --include *Q4_K_M.gguf 方式二(推荐): sudo apt update sudo apt inst...
huggingface-cli download --resume-download MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF --include *Q4_K_M.gguf 方式二(推荐): sudo apt update sudo apt install aria2 git-lfs wget https://hf-mirror.com/hfd/hfd.shchmoda+x hfd.sh ./hfd.sh MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF --...
Description:The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested. solar-10.7b-instruct-v1.0: Quantizations:['Q2_K', 'Q3_K_L', 'Q3_K_M', 'Q3_K_S', 'Q4_0', 'Q...
ModelParametersSizeDownload Llama 3 8B 4.7GB ollama run llama3 Llama 3 70B 40GB ollama run llama3:70b Phi 3 Mini 3.8B 2.3GB ollama run phi3 Phi 3 Medium 14B 7.9GB ollama run phi3:medium Gemma 2B 1.4GB ollama run gemma:2b Gemma 7B 4.8GB ollama run gemma:7b Mistral 7B 4.1GB o...
Mixtral 让开源再次伟大 | 用Mixtral-8x22b-Instruct-v0.1弄了个小游戏体验: 1. 中文能力过关 2. 指令追随很好 3. CoT稳定 4. Q1量化最低显存要求30GB,性能不错Q4版本90GB#LLM(大型语言模型) #Mistral #OpenAI #chat GPT #开源 #深度学习(Deep Learning) ...
-f Phi-3.5-mini-instruct-Q4_K_M.gguf Tokenizer The following tokenizer model types are currently supported. If you would like one to be added, please raise an issue. Otherwise, please consider using the method demonstrated in examples below, where the tokenizer is sourced from Hugging Face....
./mistralrs_server --port 1234 gguf -t mistralai/Mistral-7B-Instruct-v0.1 -m TheBloke/Mistral-7B-Instruct-v0.1-GGUF -f mistral-7b-instruct-v0.1.Q4_K_M.ggufWith a model from GGML To start a server running Llama from GGML:
GGUF( tok_model_id="mistralai/Mistral-7B-Instruct-v0.1", quantized_model_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF", quantized_filename="mistral-7b-instruct-v0.1.Q4_K_M.gguf", tokenizer_json=None, repeat_last_n=64, ) ) res = runner.send_chat_completion_request( ChatCompletion...