HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF

🤗 Hugging Face 来源text-generationapache-2.0激活 1.7B1.1 GBGGUF✓ 1 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF ./model-folder
需要做种者 →

ngxson/SmolLM2-1.7B-Instruct-Q4_K_M-GGUF

This model was converted to GGUF format from HuggingFaceTB/SmolLM2-1.7B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo ngxson/SmolLM2-1.7B-Instruct-Q4_K_M-GGUF --hf-file smollm2-1.7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo ngxson/SmolLM2-1.7B-Instruct-Q4_K_M-GGUF --hf-file smollm2-1.7b-instruct-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo ngxson/SmolLM2-1.7B-Instruct-Q4_K_M-GGUF --hf-file smollm2-1.7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo ngxson/SmolLM2-1.7B-Instruct-Q4_K_M-GGUF --hf-file smollm2-1.7b-instruct-q4_k_m.gguf -c 2048