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tensorblock/gte-Qwen2-7B-instruct-GGUF

🤗 Hugging Face sourcesentence-similarityapache-2.07B activated58 GBGGUF✓ 2 checksumsupdated today
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Run it next to your model folder. It makes the torrent, checks your files against Hugging Face, and submits it. You just start seeding and paste your key from your account. It only reads your files and never changes them. Read the script first if you like.

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo tensorblock/gte-Qwen2-7B-instruct-GGUF ./model-folder
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Alibaba-NLP/gte-Qwen2-7B-instruct - GGUF

This repo contains GGUF format model files for Alibaba-NLP/gte-Qwen2-7B-instruct.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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## Prompt template
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

Filename Quant type File Size Description
gte-Qwen2-7B-instruct-Q2_K.gguf Q2_K 2.807 GB smallest, significant quality loss - not recommended for most purposes
gte-Qwen2-7B-instruct-Q3_K_S.gguf Q3_K_S 3.251 GB very small, high quality loss
gte-Qwen2-7B-instruct-Q3_K_M.gguf Q3_K_M 3.545 GB very small, high quality loss
gte-Qwen2-7B-instruct-Q3_K_L.gguf Q3_K_L 3.806 GB small, substantial quality loss
gte-Qwen2-7B-instruct-Q4_0.gguf Q4_0 4.125 GB legacy; small, very high quality loss - prefer using Q3_K_M
gte-Qwen2-7B-instruct-Q4_K_S.gguf Q4_K_S 4.150 GB small, greater quality loss
gte-Qwen2-7B-instruct-Q4_K_M.gguf Q4_K_M 4.360 GB medium, balanced quality - recommended
gte-Qwen2-7B-instruct-Q5_0.gguf Q5_0 4.948 GB legacy; medium, balanced quality - prefer using Q4_K_M
gte-Qwen2-7B-instruct-Q5_K_S.gguf Q5_K_S 4.948 GB large, low quality loss - recommended
gte-Qwen2-7B-instruct-Q5_K_M.gguf Q5_K_M 5.069 GB large, very low quality loss - recommended
gte-Qwen2-7B-instruct-Q6_K.gguf Q6_K 5.822 GB very large, extremely low quality loss
gte-Qwen2-7B-instruct-Q8_0.gguf Q8_0 7.539 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/gte-Qwen2-7B-instruct-GGUF --include "gte-Qwen2-7B-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/gte-Qwen2-7B-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'