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SeaLLMs/SeaLLM-7B-v2.5 - GGUF

This repo contains GGUF format model files for SeaLLMs/SeaLLM-7B-v2.5.

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

Prompt template

<bos><|im_start|>system
{system_prompt}<eos>
<|im_start|>user
{prompt}<eos>
<|im_start|>assistant

Model file specification

Filename Quant type File Size Description
SeaLLM-7B-v2.5-Q2_K.gguf Q2_K 3.242 GB smallest, significant quality loss - not recommended for most purposes
SeaLLM-7B-v2.5-Q3_K_S.gguf Q3_K_S 3.709 GB very small, high quality loss
SeaLLM-7B-v2.5-Q3_K_M.gguf Q3_K_M 4.069 GB very small, high quality loss
SeaLLM-7B-v2.5-Q3_K_L.gguf Q3_K_L 4.386 GB small, substantial quality loss
SeaLLM-7B-v2.5-Q4_0.gguf Q4_0 4.668 GB legacy; small, very high quality loss - prefer using Q3_K_M
SeaLLM-7B-v2.5-Q4_K_S.gguf Q4_K_S 4.700 GB small, greater quality loss
SeaLLM-7B-v2.5-Q4_K_M.gguf Q4_K_M 4.964 GB medium, balanced quality - recommended
SeaLLM-7B-v2.5-Q5_0.gguf Q5_0 5.570 GB legacy; medium, balanced quality - prefer using Q4_K_M
SeaLLM-7B-v2.5-Q5_K_S.gguf Q5_K_S 5.570 GB large, low quality loss - recommended
SeaLLM-7B-v2.5-Q5_K_M.gguf Q5_K_M 5.723 GB large, very low quality loss - recommended
SeaLLM-7B-v2.5-Q6_K.gguf Q6_K 6.529 GB very large, extremely low quality loss
SeaLLM-7B-v2.5-Q8_0.gguf Q8_0 8.454 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/SeaLLM-7B-v2.5-GGUF --include "SeaLLM-7B-v2.5-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/SeaLLM-7B-v2.5-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
252
GGUF
Model size
8.54B params
Architecture
gemma

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Inference API
Unable to determine this model's library. Check the docs .

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