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SeaLLM-7B-v2-GGUF

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metadata
license: other
license_name: seallms
license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE
language:
  - en
  - zh
  - vi
  - id
  - th
  - ms
  - km
  - lo
  - my
  - tl
tags:
  - multilingual
  - sea
  - TensorBlock
  - GGUF
base_model: SeaLLMs/SeaLLM-7B-v2
TensorBlock

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SeaLLMs/SeaLLM-7B-v2 - GGUF

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

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

Prompt template 

<s><|im_start|>system
{system_prompt}</s><|im_start|>user
{prompt}</s><|im_start|>assistant

Model file specification

Filename Quant type File Size Description
SeaLLM-7B-v2-Q2_K.gguf Q2_K 2.605 GB smallest, significant quality loss - not recommended for most purposes
SeaLLM-7B-v2-Q3_K_S.gguf Q3_K_S 3.026 GB very small, high quality loss
SeaLLM-7B-v2-Q3_K_M.gguf Q3_K_M 3.356 GB very small, high quality loss
SeaLLM-7B-v2-Q3_K_L.gguf Q3_K_L 3.638 GB small, substantial quality loss
SeaLLM-7B-v2-Q4_0.gguf Q4_0 3.914 GB legacy; small, very high quality loss - prefer using Q3_K_M
SeaLLM-7B-v2-Q4_K_S.gguf Q4_K_S 3.943 GB small, greater quality loss
SeaLLM-7B-v2-Q4_K_M.gguf Q4_K_M 4.155 GB medium, balanced quality - recommended
SeaLLM-7B-v2-Q5_0.gguf Q5_0 4.749 GB legacy; medium, balanced quality - prefer using Q4_K_M
SeaLLM-7B-v2-Q5_K_S.gguf Q5_K_S 4.749 GB large, low quality loss - recommended
SeaLLM-7B-v2-Q5_K_M.gguf Q5_K_M 4.874 GB large, very low quality loss - recommended
SeaLLM-7B-v2-Q6_K.gguf Q6_K 5.637 GB very large, extremely low quality loss
SeaLLM-7B-v2-Q8_0.gguf Q8_0 7.301 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-GGUF --include "SeaLLM-7B-v2-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-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'