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metadata
base_model: AITeamVN/Vi-Qwen2-1.5B-RAG
language:
  - vi
license: apache-2.0
tags:
  - retrieval-augmented-generation
  - text-generation-inference
  - TensorBlock
  - GGUF
library_name: transformers
pipeline_tag: text-generation
TensorBlock

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AITeamVN/Vi-Qwen2-1.5B-RAG - GGUF

This repo contains GGUF format model files for AITeamVN/Vi-Qwen2-1.5B-RAG.

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

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
Vi-Qwen2-1.5B-RAG-Q2_K.gguf Q2_K 0.630 GB smallest, significant quality loss - not recommended for most purposes
Vi-Qwen2-1.5B-RAG-Q3_K_S.gguf Q3_K_S 0.709 GB very small, high quality loss
Vi-Qwen2-1.5B-RAG-Q3_K_M.gguf Q3_K_M 0.768 GB very small, high quality loss
Vi-Qwen2-1.5B-RAG-Q3_K_L.gguf Q3_K_L 0.820 GB small, substantial quality loss
Vi-Qwen2-1.5B-RAG-Q4_0.gguf Q4_0 0.871 GB legacy; small, very high quality loss - prefer using Q3_K_M
Vi-Qwen2-1.5B-RAG-Q4_K_S.gguf Q4_K_S 0.876 GB small, greater quality loss
Vi-Qwen2-1.5B-RAG-Q4_K_M.gguf Q4_K_M 0.918 GB medium, balanced quality - recommended
Vi-Qwen2-1.5B-RAG-Q5_0.gguf Q5_0 1.023 GB legacy; medium, balanced quality - prefer using Q4_K_M
Vi-Qwen2-1.5B-RAG-Q5_K_S.gguf Q5_K_S 1.023 GB large, low quality loss - recommended
Vi-Qwen2-1.5B-RAG-Q5_K_M.gguf Q5_K_M 1.048 GB large, very low quality loss - recommended
Vi-Qwen2-1.5B-RAG-Q6_K.gguf Q6_K 1.185 GB very large, extremely low quality loss
Vi-Qwen2-1.5B-RAG-Q8_0.gguf Q8_0 1.533 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/Vi-Qwen2-1.5B-RAG-GGUF --include "Vi-Qwen2-1.5B-RAG-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/Vi-Qwen2-1.5B-RAG-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'