metadata
library_name: transformers
language:
- ja
- en
base_model: jaeyong2/Qwen2.5-0.5B-Instruct-Ja-SFT
license: apache-2.0
tags:
- TensorBlock
- GGUF
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
jaeyong2/Qwen2.5-0.5B-Instruct-Ja-SFT - GGUF
This repo contains GGUF format model files for jaeyong2/Qwen2.5-0.5B-Instruct-Ja-SFT.
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 |
|---|---|---|---|
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q2_K.gguf | Q2_K | 0.339 GB | smallest, significant quality loss - not recommended for most purposes |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q3_K_S.gguf | Q3_K_S | 0.338 GB | very small, high quality loss |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q3_K_M.gguf | Q3_K_M | 0.355 GB | very small, high quality loss |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q3_K_L.gguf | Q3_K_L | 0.369 GB | small, substantial quality loss |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q4_0.gguf | Q4_0 | 0.352 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q4_K_S.gguf | Q4_K_S | 0.385 GB | small, greater quality loss |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q4_K_M.gguf | Q4_K_M | 0.398 GB | medium, balanced quality - recommended |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q5_0.gguf | Q5_0 | 0.397 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q5_K_S.gguf | Q5_K_S | 0.413 GB | large, low quality loss - recommended |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q5_K_M.gguf | Q5_K_M | 0.420 GB | large, very low quality loss - recommended |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q6_K.gguf | Q6_K | 0.506 GB | very large, extremely low quality loss |
| Qwen2.5-0.5B-Instruct-Ja-SFT-Q8_0.gguf | Q8_0 | 0.531 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/Qwen2.5-0.5B-Instruct-Ja-SFT-GGUF --include "Qwen2.5-0.5B-Instruct-Ja-SFT-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/Qwen2.5-0.5B-Instruct-Ja-SFT-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'