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Qwen/Qwen2.5-14B-Instruct-1M - GGUF
This repo contains GGUF format model files for Qwen/Qwen2.5-14B-Instruct-1M.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit ec7f3ac.
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 |
|---|---|---|---|
| Qwen2.5-14B-Instruct-1M-Q2_K.gguf | Q2_K | 5.770 GB | smallest, significant quality loss - not recommended for most purposes |
| Qwen2.5-14B-Instruct-1M-Q3_K_S.gguf | Q3_K_S | 6.660 GB | very small, high quality loss |
| Qwen2.5-14B-Instruct-1M-Q3_K_M.gguf | Q3_K_M | 7.339 GB | very small, high quality loss |
| Qwen2.5-14B-Instruct-1M-Q3_K_L.gguf | Q3_K_L | 7.925 GB | small, substantial quality loss |
| Qwen2.5-14B-Instruct-1M-Q4_0.gguf | Q4_0 | 8.518 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Qwen2.5-14B-Instruct-1M-Q4_K_S.gguf | Q4_K_S | 8.573 GB | small, greater quality loss |
| Qwen2.5-14B-Instruct-1M-Q4_K_M.gguf | Q4_K_M | 8.988 GB | medium, balanced quality - recommended |
| Qwen2.5-14B-Instruct-1M-Q5_0.gguf | Q5_0 | 10.267 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Qwen2.5-14B-Instruct-1M-Q5_K_S.gguf | Q5_K_S | 10.267 GB | large, low quality loss - recommended |
| Qwen2.5-14B-Instruct-1M-Q5_K_M.gguf | Q5_K_M | 10.509 GB | large, very low quality loss - recommended |
| Qwen2.5-14B-Instruct-1M-Q6_K.gguf | Q6_K | 12.125 GB | very large, extremely low quality loss |
| Qwen2.5-14B-Instruct-1M-Q8_0.gguf | Q8_0 | 15.702 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/Qwen2.5-14B-Instruct-1M-GGUF --include "Qwen2.5-14B-Instruct-1M-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/Qwen2.5-14B-Instruct-1M-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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