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agentica-org/DeepCoder-14B-Preview - GGUF

This repo contains GGUF format model files for agentica-org/DeepCoder-14B-Preview.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.

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Prompt template

<|begin▁of▁sentence|>{system_prompt}<|User|>{prompt}<|Assistant|><think>

Model file specification

Filename Quant type File Size Description
DeepCoder-14B-Preview-Q2_K.gguf Q2_K 5.770 GB smallest, significant quality loss - not recommended for most purposes
DeepCoder-14B-Preview-Q3_K_S.gguf Q3_K_S 6.660 GB very small, high quality loss
DeepCoder-14B-Preview-Q3_K_M.gguf Q3_K_M 7.339 GB very small, high quality loss
DeepCoder-14B-Preview-Q3_K_L.gguf Q3_K_L 7.925 GB small, substantial quality loss
DeepCoder-14B-Preview-Q4_0.gguf Q4_0 8.518 GB legacy; small, very high quality loss - prefer using Q3_K_M
DeepCoder-14B-Preview-Q4_K_S.gguf Q4_K_S 8.573 GB small, greater quality loss
DeepCoder-14B-Preview-Q4_K_M.gguf Q4_K_M 8.988 GB medium, balanced quality - recommended
DeepCoder-14B-Preview-Q5_0.gguf Q5_0 10.267 GB legacy; medium, balanced quality - prefer using Q4_K_M
DeepCoder-14B-Preview-Q5_K_S.gguf Q5_K_S 10.267 GB large, low quality loss - recommended
DeepCoder-14B-Preview-Q5_K_M.gguf Q5_K_M 10.509 GB large, very low quality loss - recommended
DeepCoder-14B-Preview-Q6_K.gguf Q6_K 12.125 GB very large, extremely low quality loss
DeepCoder-14B-Preview-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/agentica-org_DeepCoder-14B-Preview-GGUF --include "DeepCoder-14B-Preview-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/agentica-org_DeepCoder-14B-Preview-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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GGUF
Model size
14.8B params
Architecture
qwen2
Hardware compatibility
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