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tensorblock/
WisdomShell_RewardAnything-8B-v1-GGUF

Name: WisdomShell_RewardAnything-8B-v1-GGUF
Availability: InStock

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WisdomShell/RewardAnything-8B-v1

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WisdomShell/RewardAnything-8B-v1 - GGUF

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This repo contains GGUF format model files for WisdomShell/RewardAnything-8B-v1.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

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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
RewardAnything-8B-v1-Q2_K.gguf	Q2_K	3.282 GB	smallest, significant quality loss - not recommended for most purposes
RewardAnything-8B-v1-Q3_K_S.gguf	Q3_K_S	3.770 GB	very small, high quality loss
RewardAnything-8B-v1-Q3_K_M.gguf	Q3_K_M	4.124 GB	very small, high quality loss
RewardAnything-8B-v1-Q3_K_L.gguf	Q3_K_L	4.431 GB	small, substantial quality loss
RewardAnything-8B-v1-Q4_0.gguf	Q4_0	4.775 GB	legacy; small, very high quality loss - prefer using Q3_K_M
RewardAnything-8B-v1-Q4_K_S.gguf	Q4_K_S	4.802 GB	small, greater quality loss
RewardAnything-8B-v1-Q4_K_M.gguf	Q4_K_M	5.028 GB	medium, balanced quality - recommended
RewardAnything-8B-v1-Q5_0.gguf	Q5_0	5.721 GB	legacy; medium, balanced quality - prefer using Q4_K_M
RewardAnything-8B-v1-Q5_K_S.gguf	Q5_K_S	5.721 GB	large, low quality loss - recommended
RewardAnything-8B-v1-Q5_K_M.gguf	Q5_K_M	5.851 GB	large, very low quality loss - recommended
RewardAnything-8B-v1-Q6_K.gguf	Q6_K	6.726 GB	very large, extremely low quality loss
RewardAnything-8B-v1-Q8_0.gguf	Q8_0	8.710 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/WisdomShell_RewardAnything-8B-v1-GGUF --include "RewardAnything-8B-v1-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/WisdomShell_RewardAnything-8B-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'