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amd/AMD-OLMo-1B-SFT-DPO - GGUF

This repo contains GGUF format model files for amd/AMD-OLMo-1B-SFT-DPO.

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

Prompt template

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{system_prompt}
<|user|>
{prompt}
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Model file specification

Filename Quant type File Size Description
AMD-OLMo-1B-SFT-DPO-Q2_K.gguf Q2_K 0.480 GB smallest, significant quality loss - not recommended for most purposes
AMD-OLMo-1B-SFT-DPO-Q3_K_S.gguf Q3_K_S 0.548 GB very small, high quality loss
AMD-OLMo-1B-SFT-DPO-Q3_K_M.gguf Q3_K_M 0.604 GB very small, high quality loss
AMD-OLMo-1B-SFT-DPO-Q3_K_L.gguf Q3_K_L 0.651 GB small, substantial quality loss
AMD-OLMo-1B-SFT-DPO-Q4_0.gguf Q4_0 0.690 GB legacy; small, very high quality loss - prefer using Q3_K_M
AMD-OLMo-1B-SFT-DPO-Q4_K_S.gguf Q4_K_S 0.697 GB small, greater quality loss
AMD-OLMo-1B-SFT-DPO-Q4_K_M.gguf Q4_K_M 0.734 GB medium, balanced quality - recommended
AMD-OLMo-1B-SFT-DPO-Q5_0.gguf Q5_0 0.824 GB legacy; medium, balanced quality - prefer using Q4_K_M
AMD-OLMo-1B-SFT-DPO-Q5_K_S.gguf Q5_K_S 0.824 GB large, low quality loss - recommended
AMD-OLMo-1B-SFT-DPO-Q5_K_M.gguf Q5_K_M 0.847 GB large, very low quality loss - recommended
AMD-OLMo-1B-SFT-DPO-Q6_K.gguf Q6_K 0.967 GB very large, extremely low quality loss
AMD-OLMo-1B-SFT-DPO-Q8_0.gguf Q8_0 1.252 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/AMD-OLMo-1B-SFT-DPO-GGUF --include "AMD-OLMo-1B-SFT-DPO-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/AMD-OLMo-1B-SFT-DPO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
214
GGUF
Model size
1.18B params
Architecture
olmo

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Dataset used to train tensorblock/AMD-OLMo-1B-SFT-DPO-GGUF