metadata
base_model: princeton-nlp/gemma-2-9b-it-SimPO
tags:
- alignment-handbook
- generated_from_trainer
- TensorBlock
- GGUF
datasets:
- princeton-nlp/gemma2-ultrafeedback-armorm
license: mit
model-index:
- name: princeton-nlp/gemma-2-9b-it-SimPO
results: []
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
princeton-nlp/gemma-2-9b-it-SimPO - GGUF
This repo contains GGUF format model files for princeton-nlp/gemma-2-9b-it-SimPO.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
<bos><start_of_turn>user
{system_prompt}
{prompt}<end_of_turn>
<start_of_turn>model
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| gemma-2-9b-it-SimPO-Q2_K.gguf | Q2_K | 3.544 GB | smallest, significant quality loss - not recommended for most purposes |
| gemma-2-9b-it-SimPO-Q3_K_S.gguf | Q3_K_S | 4.040 GB | very small, high quality loss |
| gemma-2-9b-it-SimPO-Q3_K_M.gguf | Q3_K_M | 4.435 GB | very small, high quality loss |
| gemma-2-9b-it-SimPO-Q3_K_L.gguf | Q3_K_L | 4.780 GB | small, substantial quality loss |
| gemma-2-9b-it-SimPO-Q4_0.gguf | Q4_0 | 5.069 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| gemma-2-9b-it-SimPO-Q4_K_S.gguf | Q4_K_S | 5.103 GB | small, greater quality loss |
| gemma-2-9b-it-SimPO-Q4_K_M.gguf | Q4_K_M | 5.365 GB | medium, balanced quality - recommended |
| gemma-2-9b-it-SimPO-Q5_0.gguf | Q5_0 | 6.038 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| gemma-2-9b-it-SimPO-Q5_K_S.gguf | Q5_K_S | 6.038 GB | large, low quality loss - recommended |
| gemma-2-9b-it-SimPO-Q5_K_M.gguf | Q5_K_M | 6.191 GB | large, very low quality loss - recommended |
| gemma-2-9b-it-SimPO-Q6_K.gguf | Q6_K | 7.068 GB | very large, extremely low quality loss |
| gemma-2-9b-it-SimPO-Q8_0.gguf | Q8_0 | 9.152 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/gemma-2-9b-it-SimPO-GGUF --include "gemma-2-9b-it-SimPO-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/gemma-2-9b-it-SimPO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'