Text Generation
Transformers
GGUF
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TensorBlock
GGUF
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metadata
license: apache-2.0
datasets:
  - Intel/orca_dpo_pairs
  - nvidia/HelpSteer
  - jondurbin/truthy-dpo-v0.1
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - TensorBlock
  - GGUF
base_model: abhishekchohan/SOLAR-10.7B-Instruct-Forest-DPO-v1
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abhishekchohan/SOLAR-10.7B-Instruct-Forest-DPO-v1 - GGUF

This repo contains GGUF format model files for abhishekchohan/SOLAR-10.7B-Instruct-Forest-DPO-v1.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Prompt template

### System:
{system_prompt}

### User:
{prompt}

### Assistant:

Model file specification

Filename Quant type File Size Description
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q2_K.gguf Q2_K 4.003 GB smallest, significant quality loss - not recommended for most purposes
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q3_K_S.gguf Q3_K_S 4.665 GB very small, high quality loss
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q3_K_M.gguf Q3_K_M 5.196 GB very small, high quality loss
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q3_K_L.gguf Q3_K_L 5.651 GB small, substantial quality loss
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q4_0.gguf Q4_0 6.072 GB legacy; small, very high quality loss - prefer using Q3_K_M
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q4_K_S.gguf Q4_K_S 6.119 GB small, greater quality loss
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q4_K_M.gguf Q4_K_M 6.462 GB medium, balanced quality - recommended
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q5_0.gguf Q5_0 7.397 GB legacy; medium, balanced quality - prefer using Q4_K_M
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q5_K_S.gguf Q5_K_S 7.397 GB large, low quality loss - recommended
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q5_K_M.gguf Q5_K_M 7.598 GB large, very low quality loss - recommended
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q6_K.gguf Q6_K 8.805 GB very large, extremely low quality loss
SOLAR-10.7B-Instruct-Forest-DPO-v1-Q8_0.gguf Q8_0 11.404 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/SOLAR-10.7B-Instruct-Forest-DPO-v1-GGUF --include "SOLAR-10.7B-Instruct-Forest-DPO-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/SOLAR-10.7B-Instruct-Forest-DPO-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'