metadata
pipeline_tag: text-generation
inference: true
widget:
- text: 'def print_hello_world():'
example_title: Hello world
group: Python
license: bigscience-openrail-m
datasets:
- books
- arxiv
- c4
- falcon-refinedweb
- wiki
- github-issues
- stack_markdown
library_name: transformers
tags:
- code
- TensorBlock
- GGUF
language:
- en
base_model: smallcloudai/Refact-1_6-base
smallcloudai/Refact-1_6-base - GGUF
This repo contains GGUF format model files for smallcloudai/Refact-1_6-base.
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
Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Refact-1_6-base-Q2_K.gguf | Q2_K | 0.624 GB | smallest, significant quality loss - not recommended for most purposes |
| Refact-1_6-base-Q3_K_S.gguf | Q3_K_S | 0.723 GB | very small, high quality loss |
| Refact-1_6-base-Q3_K_M.gguf | Q3_K_M | 0.793 GB | very small, high quality loss |
| Refact-1_6-base-Q3_K_L.gguf | Q3_K_L | 0.854 GB | small, substantial quality loss |
| Refact-1_6-base-Q4_0.gguf | Q4_0 | 0.920 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Refact-1_6-base-Q4_K_S.gguf | Q4_K_S | 0.926 GB | small, greater quality loss |
| Refact-1_6-base-Q4_K_M.gguf | Q4_K_M | 0.968 GB | medium, balanced quality - recommended |
| Refact-1_6-base-Q5_0.gguf | Q5_0 | 1.106 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Refact-1_6-base-Q5_K_S.gguf | Q5_K_S | 1.106 GB | large, low quality loss - recommended |
| Refact-1_6-base-Q5_K_M.gguf | Q5_K_M | 1.131 GB | large, very low quality loss - recommended |
| Refact-1_6-base-Q6_K.gguf | Q6_K | 1.303 GB | very large, extremely low quality loss |
| Refact-1_6-base-Q8_0.gguf | Q8_0 | 1.687 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/smallcloudai_Refact-1_6-base-GGUF --include "Refact-1_6-base-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/smallcloudai_Refact-1_6-base-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'