metadata
license: cc-by-nc-nd-4.0
datasets:
- vandijklab/immune-c2s
language:
- en
tags:
- pytorch
- causal-lm
- scRNA-seq
- TensorBlock
- GGUF
base_model: vandijklab/pythia-160m-c2s
vandijklab/pythia-160m-c2s - GGUF
This repo contains GGUF format model files for vandijklab/pythia-160m-c2s.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
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Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| pythia-160m-c2s-Q2_K.gguf | Q2_K | 0.078 GB | smallest, significant quality loss - not recommended for most purposes |
| pythia-160m-c2s-Q3_K_S.gguf | Q3_K_S | 0.087 GB | very small, high quality loss |
| pythia-160m-c2s-Q3_K_M.gguf | Q3_K_M | 0.095 GB | very small, high quality loss |
| pythia-160m-c2s-Q3_K_L.gguf | Q3_K_L | 0.099 GB | small, substantial quality loss |
| pythia-160m-c2s-Q4_0.gguf | Q4_0 | 0.103 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| pythia-160m-c2s-Q4_K_S.gguf | Q4_K_S | 0.104 GB | small, greater quality loss |
| pythia-160m-c2s-Q4_K_M.gguf | Q4_K_M | 0.110 GB | medium, balanced quality - recommended |
| pythia-160m-c2s-Q5_0.gguf | Q5_0 | 0.119 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| pythia-160m-c2s-Q5_K_S.gguf | Q5_K_S | 0.119 GB | large, low quality loss - recommended |
| pythia-160m-c2s-Q5_K_M.gguf | Q5_K_M | 0.124 GB | large, very low quality loss - recommended |
| pythia-160m-c2s-Q6_K.gguf | Q6_K | 0.135 GB | very large, extremely low quality loss |
| pythia-160m-c2s-Q8_0.gguf | Q8_0 | 0.175 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/pythia-160m-c2s-GGUF --include "pythia-160m-c2s-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/pythia-160m-c2s-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'