---
library_name: transformers
tags:
- TensorBlock
- GGUF
base_model: akrishnan/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123
---

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## akrishnan/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123 - GGUF

This repo contains GGUF format model files for [akrishnan/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123](https://huggingface.co/akrishnan/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4882](https://github.com/ggml-org/llama.cpp/commit/be7c3034108473beda214fd1d7c98fd6a7a3bdf5).

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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 |
| -------- | ---------- | --------- | ----------- |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q2_K.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q2_K.gguf) | Q2_K | 0.122 GB | smallest, significant quality loss - not recommended for most purposes |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q3_K_S.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q3_K_S.gguf) | Q3_K_S | 0.136 GB | very small, high quality loss |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q3_K_M.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q3_K_M.gguf) | Q3_K_M | 0.149 GB | very small, high quality loss |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q3_K_L.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q3_K_L.gguf) | Q3_K_L | 0.157 GB | small, substantial quality loss |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q4_0.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q4_0.gguf) | Q4_0 | 0.163 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q4_K_S.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q4_K_S.gguf) | Q4_K_S | 0.163 GB | small, greater quality loss |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q4_K_M.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q4_K_M.gguf) | Q4_K_M | 0.174 GB | medium, balanced quality - recommended |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q5_0.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q5_0.gguf) | Q5_0 | 0.188 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q5_K_S.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q5_K_S.gguf) | Q5_K_S | 0.188 GB | large, low quality loss - recommended |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q5_K_M.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q5_K_M.gguf) | Q5_K_M | 0.196 GB | large, very low quality loss - recommended |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q6_K.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q6_K.gguf) | Q6_K | 0.215 GB | very large, extremely low quality loss |
| [gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q8_0.gguf](https://huggingface.co/tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF/blob/main/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-Q8_0.gguf) | Q8_0 | 0.276 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF --include "gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-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:

```shell
huggingface-cli download tensorblock/gpt2-350M-unlearning-BIOSR_supersampled_biographies_x10_lr_0.0005_seed_123-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```