---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
datasets:
- ai2_arc
- unalignment/spicy-3.1
- codeparrot/apps
- facebook/belebele
- boolq
- jondurbin/cinematika-v0.1
- drop
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- cais/mmlu
- Muennighoff/natural-instructions
- openbookqa
- piqa
- Vezora/Tested-22k-Python-Alpaca
- cakiki/rosetta-code
- Open-Orca/SlimOrca
- spider
- squad_v2
- migtissera/Synthia-v1.3
- datasets/winogrande
- nvidia/HelpSteer
- Intel/orca_dpo_pairs
- unalignment/toxic-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- allenai/ultrafeedback_binarized_cleaned
- Squish42/bluemoon-fandom-1-1-rp-cleaned
- LDJnr/Capybara
- JULIELab/EmoBank
- kingbri/PIPPA-shareGPT
base_model: jondurbin/bagel-dpo-34b-v0.2
tags:
- TensorBlock
- GGUF
---
## jondurbin/bagel-dpo-34b-v0.2 - GGUF
This repo contains GGUF format model files for [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
## Prompt template
```
[INST] <>
{system_prompt}
<>
{prompt} [/INST]
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [bagel-dpo-34b-v0.2-Q2_K.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q2_K.gguf) | Q2_K | 11.944 GB | smallest, significant quality loss - not recommended for most purposes |
| [bagel-dpo-34b-v0.2-Q3_K_S.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q3_K_S.gguf) | Q3_K_S | 13.933 GB | very small, high quality loss |
| [bagel-dpo-34b-v0.2-Q3_K_M.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q3_K_M.gguf) | Q3_K_M | 15.511 GB | very small, high quality loss |
| [bagel-dpo-34b-v0.2-Q3_K_L.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q3_K_L.gguf) | Q3_K_L | 16.894 GB | small, substantial quality loss |
| [bagel-dpo-34b-v0.2-Q4_0.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q4_0.gguf) | Q4_0 | 18.130 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [bagel-dpo-34b-v0.2-Q4_K_S.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q4_K_S.gguf) | Q4_K_S | 18.253 GB | small, greater quality loss |
| [bagel-dpo-34b-v0.2-Q4_K_M.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q4_K_M.gguf) | Q4_K_M | 19.240 GB | medium, balanced quality - recommended |
| [bagel-dpo-34b-v0.2-Q5_0.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q5_0.gguf) | Q5_0 | 22.080 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [bagel-dpo-34b-v0.2-Q5_K_S.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q5_K_S.gguf) | Q5_K_S | 22.080 GB | large, low quality loss - recommended |
| [bagel-dpo-34b-v0.2-Q5_K_M.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q5_K_M.gguf) | Q5_K_M | 22.651 GB | large, very low quality loss - recommended |
| [bagel-dpo-34b-v0.2-Q6_K.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q6_K.gguf) | Q6_K | 26.276 GB | very large, extremely low quality loss |
| [bagel-dpo-34b-v0.2-Q8_0.gguf](https://huggingface.co/tensorblock/bagel-dpo-34b-v0.2-GGUF/blob/main/bagel-dpo-34b-v0.2-Q8_0.gguf) | Q8_0 | 34.033 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/bagel-dpo-34b-v0.2-GGUF --include "bagel-dpo-34b-v0.2-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/bagel-dpo-34b-v0.2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```