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---
license: cc-by-nc-4.0
language:
- en
- de
- fr
- zh
- pt
- nl
- ru
- ko
- it
- es
metrics:
- comet
pipeline_tag: translation
base_model: Unbabel/TowerBase-7B-v0.1
tags:
- TensorBlock
- GGUF
model-index:
- name: TowerBase-7B-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 51.02
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Unbabel/TowerBase-7B-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 77.68
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Unbabel/TowerBase-7B-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 43.48
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Unbabel/TowerBase-7B-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 37.29
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Unbabel/TowerBase-7B-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.06
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Unbabel/TowerBase-7B-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 13.12
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Unbabel/TowerBase-7B-v0.1
name: Open LLM Leaderboard
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;">
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
</p>
</div>
</div>
## Unbabel/TowerBase-7B-v0.1 - GGUF
This repo contains GGUF format model files for [Unbabel/TowerBase-7B-v0.1](https://huggingface.co/Unbabel/TowerBase-7B-v0.1).
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
```
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [TowerBase-7B-v0.1-Q2_K.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q2_K.gguf) | Q2_K | 2.359 GB | smallest, significant quality loss - not recommended for most purposes |
| [TowerBase-7B-v0.1-Q3_K_S.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q3_K_S.gguf) | Q3_K_S | 2.746 GB | very small, high quality loss |
| [TowerBase-7B-v0.1-Q3_K_M.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q3_K_M.gguf) | Q3_K_M | 3.072 GB | very small, high quality loss |
| [TowerBase-7B-v0.1-Q3_K_L.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q3_K_L.gguf) | Q3_K_L | 3.350 GB | small, substantial quality loss |
| [TowerBase-7B-v0.1-Q4_0.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q4_0.gguf) | Q4_0 | 3.563 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [TowerBase-7B-v0.1-Q4_K_S.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q4_K_S.gguf) | Q4_K_S | 3.592 GB | small, greater quality loss |
| [TowerBase-7B-v0.1-Q4_K_M.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q4_K_M.gguf) | Q4_K_M | 3.801 GB | medium, balanced quality - recommended |
| [TowerBase-7B-v0.1-Q5_0.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q5_0.gguf) | Q5_0 | 4.332 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [TowerBase-7B-v0.1-Q5_K_S.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q5_K_S.gguf) | Q5_K_S | 4.332 GB | large, low quality loss - recommended |
| [TowerBase-7B-v0.1-Q5_K_M.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q5_K_M.gguf) | Q5_K_M | 4.455 GB | large, very low quality loss - recommended |
| [TowerBase-7B-v0.1-Q6_K.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q6_K.gguf) | Q6_K | 5.149 GB | very large, extremely low quality loss |
| [TowerBase-7B-v0.1-Q8_0.gguf](https://huggingface.co/tensorblock/TowerBase-7B-v0.1-GGUF/tree/main/TowerBase-7B-v0.1-Q8_0.gguf) | Q8_0 | 6.669 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/TowerBase-7B-v0.1-GGUF --include "TowerBase-7B-v0.1-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/TowerBase-7B-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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