---
datasets:
- argilla/ultrafeedback-binarized-preferences
language:
- en
base_model: argilla/notus-7b-v1
library_name: transformers
pipeline_tag: text-generation
tags:
- dpo
- rlaif
- preference
- ultrafeedback
- TensorBlock
- GGUF
license: mit
model-index:
- name: notus-7b-v1
  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: 0.6459044368600683
      name: normalized accuracy
    source:
      url: https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
      name: Open LLM Leaderboard Results
  - 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: 0.8478390758812986
      name: normalized accuracy
    source:
      url: https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
      name: Open LLM Leaderboard Results
  - 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: 0.5436768358952805
    source:
      url: https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
      name: Open LLM Leaderboard Results
  - 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: 0.6303308230938872
      name: accuracy
    source:
      url: https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
      name: Open LLM Leaderboard Results
  - 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: 0.1516300227445034
      name: accuracy
    source:
      url: https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
      name: Open LLM Leaderboard Results
  - 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: 0.7940015785319653
      name: accuracy
    source:
      url: https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
      name: Open LLM Leaderboard Results
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AlpacaEval
      type: tatsu-lab/alpaca_eval
    metrics:
    - type: tatsu-lab/alpaca_eval
      value: 0.9142
      name: win rate
    source:
      url: https://tatsu-lab.github.io/alpaca_eval/
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MT-Bench
      type: unknown
    metrics:
    - type: unknown
      value: 7.3
      name: score
    source:
      url: https://huggingface.co/spaces/lmsys/mt-bench
---

<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>

## argilla/notus-7b-v1 - GGUF

This repo contains GGUF format model files for [argilla/notus-7b-v1](https://huggingface.co/argilla/notus-7b-v1).

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).


## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
  <th style="font-size: 25px;">Awesome MCP Servers</th>
  <th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
  <tr>
    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
  </tr>
  <tr>
    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
  </tr>
<tr>
  <th>
    <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
      display: inline-block;
      padding: 8px 16px;
      background-color: #FF7F50;
      color: white;
      text-decoration: none;
      border-radius: 6px;
      font-weight: bold;
      font-family: sans-serif;
    ">👀 See what we built 👀</a>
  </th>
  <th>
    <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
      display: inline-block;
      padding: 8px 16px;
      background-color: #FF7F50;
      color: white;
      text-decoration: none;
      border-radius: 6px;
      font-weight: bold;
      font-family: sans-serif;
    ">👀 See what we built 👀</a>
  </th>
</tr>
</table>
## Prompt template


```
<|system|>
{system_prompt}</s>
<|user|>
{prompt}</s>
<|assistant|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [notus-7b-v1-Q2_K.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q2_K.gguf) | Q2_K | 2.532 GB | smallest, significant quality loss - not recommended for most purposes |
| [notus-7b-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q3_K_S.gguf) | Q3_K_S | 2.947 GB | very small, high quality loss |
| [notus-7b-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q3_K_M.gguf) | Q3_K_M | 3.277 GB | very small, high quality loss |
| [notus-7b-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q3_K_L.gguf) | Q3_K_L | 3.560 GB | small, substantial quality loss |
| [notus-7b-v1-Q4_0.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q4_0.gguf) | Q4_0 | 3.827 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [notus-7b-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q4_K_S.gguf) | Q4_K_S | 3.856 GB | small, greater quality loss |
| [notus-7b-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q4_K_M.gguf) | Q4_K_M | 4.068 GB | medium, balanced quality - recommended |
| [notus-7b-v1-Q5_0.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q5_0.gguf) | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [notus-7b-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q5_K_S.gguf) | Q5_K_S | 4.654 GB | large, low quality loss - recommended |
| [notus-7b-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q5_K_M.gguf) | Q5_K_M | 4.779 GB | large, very low quality loss - recommended |
| [notus-7b-v1-Q6_K.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q6_K.gguf) | Q6_K | 5.534 GB | very large, extremely low quality loss |
| [notus-7b-v1-Q8_0.gguf](https://huggingface.co/tensorblock/notus-7b-v1-GGUF/blob/main/notus-7b-v1-Q8_0.gguf) | Q8_0 | 7.167 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/notus-7b-v1-GGUF --include "notus-7b-v1-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/notus-7b-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```