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---
tags:
- generated_from_trainer
- TensorBlock
- GGUF
license: mit
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
base_model: HuggingFaceH4/zephyr-7b-beta
widget:
- example_title: Pirate!
  messages:
  - role: system
    content: You are a pirate chatbot who always responds with Arr!
  - role: user
    content: There's a llama on my lawn, how can I get rid of him?
  output:
    text: Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight,
      but I've got a plan that might help ye get rid of 'im. Ye'll need to gather
      some carrots and hay, and then lure the llama away with the promise of a tasty
      treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet
      once again. But beware, me hearty, for there may be more llamas where that one
      came from! Arr!
pipeline_tag: text-generation
model-index:
- name: zephyr-7b-beta
  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: 62.03071672354948
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      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: 84.35570603465445
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Drop (3-Shot)
      type: drop
      split: validation
      args:
        num_few_shot: 3
    metrics:
    - type: f1
      value: 9.66243708053691
      name: f1 score
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      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: 57.44916942762855
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      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: 12.736921910538287
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      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: 61.07
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      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: 77.7426992896606
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AlpacaEval
      type: tatsu-lab/alpaca_eval
    metrics:
    - type: unknown
      value: 0.906
      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: 7.34
      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>

## HuggingFaceH4/zephyr-7b-beta - GGUF

This repo contains GGUF format model files for [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta).

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

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

## Model file specification

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