---
license: apache-2.0
datasets:
- nicholasKluge/instruct-aira-dataset
language:
- en
metrics:
- accuracy
library_name: transformers
tags:
- alignment
- instruction tuned
- text generation
- conversation
- assistant
- TensorBlock
- GGUF
pipeline_tag: text-generation
widget:
- text: <|startofinstruction|>Can you explain what is Machine Learning?<|endofinstruction|>
  example_title: Machine Learning
- text: <|startofinstruction|>Do you know anything about virtue ethics?<|endofinstruction|>
  example_title: Ethics
- text: <|startofinstruction|>How can I make my girlfriend happy?<|endofinstruction|>
  example_title: Advise
inference:
  parameters:
    repetition_penalty: 1.2
    temperature: 0.2
    top_k: 30
    top_p: 0.3
    max_new_tokens: 200
    length_penalty: 0.3
    early_stopping: true
co2_eq_emissions:
  emissions: 1690
  source: CodeCarbon
  training_type: fine-tuning
  geographical_location: United States of America
  hardware_used: NVIDIA A100-SXM4-40GB
base_model: nicholasKluge/Aira-2-1B5
---

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</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
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## nicholasKluge/Aira-2-1B5 - GGUF

This repo contains GGUF format model files for [nicholasKluge/Aira-2-1B5](https://huggingface.co/nicholasKluge/Aira-2-1B5).

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).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Aira-2-1B5-Q2_K.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q2_K.gguf) | Q2_K | 0.845 GB | smallest, significant quality loss - not recommended for most purposes |
| [Aira-2-1B5-Q3_K_S.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q3_K_S.gguf) | Q3_K_S | 0.845 GB | very small, high quality loss |
| [Aira-2-1B5-Q3_K_M.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q3_K_M.gguf) | Q3_K_M | 0.966 GB | very small, high quality loss |
| [Aira-2-1B5-Q3_K_L.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q3_K_L.gguf) | Q3_K_L | 1.027 GB | small, substantial quality loss |
| [Aira-2-1B5-Q4_0.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q4_0.gguf) | Q4_0 | 0.906 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Aira-2-1B5-Q4_K_S.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q4_K_S.gguf) | Q4_K_S | 1.037 GB | small, greater quality loss |
| [Aira-2-1B5-Q4_K_M.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q4_K_M.gguf) | Q4_K_M | 1.110 GB | medium, balanced quality - recommended |
| [Aira-2-1B5-Q5_0.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q5_0.gguf) | Q5_0 | 1.087 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Aira-2-1B5-Q5_K_S.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q5_K_S.gguf) | Q5_K_S | 1.149 GB | large, low quality loss - recommended |
| [Aira-2-1B5-Q5_K_M.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q5_K_M.gguf) | Q5_K_M | 1.286 GB | large, very low quality loss - recommended |
| [Aira-2-1B5-Q6_K.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q6_K.gguf) | Q6_K | 1.519 GB | very large, extremely low quality loss |
| [Aira-2-1B5-Q8_0.gguf](https://huggingface.co/tensorblock/Aira-2-1B5-GGUF/blob/main/Aira-2-1B5-Q8_0.gguf) | Q8_0 | 1.630 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/Aira-2-1B5-GGUF --include "Aira-2-1B5-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/Aira-2-1B5-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```