File size: 7,301 Bytes

---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: true
new_version: tiiuae/falcon-11B
widget:
- text: Hey Falcon! Any recommendations for my holidays in Abu Dhabi?
  example_title: Abu Dhabi Trip
- text: What's the Everett interpretation of quantum mechanics?
  example_title: 'Q/A: Quantum & Answers'
- text: Give me a list of the top 10 dive sites you would recommend around the world.
  example_title: Diving Top 10
- text: Can you tell me more about deep-water soloing?
  example_title: Extreme sports
- text: Can you write a short tweet about the Apache 2.0 release of our latest AI
    model, Falcon LLM?
  example_title: Twitter Helper
- text: What are the responsabilities of a Chief Llama Officer?
  example_title: Trendy Jobs
license: apache-2.0
base_model: tiiuae/falcon-7b-instruct
tags:
- TensorBlock
- GGUF
---

<div style="width: auto; margin-left: auto; margin-right: auto">
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## tiiuae/falcon-7b-instruct - GGUF

This repo contains GGUF format model files for [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct).

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).


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      ">🚀 Try it now! 🚀</a>
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        font-weight: bold;
        font-family: sans-serif;
      ">👀 See what we built 👀</a>
    </th>
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</table>
## Prompt template


```
{system_prompt}

User: {prompt}

Assistant:
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [falcon-7b-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q2_K.gguf) | Q2_K | 3.595 GB | smallest, significant quality loss - not recommended for most purposes |
| [falcon-7b-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q3_K_S.gguf) | Q3_K_S | 3.595 GB | very small, high quality loss |
| [falcon-7b-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q3_K_M.gguf) | Q3_K_M | 3.856 GB | very small, high quality loss |
| [falcon-7b-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q3_K_L.gguf) | Q3_K_L | 4.078 GB | small, substantial quality loss |
| [falcon-7b-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q4_0.gguf) | Q4_0 | 3.922 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [falcon-7b-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q4_K_S.gguf) | Q4_K_S | 4.420 GB | small, greater quality loss |
| [falcon-7b-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q4_K_M.gguf) | Q4_K_M | 4.633 GB | medium, balanced quality - recommended |
| [falcon-7b-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q5_0.gguf) | Q5_0 | 4.727 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [falcon-7b-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q5_K_S.gguf) | Q5_K_S | 4.976 GB | large, low quality loss - recommended |
| [falcon-7b-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q5_K_M.gguf) | Q5_K_M | 5.338 GB | large, very low quality loss - recommended |
| [falcon-7b-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q6_K.gguf) | Q6_K | 6.548 GB | very large, extremely low quality loss |
| [falcon-7b-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/falcon-7b-instruct-GGUF/blob/main/falcon-7b-instruct-Q8_0.gguf) | Q8_0 | 7.145 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/falcon-7b-instruct-GGUF --include "falcon-7b-instruct-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/falcon-7b-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```