---
library_name: peft
license: unknown
datasets:
- ehartford/dolphin
- garage-bAInd/Open-Platypus
tags:
- falcon
inference: false
pipeline_tag: text-generation
---

# falcon-180b-instruct-peft 🦅

This instruction model was built via parameter-efficient QLoRA finetuning of [falcon-180b](https://huggingface.co/tiiuae/falcon-180B) on the first 5k rows of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) and the first 5k riws of [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). Finetuning was executed on 4x A6000s (48 GB RTX) for roughly XX hours on the [Lambda Labs](https://cloud.lambdalabs.com/instances) platform.

### Benchmark metrics

| Metric                | Value |
|-----------------------|-------|
| MMLU (5-shot)         | Coming |
| ARC (25-shot)         | Coming |
| HellaSwag (10-shot)   | Coming |
| TruthfulQA (0-shot)   | Coming |
| Avg.                  | Coming |

We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).

### Helpful Links

* Model license: [Falcon-180B TII License](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt)
* Basic usage: coming
* Finetuning code: coming
* Loss curves: coming
* Runtime stats: coming

### Example prompts and responses

Example 1: 

**User**:
> You are a helpful assistant. Write me a numbered list of things to do in New York City.\n

**falcon-180b-instruct-peft**:
coming

<br>

Example 2: 

**User**:

> You are a helpful assistant. Write a short email inviting my friends to a dinner party on Friday. Respond succinctly.\n

**falcon-180b-instruct-peft**:

coming

<br>

Example 3: 

**User**:

> You are a helpful assistant. Tell me a recipe for vegan banana bread.\n

**falcon-180b-instruct-peft**:

coming

<br>


## Finetuning Description

![loss curves](https://raw.githubusercontent.com/daniel-furman/sft-demos/main/assets/jul_24_23_1_14_00_log_loss_curves_falcon-180b-instruct-peft.png)

The above loss curve was generated from the run's private wandb.ai log.  

## Limitations and Biases

_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_

This model can produce factually incorrect output, and should not be relied on to produce factually accurate information.
This model was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

## How to Use

coming

### Runtime tests

coming

## Acknowledgements

This model was finetuned by Daniel Furman on Sep 10, 2023 and is for RESEARCH ONLY PURPOSES.

## Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.

## tiiuae/falcon-180B citation

```
@article{falcon,
  title={The Falcon Series of Language Models: Towards Open Frontier Models},
  author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Alhammadi, Maitha and Daniele, Mazzotta and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
  year={2023}
}
```

---

### Framework versions


- PEFT 0.5.0.dev0