README.md

---
language:
- eng
license:
- mit
tags:
- llama-2
- sft
datasets:
- LDJnr/Puffin
model_name: Redmond Puffin 13B V1.3
base_model: NousResearch/Redmond-Puffin-13B
inference: false
model_creator: NousResearch
model_type: llama
prompt_template: '### human: {prompt}


  ### response:

  '
quantized_by: TheBloke
---

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# Redmond Puffin 13B V1.3 - GGUF
- Model creator: [NousResearch](https://huggingface.co/NousResearch)
- Original model: [Redmond Puffin 13B V1.3](https://huggingface.co/NousResearch/Redmond-Puffin-13B)

<!-- description start -->
## Description

This repo contains GGUF format model files for [NousResearch's Redmond Puffin 13B V1.3](https://huggingface.co/NousResearch/Redmond-Puffin-13B).

<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

Here is an incomplate list of clients and libraries that are known to support GGUF:

* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.

<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Redmond-Puffin-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF)
* [NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Redmond-Puffin-13B)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: Human-Response2

```
### human: {prompt}

### response:

```

<!-- prompt-template end -->
<!-- licensing start -->
## Licensing

The creator of the source model has listed its license as `['mit']`, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [NousResearch's Redmond Puffin 13B V1.3](https://huggingface.co/NousResearch/Redmond-Puffin-13B).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

## Explanation of quantisation methods
<details>
  <summary>Click to see details</summary>

The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->

<!-- README_GGUF.md-provided-files start -->
## Provided files

| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [redmond-puffin-13b.Q2_K.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [redmond-puffin-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [redmond-puffin-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [redmond-puffin-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [redmond-puffin-13b.Q4_0.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [redmond-puffin-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [redmond-puffin-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [redmond-puffin-13b.Q5_0.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [redmond-puffin-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [redmond-puffin-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [redmond-puffin-13b.Q6_K.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [redmond-puffin-13b.Q8_0.gguf](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGUF/blob/main/redmond-puffin-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |

**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.



<!-- README_GGUF.md-provided-files end -->

<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files

**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev

### In `text-generation-webui`

Under Download Model, you can enter the model repo: TheBloke/Redmond-Puffin-13B-GGUF and below it, a specific filename to download, such as: redmond-puffin-13b.q4_K_M.gguf.

Then click Download.

### On the command line, including multiple files at once

I recommend using the `huggingface-hub` Python library:

```shell
pip3 install huggingface-hub>=0.17.1
```

Then you can download any individual model file to the current directory, at high speed, with a command like this:

```shell
huggingface-cli download TheBloke/Redmond-Puffin-13B-GGUF redmond-puffin-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

<details>
  <summary>More advanced huggingface-cli download usage</summary>

You can also download multiple files at once with a pattern:

```shell
huggingface-cli download TheBloke/Redmond-Puffin-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```

For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).

To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:

```shell
pip3 install hf_transfer
```

And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:

```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Redmond-Puffin-13B-GGUF redmond-puffin-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->

<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command

Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

```shell
./main -ngl 32 -m redmond-puffin-13b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### human: {prompt}\n\n### response:"
```

Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

## How to run in `text-generation-webui`

Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).

## How to run from Python code

You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.

### How to load this model from Python using ctransformers

#### First install the package

```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```

#### Simple example code to load one of these GGUF models

```python
from ctransformers import AutoModelForCausalLM

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Redmond-Puffin-13B-GGUF", model_file="redmond-puffin-13b.q4_K_M.gguf", model_type="llama", gpu_layers=50)

print(llm("AI is going to"))
```

## How to use with LangChain

Here's guides on using llama-cpp-python or ctransformers with LangChain:

* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

<!-- README_GGUF.md-how-to-run end -->

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## Discord

For further support, and discussions on these models and AI in general, join us at:

[TheBloke AI's Discord server](https://discord.gg/theblokeai)

## Thanks, and how to contribute

Thanks to the [chirper.ai](https://chirper.ai) team!

Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

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**Special thanks to**: Aemon Algiz.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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<!-- original-model-card start -->
# Original model card: NousResearch's Redmond Puffin 13B V1.3


## **Redmond-Puffin-13b-V1.3**

**The first commercially available language model released by Nous Research!**

Redmond-Puffin-13B is likely the worlds first llama-2 based, fine-tuned language models, leveraging a hand curated set of 3K high quality examples, many of which take full advantage of the 4096 context length of Llama 2. This model was fine-tuned by Nous Research, with LDJ leading the training and dataset curation, along with significant dataset formation contributions by J-Supha.

Special thank you to Redmond AI for sponsoring the compute.

Special thank you to Emozilla for assisting with training experimentations and many issues encountered during training.

Notable mentions for assisting in some of the training issues goes to: Caseus and Teknium.

## Model Training

Redmond-Puffin 13B-V1.3 is a new model trained for multiple epochs on a dataset of 3,000 carefully curated GPT-4 examples, most of which are long context conversations between a real human and GPT-4.

Additional data came from carefully curated sub sections of datasets such as CamelAI's Physics, Chemistry, Biology and Math.

## Prompt Format

The reccomended model usage is:

```
### human:

### response:
```
Optional reccomended pre-prompt / system prompt:

```
### human: Interact in conversation to the best of your ability, please be concise, logical, intelligent and coherent.

### response: Sure! sounds good.
```

## When should I use Puffin or Hermes 2?

Puffin and Hermes-2 both beat previous SOTA for GPT4ALL benchmarks, with Hermes-2 winning by a 0.1% margin over Puffin.

- Hermes 2 is trained on purely single turn instruction examples.

- Puffin is trained mostly on multi-turn, long context, highly curated and cleaned GPT-4 conversations with real humans, as well as curated single-turn examples relating to Physics, Bio, Math and Chem.

For these reasons, it's reccomended to give Puffin a try if you want to have multi-turn conversations and/or long context communication.

## Example Outputs!:

![puffin](https://i.imgur.com/P0MsN8B.png)

![puffin](https://i.imgur.com/8EO3ThV.png)

![puffin](https://i.imgur.com/5IWolFw.png)

![puffin](https://i.imgur.com/TQui8m7.png)

![puffin](https://i.imgur.com/tderIfl.png)

## Notable Features:

 - The first Llama-2 based fine-tuned model released by Nous Research.

 - Ability to recall information upto 2023 without internet (ChatGPT cut off date is in 2021)

 - Pretrained on 2 trillion tokens of text. (This is double the amount of most Open LLM's)

 - Pretrained with a context length of 4096 tokens, and fine-tuned on a significant amount of multi-turn conversations reaching that full token limit.

 - The first commercially available language model released by Nous Research.

## Current Limitations

Some token mismatch problems and formatting issues have been idenitifed, these may very possibly effect the current output quality.

We plan to have these solved in an updated Puffin model in the very near future, please stay tuned!

## Future Plans

This is a relatively early build amongst the grand plans for the future of Puffin!

Current limitations: Some token mismatch problems have been identified, these may effect the current output quality, we plan to have this solved in Puffin V2 along with other improvements.

## How you can help!

In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations.

If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!

## Benchmarks!

As of Puffins release, it achieves a new SOTA for the GPT4All benchmarks! Supplanting Hermes for the #1 position!
(Rounded to nearest tenth)

Previous Sota: Hermes - 68.8
New Sota:      Puffin - 69.9 (+1.1)

note: After release, Puffin has since had its average GPT4All score beaten by 0.1%, by Nous' very own Model Hermes-2!
Latest SOTA w/ Hermes 2- 70.0 (+0.1 over Puffins 69.9 score)

That being said, Puffin supplants Hermes-2 for the #1 spot in Arc-E, HellaSwag and Winogrande!

Puffin also perfectly ties with Hermes in PIQA, however Hermes-2 still excels in much of Big Bench and AGIEval, so it's highly reccomended you give it a try as well!

GPT4all :

```
|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.4983|±  |0.0146|
|             |       |acc_norm|0.5068|±  |0.0146|
|arc_easy     |      0|acc     |0.7980|±  |0.0082|
|             |       |acc_norm|0.7757|±  |0.0086|
|boolq        |      1|acc     |0.8150|±  |0.0068|
|hellaswag    |      0|acc     |0.6132|±  |0.0049|
|             |       |acc_norm|0.8043|±  |0.0040|
|openbookqa   |      0|acc     |0.3560|±  |0.0214|
|             |       |acc_norm|0.4560|±  |0.0223|
|piqa         |      0|acc     |0.7954|±  |0.0094|
|             |       |acc_norm|0.8069|±  |0.0092|
|winogrande   |      0|acc     |0.7245|±  |0.0126|
```



```
|                      Task                      |Version|       Metric        |Value |   |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5368|±  |0.0363|
|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7127|±  |0.0236|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3023|±  |0.0286|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.1003|±  |0.0159|
|                                                |       |exact_str_match      |0.0000|±  |0.0000|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2520|±  |0.0194|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.1743|±  |0.0143|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4200|±  |0.0285|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.2900|±  |0.0203|
|bigbench_navigate                               |      0|multiple_choice_grade|0.5000|±  |0.0158|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.5430|±  |0.0111|
|bigbench_ruin_names                             |      0|multiple_choice_grade|0.4442|±  |0.0235|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.2074|±  |0.0128|
|bigbench_snarks                                 |      0|multiple_choice_grade|0.5083|±  |0.0373|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.4970|±  |0.0159|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.3260|±  |0.0148|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2136|±  |0.0116|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1326|±  |0.0081|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4200|±  |0.0285|
```

AGI Eval:

```
|             Task             |Version| Metric |Value |   |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |0.2283|±  |0.0264|
|                              |       |acc_norm|0.2244|±  |0.0262|
|agieval_logiqa_en             |      0|acc     |0.2780|±  |0.0176|
|                              |       |acc_norm|0.3164|±  |0.0182|
|agieval_lsat_ar               |      0|acc     |0.2348|±  |0.0280|
|                              |       |acc_norm|0.2043|±  |0.0266|
|agieval_lsat_lr               |      0|acc     |0.3392|±  |0.0210|
|                              |       |acc_norm|0.2961|±  |0.0202|
|agieval_lsat_rc               |      0|acc     |0.4387|±  |0.0303|
|                              |       |acc_norm|0.3569|±  |0.0293|
|agieval_sat_en                |      0|acc     |0.5874|±  |0.0344|
|                              |       |acc_norm|0.5194|±  |0.0349|
|agieval_sat_en_without_passage|      0|acc     |0.4223|±  |0.0345|
|                              |       |acc_norm|0.3447|±  |0.0332|
|agieval_sat_math              |      0|acc     |0.3364|±  |0.0319|
|                              |       |acc_norm|0.2773|±  |0.0302|
```

<!-- original-model-card end -->