transformers gguf mistral alignment-handbook generated_from_trainer juanako UNA dataset:HuggingFaceH4/ultrafeedback_binarized arxiv:2109.07958 arxiv:2310.16944 arxiv:2305.18290 base_model:fblgit/juanako-7b-UNA base_model:quantized:fblgit/juanako-7b-UNA license:apache-2.0 model-index region:us conversational
---
base_model: fblgit/juanako-7b-UNA
datasets:
- HuggingFaceH4/ultrafeedback_binarized
license: apache-2.0
tags:
- alignment-handbook
- generated_from_trainer
- juanako
- mistral
- UNA
inference: false
model_creator: FBL
model_type: mistral
prompt_template: '<|im_start|>system

  {system_message}<|im_end|>

  <|im_start|>user

  {prompt}<|im_end|>

  <|im_start|>assistant

  '
quantized_by: TheBloke
model-index:
- name: juanako-7b-UNA
  results:
  - task:
      type: text-generation
      name: TruthfulQA (MC2)
    dataset:
      name: truthful_qa
      type: text-generation
      config: multiple_choice
      split: validation
    metrics:
    - type: accuracy
      value: 65.13
      verified: true
  - task:
      type: text-generation
      name: ARC-Challenge
    dataset:
      name: ai2_arc
      type: text-generation
      config: ARC-Challenge
      split: test
    metrics:
    - type: accuracy
      value: 68.17
      verified: true
  - task:
      type: text-generation
      name: HellaSwag
    dataset:
      name: Rowan/hellaswag
      type: text-generation
      split: test
    metrics:
    - type: accuracy
      value: 85.34
      verified: true
    - type: accuracy
      value: 83.57
  - task:
      type: text-generation
      name: Winogrande
    dataset:
      name: winogrande
      type: text-generation
      config: winogrande_debiased
      split: test
    metrics:
    - type: accuracy
      value: 78.85
      verified: true
  - task:
      type: text-generation
      name: MMLU
    dataset:
      name: cais/mmlu
      type: text-generation
      config: all
      split: test
    metrics:
    - type: accuracy
      value: 62.47
      verified: true
  - task:
      type: text-generation
      name: DROP
    dataset:
      name: drop
      type: text-generation
      split: validation
    metrics:
    - type: accuracy
      value: 38.74
      verified: true
  - task:
      type: text-generation
      name: PubMedQA
    dataset:
      name: bigbio/pubmed_qa
      type: text-generation
      config: pubmed_qa_artificial_bigbio_qa
      split: validation
    metrics:
    - type: accuracy
      value: 76.0
---
<!-- markdownlint-disable MD041 -->

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# Juanako 7B UNA - GGUF
- Model creator: [FBL](https://huggingface.co/fblgit)
- Original model: [Juanako 7B UNA](https://huggingface.co/fblgit/juanako-7b-UNA)

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

This repo contains GGUF format model files for [FBL's Juanako 7B UNA](https://huggingface.co/fblgit/juanako-7b-UNA).

These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).

<!-- 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.

Here is an incomplete 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

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

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/juanako-7B-UNA-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/juanako-7B-UNA-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF)
* [FBL's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/fblgit/juanako-7b-UNA)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: ChatML

```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

```

<!-- prompt-template end -->


<!-- compatibility_gguf start -->
## Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [juanako-7b-una.Q2_K.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [juanako-7b-una.Q3_K_S.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [juanako-7b-una.Q3_K_M.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [juanako-7b-una.Q3_K_L.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [juanako-7b-una.Q4_0.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [juanako-7b-una.Q4_K_S.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [juanako-7b-una.Q4_K_M.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [juanako-7b-una.Q5_0.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [juanako-7b-una.Q5_K_S.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [juanako-7b-una.Q5_K_M.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [juanako-7b-una.Q6_K.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [juanako-7b-una.Q8_0.gguf](https://huggingface.co/TheBloke/juanako-7B-UNA-GGUF/blob/main/juanako-7b-una.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 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/juanako-7B-UNA-GGUF and below it, a specific filename to download, such as: juanako-7b-una.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
```

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/juanako-7B-UNA-GGUF juanako-7b-una.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

<details>
  <summary>More advanced huggingface-cli download usage (click to read)</summary>

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

```shell
huggingface-cli download TheBloke/juanako-7B-UNA-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
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/juanako-7B-UNA-GGUF juanako-7b-una.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before 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 [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

```shell
./main -ngl 35 -m juanako-7b-una.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```

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

Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

### How to load this model in Python code, using llama-cpp-python

For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).

#### First install the package

Run one of the following commands, according to your system:

```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```

#### Simple llama-cpp-python example code

```python
from llama_cpp import Llama

# 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 = Llama(
  model_path="./juanako-7b-una.Q4_K_M.gguf",  # Download the model file first
  n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)

# Chat Completion API

llm = Llama(model_path="./juanako-7b-una.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)
```

## How to use with LangChain

Here are guides on using llama-cpp-python and 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 -->

<!-- footer start -->
<!-- 200823 -->
## 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.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI

**Special thanks to**: Aemon Algiz.

**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius


Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

<!-- footer end -->

<!-- original-model-card start -->
# Original model card: FBL's Juanako 7B UNA


# juanako-7b-UNA (Uniform Neural Alignment)

This model is a fine-tuned version of [fblgit/juanako-7b-UNA-v2-phase-1](https://huggingface.co/fblgit/juanako-7b-UNA-v2-phase-1) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It outperforms in many aspects most of the current Mistral based models and is the **latest and most powerful juanako version as of now**.

## Scores

The official HuggingFace results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/fblgit/juanako-7b-UNA/results_2023-11-28T08-33-33.965228.json)

| Model | Average ⬆️| ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️| TruthfulQA (MC) (0-s) ⬆️ | Winogrande (5-s) | GSM8K (5-s) | DROP (3-s) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 50.32 | 59.58  | 83.31  | 64.16  | 42.15 | 78.37 | 18.12 | 6.14 |
| [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) | 59.0 | 66.21 | 83.64 | 62.37  | 59.65 | 78.14 | 19.56 | 43.84 |
| [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA) | **59.91** | **68.17** | **85.34** | 62.47  | **65.13** | **78.85** | **20.7** | 38.74 |

It scores: **59.91** according HuggingFace LLM Leaderboard.
It scores: **65.1** with `big-refactor` branch of lm-eval-harness

Author [Xavier M.](mailto:xavi@juanako.ai) @fblgit

## Model description

juanako uses UNA, Uniform Neural Alignment. A training technique that ease alignment between transformer layers yet to be published.

### Prompts
The following prompts showed positive results, it may depend the task and needs further experimentation but this should work for starters:
```
<|im_start|>system
- You are a helpful assistant chatbot trained by MosaicML.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
<|im_start|>user
Explain QKV<|im_end|>
<|im_start|>assistant
```
```
### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!

### Human: Explain QKV
### Assistant:
```
```
[Round <|round|>]
问：Explain QKV
答：
```
```
[Round <|round|>]
Question：Explain QKV
Answer：
```
```
Question：Explain QKV
Answer：
```

## Evaluations (lm-eval big-refactor branch)

### TruthfulQA 0-Shot
```
|    Tasks     |Version|Filter|Metric|Value |   |Stderr|
|--------------|-------|------|------|-----:|---|-----:|
|truthfulqa_mc2|Yaml   |none  |acc   |0.6549|±  |0.0153|
```
### ARC 25-Shot
```
|    Tasks    |Version|Filter| Metric |Value |   |Stderr|
|-------------|-------|------|--------|-----:|---|-----:|
|arc_challenge|Yaml   |none  |acc     |0.6476|±  |0.0140|
|             |       |none  |acc_norm|0.6809|±  |0.0136|
```
### HellaSwag 10-Shot
```
|  Tasks  |Version|Filter| Metric |Value |   |Stderr|
|---------|-------|------|--------|-----:|---|-----:|
|hellaswag|Yaml   |none  |acc     |0.6703|±  |0.0047|
|         |       |none  |acc_norm|0.8520|±  |0.0035|
```
### GSM8k 5-Shot
```
|Tasks|Version|  Filter  |  Metric   |Value |   |Stderr|
|-----|-------|----------|-----------|-----:|---|-----:|
|gsm8k|Yaml   |get-answer|exact_match|0.4898|±  |0.0138|
```
### GPT Evaluations 0-Shot
```
|    Tasks     |Version|Filter|  Metric  |Value |   |Stderr|
|--------------|-------|------|----------|-----:|---|-----:|
|boolq         |Yaml   |none  |acc       |0.8703|±  |0.0059|
|lambada_openai|Yaml   |none  |perplexity|3.2598|±  |0.0705|
|              |       |none  |acc       |0.7336|±  |0.0062|
|piqa          |Yaml   |none  |acc       |0.8254|±  |0.0089|
|              |       |none  |acc_norm  |0.8292|±  |0.0088|
|sciq          |Yaml   |none  |acc       |0.9580|±  |0.0063|
|              |       |none  |acc_norm  |0.9130|±  |0.0089|
```
### MathQA 0-Shot
```
|Tasks |Version|Filter| Metric |Value |   |Stderr|
|------|-------|------|--------|-----:|---|-----:|
|mathqa|Yaml   |none  |acc     |0.3752|±  |0.0089|
|      |       |none  |acc_norm|0.3772|±  |0.0089|
```
### PiQa 1-Shot
```
|Tasks|Version|Filter| Metric |Value |   |Stderr|
|-----|-------|------|--------|-----:|---|-----:|
|piqa |Yaml   |none  |acc     |0.8308|±  |0.0087|
|     |       |none  |acc_norm|0.8357|±  |0.0086|
```
### Winogrande 5-Shot
```
|  Tasks   |Version|Filter|Metric|Value|   |Stderr|
|----------|-------|------|------|----:|---|-----:|
|winogrande|Yaml   |none  |acc   |0.768|±  |0.0119|
```
### PubMedQA 0-Shot
```
| Tasks  |Version|Filter|Metric|Value|   |Stderr|
|--------|-------|------|------|----:|---|-----:|
|pubmedqa|Yaml   |none  |acc   | 0.76|±  |0.0191|
```
### RACE 1-Shot
```
|Tasks|Version|Filter|Metric|Value |   |Stderr|
|-----|-------|------|------|-----:|---|-----:|
|race |Yaml   |none  |acc   |0.5282|±  |0.0154|
```
### MMLU 5-Shot (8-Bit)
```
|      Groups      |Version|Filter|Metric|Value |   |Stderr|
|------------------|-------|------|------|-----:|---|-----:|
|mmlu              |N/A    |none  |acc   |0.6137|±  |0.1243|
| - humanities     |N/A    |none  |acc   |0.5671|±  |0.1101|
| - other          |N/A    |none  |acc   |0.6859|±  |0.1164|
| - social_sciences|N/A    |none  |acc   |0.7195|±  |0.0713|
| - stem           |N/A    |none  |acc   |0.5087|±  |0.1297|
```
### DROP 3-Shot (8-Bit) (Instruct-Eval)
```
{'score': 0.49801113762927607}
{'drop': 49.8}
drop: 49.8
```

### CRASS 0-Shot (Instruct-Eval)
```
{'score': 0.8357664233576643}
{'crass': 83.58}
crass: 83.58
```

## Training Details

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 14
- gradient_accumulation_steps: 16
- total_train_batch_size: 224
- total_eval_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4795        | 0.2   | 56   | 0.4958          | -1.3684        | -2.6385          | 0.7552             | 1.2701          | -265.3887      | -241.2612    | -2.2572         | -2.4922       |
| 0.4642        | 0.4   | 112  | 0.4859          | -1.0380        | -1.9769          | 0.7273             | 0.9389          | -258.7718      | -237.9569    | -2.2414         | -2.4751       |
| 0.4758        | 0.61  | 168  | 0.4808          | -1.2594        | -2.3704          | 0.7343             | 1.1110          | -262.7074      | -240.1708    | -2.2305         | -2.4633       |
| 0.4549        | 0.81  | 224  | 0.4768          | -1.1906        | -2.3201          | 0.7552             | 1.1295          | -262.2044      | -239.4827    | -2.2284         | -2.4610       |


### Framework versions

- Transformers 4.35.0-UNA
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1

## Citations
If you find juanako useful please:

```
@misc{juanako7buna,
  title={Juanako: Uniform Neural Alignment},
  author={Xavier Murias},
  year={2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/fblgit/juanako-7b-UNA}},
}
```

Thanks to all the brilliant humans behind the creation of AI, here some of the ones that we find relevant to our research. If you feel a citation is missing, please contact.
```
@misc{lin2021truthfulqa,
  title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
  author={Stephanie Lin and Jacob Hilton and Owain Evans},
  year={2021},
  eprint={2109.07958},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
@misc{tunstall2023zephyr,
      title={Zephyr: Direct Distillation of LM Alignment},
      author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
      year={2023},
      eprint={2310.16944},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@inproceedings{Bisk2020,
  author = {Yonatan Bisk and Rowan Zellers and
            Ronan Le Bras and Jianfeng Gao
            and Yejin Choi},
  title = {PIQA: Reasoning about Physical Commonsense in
           Natural Language},
  booktitle = {Thirty-Fourth AAAI Conference on
               Artificial Intelligence},
  year = {2020},
}
@software{eval-harness,
  author       = {Gao, Leo and
                  Tow, Jonathan and
                  Biderman, Stella and
                  Black, Sid and
                  DiPofi, Anthony and
                  Foster, Charles and
                  Golding, Laurence and
                  Hsu, Jeffrey and
                  McDonell, Kyle and
                  Muennighoff, Niklas and
                  Phang, Jason and
                  Reynolds, Laria and
                  Tang, Eric and
                  Thite, Anish and
                  Wang, Ben and
                  Wang, Kevin and
                  Zou, Andy},
  title        = {A framework for few-shot language model evaluation},
  month        = sep,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.0.1},
  doi          = {10.5281/zenodo.5371628},
  url          = {https://doi.org/10.5281/zenodo.5371628}
}
@misc{rafailov2023direct,
    title={Direct Preference Optimization: Your Language Model is Secretly a Reward Model},
    author={Rafael Rafailov and Archit Sharma and Eric Mitchell and Stefano Ermon and Christopher D. Manning and Chelsea Finn},
    year={2023},
    eprint={2305.18290},
    archivePrefix={arXiv},
}
```

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