README.md

---
license: afl-3.0
library_name: transformers
tags:
- UNA
- juanako
datasets:
- jondurbin/py-dpo-v0.1
- Replete-AI/code_bagel_hermes-2.5
- mlabonne/orpo-dpo-mix-40k
quantized_by: bartowski
pipeline_tag: text-generation
model-index:
- name: UNA-ThePitbull-21.4B-v2
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 77.73
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 91.79
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 68.25
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 78.24
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 87.37
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 63.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
      name: Open LLM Leaderboard
---

# UNA-ThePitbull 21.4B v2

Introducing the best LLM in the industry. Nearly as good as a 70B, just a 21.4B based on saltlux/luxia-21.4b-alignment-v1.0
![UNA - ThePitbull 21.4B v2](https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2/resolve/main/DE-UNA-ThePitbull-21.4B-v2.png)

This model has not been poisoned to score high and be useless. We release him becaues its the real deal of EQ & IQ all together in a crazy powerful smart and conversational model.

## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-ThePitbull-21.4B-v2)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |77.82|
|AI2 Reasoning Challenge (25-Shot)|77.73|
|HellaSwag (10-Shot)              |91.79|
|MMLU (5-Shot)                    |68.25|
|TruthfulQA (0-shot)              |78.24|
|Winogrande (5-shot)              |87.37|
|GSM8k (5-shot)                   |63.53|

## Llamacpp imatrix Quantizations of UNA-ThePitbull-21.4B-v2

Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3001">b3001</a> for quantization.

Original model: https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2

All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)

## Prompt format

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

```

## Download a file (not the whole branch) from below:

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [UNA-ThePitbull-21.4B-v2-Q8_0.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q8_0.gguf) | Q8_0 | 22.76GB | Extremely high quality, generally unneeded but max available quant. |
| [UNA-ThePitbull-21.4B-v2-Q6_K.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q6_K.gguf) | Q6_K | 17.57GB | Very high quality, near perfect, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q5_K_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q5_K_M.gguf) | Q5_K_M | 15.17GB | High quality, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q5_K_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q5_K_S.gguf) | Q5_K_S | 14.80GB | High quality, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q4_K_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q4_K_M.gguf) | Q4_K_M | 12.91GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q4_K_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q4_K_S.gguf) | Q4_K_S | 12.27GB | Slightly lower quality with more space savings, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-IQ4_NL.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ4_NL.gguf) | IQ4_NL | 12.24GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [UNA-ThePitbull-21.4B-v2-IQ4_XS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ4_XS.gguf) | IQ4_XS | 11.60GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [UNA-ThePitbull-21.4B-v2-Q3_K_L.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q3_K_L.gguf) | Q3_K_L | 11.37GB | Lower quality but usable, good for low RAM availability. |
| [UNA-ThePitbull-21.4B-v2-Q3_K_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q3_K_M.gguf) | Q3_K_M | 10.46GB | Even lower quality. |
| [UNA-ThePitbull-21.4B-v2-IQ3_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ3_M.gguf) | IQ3_M | 9.81GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [UNA-ThePitbull-21.4B-v2-IQ3_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ3_S.gguf) | IQ3_S | 9.47GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [UNA-ThePitbull-21.4B-v2-Q3_K_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q3_K_S.gguf) | Q3_K_S | 9.43GB | Low quality, not recommended. |
| [UNA-ThePitbull-21.4B-v2-IQ3_XS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ3_XS.gguf) | IQ3_XS | 8.99GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [UNA-ThePitbull-21.4B-v2-IQ3_XXS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ3_XXS.gguf) | IQ3_XXS | 8.41GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [UNA-ThePitbull-21.4B-v2-Q2_K.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-Q2_K.gguf) | Q2_K | 8.12GB | Very low quality but surprisingly usable. |
| [UNA-ThePitbull-21.4B-v2-IQ2_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ2_M.gguf) | IQ2_M | 7.49GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [UNA-ThePitbull-21.4B-v2-IQ2_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ2_S.gguf) | IQ2_S | 6.95GB | Very low quality, uses SOTA techniques to be usable. |
| [UNA-ThePitbull-21.4B-v2-IQ2_XS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ2_XS.gguf) | IQ2_XS | 6.55GB | Very low quality, uses SOTA techniques to be usable. |
| [UNA-ThePitbull-21.4B-v2-IQ2_XXS.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ2_XXS.gguf) | IQ2_XXS | 5.95GB | Lower quality, uses SOTA techniques to be usable. |
| [UNA-ThePitbull-21.4B-v2-IQ1_M.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ1_M.gguf) | IQ1_M | 5.27GB | Extremely low quality, *not* recommended. |
| [UNA-ThePitbull-21.4B-v2-IQ1_S.gguf](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF/blob/main/UNA-ThePitbull-21.4B-v2-IQ1_S.gguf) | IQ1_S | 4.86GB | Extremely low quality, *not* recommended. |

## Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

```
pip install -U "huggingface_hub[cli]"
```

Then, you can target the specific file you want:

```
huggingface-cli download bartowski/UNA-ThePitbull-21.4B-v2-GGUF --include "UNA-ThePitbull-21.4B-v2-Q4_K_M.gguf" --local-dir ./
```

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

```
huggingface-cli download bartowski/UNA-ThePitbull-21.4B-v2-GGUF --include "UNA-ThePitbull-21.4B-v2-Q8_0.gguf/*" --local-dir UNA-ThePitbull-21.4B-v2-Q8_0
```

You can either specify a new local-dir (UNA-ThePitbull-21.4B-v2-Q8_0) or download them all in place (./)

## Which file should I choose?

A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

## Difference V1 vs V2

On V2 we implemented a different UNA strategy and covered partially the MLP's and Attention Layers.
We also performed further SFT over V1 and further DPO over V1 and we'll release some of those soon as well.

### Changes

1. SFT over V1 with `Replete-AI/code_bagel_hermes-2.5` at 1.0e-4 till 5.0e-5
2. DPO with: 1.0e-4 to min_lr 5.0e-5
* `mlabonne/orpo-dpo-mix-40k`
* `jondurbin/py-dpo-v0.1`
  
# Evaluations

Can only be compared with its non-una base model: the original luxia-21.4b and ThePitbull-v1

## UNA v2 (VLLM) Evaluations:
```
vllm (pretrained=/data/tools/mergekit/una-thepitbull-v5,dtype=bfloat16,gpu_memory_utilization=0.8,max_model_len=2048,data_parallel_size=2,tensor_parallel_size=4), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8
|    Tasks     |Version|     Filter     |n-shot|  Metric   |Value |   |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k         |      3|strict-match    |     5|exact_match|0.7695|±  |0.0116|+
|              |       |flexible-extract|     5|exact_match|0.7695|±  |0.0116|+
|hellaswag     |      1|none            |    10|acc        |0.8110|±  |0.0039|
|              |       |none            |    10|acc_norm   |0.9169|±  |0.0028|+
|winogrande    |      1|none            |     5|acc        |0.8777|±  |0.0092|+
|mmlu          |N/A    |none            |     0|acc        |0.6427|±  |0.0038|-
|arc_challenge |      1|none            |    25|acc        |0.7713|±  |0.0123|
|              |       |none            |    25|acc_norm   |0.7875|±  |0.0120|+
|truthfulqa_mc2|      2|none            |     0|acc        |0.7824|±  |0.0135|-
|mathqa        |      1|none            |     0|acc        |0.4037|±  | 0.009|
|              |       |none            |     0|acc_norm   |0.4034|±  | 0.009|+
|pubmedqa      |      1|none            |     0|acc        |0.7260|±  | 0.020|+
|boolq         |      2|none            |     0|acc        |0.8602|±  |0.0061|+
```

## UNA v1 (VLLM) Evaluations
```
|    Tasks     |Version|     Filter     |n-shot|  Metric   |Value |   |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k         |      3|strict-match    |     5|exact_match|0.7566|±  |0.0118|
|              |       |flexible-extract|     5|exact_match|0.7582|±  |0.0118|
|hellaswag     |      1|none            |    10|acc        |0.8168|±  |0.0039|
|              |       |none            |    10|acc_norm   |0.9188|±  |0.0027|
|winogrande    |      1|none            |     5|acc        |0.8635|±  |0.0097|
|mmlu          |    N/A|none            |     0|acc        |0.6444|±  |0.0038|
|arc_challenge |      1|none            |    25|acc        |0.7747|±  |0.0122|
|              |       |none            |    25|acc_norm   |0.7850|±  |0.0120|
|truthfulqa_mc2|      2|none            |     0|acc        |0.7902|±  |0.0134|
|mathqa        |      1|none            |     0|acc        |0.4030|±  | 0.009|
|              |       |none            |     0|acc_norm   |0.4034|±  | 0.009|
|pubmedqa      |      1|none            |     0|acc        |0.6860|±  |0.0208|
|boolq         |      2|none            |     0|acc        |0.8401|±  |0.0064|
```

## Original (VLLM) Evaluations
```
|    Tasks     |Version|     Filter     |n-shot|  Metric   |Value |   |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k         |      3|strict-match    |     5|exact_match|0.7528|±  |0.0119|
|              |       |flexible-extract|     5|exact_match|0.7521|±  |0.0119|
|hellaswag     |      1|none            |    10|acc        |0.8117|±  |0.0039|
|              |       |none            |    10|acc_norm   |0.9167|±  |0.0028|
|winogrande    |      1|none            |     5|acc        |0.8682|±  |0.0095|
|mmlu          |    N/A|none            |     0|acc        |0.6448|±  |0.0038|
|arc_challenge |      1|none            |    25|acc        |0.7688|±  |0.0123|
|              |       |none            |    25|acc_norm   |0.7730|±  |0.0122|
|truthfulqa_mc2|      2|none            |     0|acc        |0.7895|±  |0.0133|
|mathqa        |      1|none            |     0|acc        |0.4000|±  | 0.009|
|              |       |none            |     0|acc_norm   |0.4003|±  | 0.009|
|pubmedqa      |      1|none            |     0|acc        |0.6680|±  |0.0211|
|boolq         |      2|none            |     0|acc        |0.8346|±  |0.0065|
```

## Citations
* mlabonne
* jondurbin & Replete-AI
* bartowski
* saltlux

If you use UNA models dont forget to cite:
```
@misc{unathepitbull21b,
  title={ThePitbull: Uniform Neural Alignment}, 
  author={Xavier Murias},
  year={2024},
  publisher = {Juanako.AI},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/fblgit/UNA-ThePitbull-21.4-v1}},
}
```