---
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
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.

Quant Versions available at [bartowski/UNA-ThePitbull-21.4B-v2-GGUF](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF)

## 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 for 1 epoch
2. DPO with: 1.0e-4 to min_lr 5.0e-5 for 1 epoch
* `mlabonne/orpo-dpo-mix-40k`
* `jondurbin/py-dpo-v0.1`

# Evaluations
## [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|

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}},
}
```