README.md

---
license: apache-2.0
tags:
- UNA
- simple-math
- juanako
base_model: abacusai/Smaug-34B-v0.1
datasets:
- fblgit/simple-math
- jondurbin/bagel-v0.3
model-index:
- name: UNA-SimpleSmaug-34b-v1beta
  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: 74.57
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      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: 86.74
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      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: 76.68
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      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: 70.17
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      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: 83.82
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      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: 72.48
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 45.56
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 32.78
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 0.15
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 8.95
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 11.96
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 39.33
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/UNA-SimpleSmaug-34b-v1beta
      name: Open LLM Leaderboard
---

# UNA-SimpleSmaug-34b-v1beta

Scoring 04-February-2024 #1 34B model, outperforming its original base model Smaug-34B-v0.1 with `77.41` 😎
Oh, btw.. this one went thru SFT so the abacus inside Smaug is back to normal.. so you can further train/dpo him .. RESET!.. 

*UPDATES* March : Stills undisputed 34B King
                  Smaug 70B stills undisputed 70B King

====
And people wonders.. why there is no UNA of Hermes or Smaug 70B? << i dont think is worth the time to spend on a model that is widely known for not being too useful, likely UNA can fix some of the internal mess.. 
for Hermes, we spoke chitchat quick a couple times but nothing solid, but we would like to make a reborn of excellent models using UNA, just liek we did with UNA-Dolphin where we saw
relevant performance is short time.
===

![UNA](https://huggingface.co/fblgit/UNA-SimpleSmaug-34b-v1beta/resolve/main/unasimple.png)
Applied UNA only on the Attention, not on the MLP's
* Is based on Smaug
* SimpleMath dataset
* It was trained on Axolotl

## Experiment
The thing here is to understand whats the impact of SimpleMath applied at the attention layer during a SFT session and how it impacts on the neural network overall.

Results: Improving mathematican and reasoning capabilities without degrading and presserving previous training sessions.

**And enjoy our ModelSimilarities tool detector** https://github.com/fblgit/model-similarity where we confirmed numerically the bloodties of the model.
## Evals


|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |77.41|
|AI2 Reasoning Challenge (25-Shot)|74.57|
|HellaSwag (10-Shot)              |86.74|
|MMLU (5-Shot)                    |76.68|
|TruthfulQA (0-shot)              |70.17|
|Winogrande (5-shot)              |83.82|
|GSM8k (5-shot)                   |72.48|

```
|    Task     |Version| Metric |Value            |
|-------------|------:|--------|----------------:|
|arc_challenge|     HF|acc_norm| 0.7457337883959 |
|gsm8k        |     HF|acc     | 0.7247915087187 |
|mmlu         |     HF|acc     | 0.7649553475572 |
|mmlu         |     HF|acc_norm| 0.7681713551647 |
|hellaswag    |     HF|acc_norm| 0.8673571001792 | 
|truthfulqa   |     HF|mc2     | 0.7016557407771 |
|winogrande   |     HF|acc     | 0.8382004735595 |
|------------------------------------------------|
```

Increasing GSM, MMLU, ARC, WINO.

## Citations
To abacusai for making Smaug-34B, the Bagel, and all the magic behind the base model.

**If you use the model, provide citation even for merges or anything.**
# [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-SimpleSmaug-34b-v1beta)



# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-SimpleSmaug-34b-v1beta)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |23.12|
|IFEval (0-Shot)    |45.56|
|BBH (3-Shot)       |32.78|
|MATH Lvl 5 (4-Shot)| 0.15|
|GPQA (0-shot)      | 8.95|
|MuSR (0-shot)      |11.96|
|MMLU-PRO (5-shot)  |39.33|