File size: 8,899 Bytes

---
license: cc-by-nc-4.0
model-index:
- name: Mistral-11B-TestBench11
  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: 64.42
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Mistral-11B-TestBench11
      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: 83.93
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Mistral-11B-TestBench11
      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: 63.82
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Mistral-11B-TestBench11
      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: 56.68
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Mistral-11B-TestBench11
      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: 77.74
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Mistral-11B-TestBench11
      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: 14.94
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Undi95/Mistral-11B-TestBench11
      name: Open LLM Leaderboard
---
I FUCKED UP, THIS MODEL IS MEANT TO BE A BFLOAT16 MODEL, I'M CURRENTLY REDOING IT IN THE CORRECT WAY (look at the recipe, it end in float16, i'm so dumb lmao). It SHOULD be even better, I saw the problem after finetuning it, something was off. It's usable, it rank the best, but it's not even on the right float...KEK

Fixed model should be here: [NeverSleep/Mistral-11B-OmniMix-bf16](https://huggingface.co/NeverSleep/Mistral-11B-OmniMix-bf16)

Don't mind this one at the moment, I need to finetune it for RP, it's just a test.

## Description

This repo contains fp16 files of Mistral-11B-OmniMix.

My goal for this model was only to make it score the highest possible with merge and layer toying, proving that:
- Benchmark are objective
- You should try a model yourself and don't go blindly to the highest rated one
- Merge/Layer toying CAN be usable to do better model (maybe?)


## Model used
- [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
- [Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus)
- [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B)
- [zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)



## Prompt template

The best one after further testing is this one:

```
<|system|>
Below is an instruction that describes a task. Write a response that appropriately completes the request.
<|user|>
{prompt}
<|assistant|>
```


![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/tWIx8yeoallv94zrhN6L-.png)

But these one work too:

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

```

```
USER: <prompt>
ASSISTANT:
```

Or use any prompting system from one of the 4 source model, should work.

## The secret sauce

Mistral-11B-OpenOrcaPlatypus :
```
slices:
  - sources:
    - model: Open-Orca/Mistral-7B-OpenOrca
      layer_range: [0, 24]
  - sources:
    - model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```

Mistral-11B-CC-Zephyr :
```
slices:
  - sources:
    - model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
      layer_range: [0, 24]
  - sources:
    - model: "/content/drive/MyDrive/Zephyr-7B"
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```

Mistral-11B-OmniMix :
```
slices:
  - sources:
      - model: Mistral-11B-OpenOrcaPlatypus
        layer_range: [0, 48]
      - model: Mistral-11B-CC-Zephyr
        layer_range: [0, 48]
merge_method: slerp
base_model: Undi95/Mistral-11B-OpenOrcaPlatypus
parameters:
  t:
    - filter: lm_head 
      value: [0.75]
    - filter: embed_tokens
      value: [0.75]
    - filter: self_attn
      value: [0.75, 0.25]
    - filter: mlp
      value:  [0.25, 0.75]
    - filter: layernorm
      value: [0.5, 0.5]
    - filter: modelnorm
      value: [0.75]
    - value: 0.5 # fallback for rest of tensors
dtype: float16
```
I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here.

## Some scoring I done myself

This was named "Mistral-11B-TestBench11", keep that in mind while looking trough this.

hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-Test), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4
|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.5597|±  |0.0145|
|             |       |acc_norm|0.5819|±  |0.0144|
|arc_easy     |      0|acc     |0.8308|±  |0.0077|
|             |       |acc_norm|0.8215|±  |0.0079|
|hellaswag    |      0|acc     |0.6371|±  |0.0048|
|             |       |acc_norm|0.8213|±  |0.0038|
|piqa         |      0|acc     |0.8134|±  |0.0091|
|             |       |acc_norm|0.8275|±  |0.0088|
|truthfulqa_mc|      1|mc1     |0.3990|±  |0.0171|
|             |       |mc2     |0.5685|±  |0.0155|
|winogrande   |      0|acc     |0.7474|±  |0.0122|


![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/LggyIlV-oY7NbLwi7mnix.png)

This model seem to be the best out of my 3 latest try:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/hnqNyljs5Y8JppuA_io8w.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/b-a-sB2qRHApPX52S2nD7.png)

You can find all the work I have done trying on this [Pastebin](https://pastebin.com/nHLCxQJv).

## Others

Special thanks to Sushi, [Henky](https://github.com/KoboldAI/KoboldAI-Client) for the machine he give me for big task, and [Charles Goddard](https://github.com/cg123) for his amazing tool.

If you want to support me, you can [here](https://ko-fi.com/undiai).

# [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_Undi95__Mistral-11B-TestBench11)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 53.01   |
| ARC (25-shot)         | 64.42          |
| HellaSwag (10-shot)   | 83.93    |
| MMLU (5-shot)         | 63.82         |
| TruthfulQA (0-shot)   | 56.68   |
| Winogrande (5-shot)   | 77.74   |
| GSM8K (5-shot)        | 14.94        |
| DROP (3-shot)         | 9.57         |

# [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_Undi95__Mistral-11B-TestBench11)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |60.25|
|AI2 Reasoning Challenge (25-Shot)|64.42|
|HellaSwag (10-Shot)              |83.93|
|MMLU (5-Shot)                    |63.82|
|TruthfulQA (0-shot)              |56.68|
|Winogrande (5-shot)              |77.74|
|GSM8k (5-shot)                   |14.94|