safetensors mistral 4-bit bitsandbytes region:us
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Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

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MBX-7B-v3-DPO - bnb 4bits
- Model creator: https://huggingface.co/macadeliccc/
- Original model: https://huggingface.co/macadeliccc/MBX-7B-v3-DPO/




Original model description:
---
license: cc
library_name: transformers
datasets:
- jondurbin/truthy-dpo-v0.1
model-index:
- name: MBX-7B-v3-DPO
  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: 73.55
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/MBX-7B-v3-DPO
      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: 89.11
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/MBX-7B-v3-DPO
      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: 64.91
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/MBX-7B-v3-DPO
      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: 74.0
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/MBX-7B-v3-DPO
      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: 85.56
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/MBX-7B-v3-DPO
      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: 69.67
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/MBX-7B-v3-DPO
      name: Open LLM Leaderboard
---

# MBX-7B-v3-DPO

This model is a finetune of [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) using jondurbin/truthy-dpo-v0.1

![MBX-v3-orca](MBX-v3-orca.png)

## Code Example 

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/MBX-7B-v3-DPO")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/MBX-7B-v3-DPO")

messages = [
    {"role": "system", "content": "Respond to the users request like a pirate"},
    {"role": "user", "content": "Can you write me a quicksort algorithm?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
```

## Example Output 

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/g5_PTJhGJAcG88wmZz1IO.png)

## GGUF

Available [here](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO-GGUF/tree/main)

## Exllamav2

Quants are available from bartowski, check them out [here](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2)

Download the size you want below, VRAM figures are estimates.

| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB |  9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |

## Evaluations

## EQ-Bench Comparison

<pre>----Benchmark Complete----
2024-01-30 15:22:18
Time taken: 145.9 mins
Prompt Format: ChatML
Model: macadeliccc/MBX-7B-v3-DPO
Score (v2): 74.32
Parseable: 166.0
---------------
Batch completed
Time taken: 145.9 mins
---------------
</pre>

### Original Model
<pre>----Benchmark Complete----
2024-01-31 01:26:26
Time taken: 89.1 mins
Prompt Format: Mistral
Model: flemmingmiguel/MBX-7B-v3
Score (v2): 73.87
Parseable: 168.0
---------------
Batch completed
Time taken: 89.1 mins
---------------
</pre>

|                              Model                              |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|-----------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[MBX-7B-v3-DPO](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO)|  45.16|  77.73|     74.62|   48.83|  61.58|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |27.95|±  |  2.82|
|                              |       |acc_norm|26.77|±  |  2.78|
|agieval_logiqa_en             |      0|acc     |41.01|±  |  1.93|
|                              |       |acc_norm|40.55|±  |  1.93|
|agieval_lsat_ar               |      0|acc     |25.65|±  |  2.89|
|                              |       |acc_norm|23.91|±  |  2.82|
|agieval_lsat_lr               |      0|acc     |50.78|±  |  2.22|
|                              |       |acc_norm|52.94|±  |  2.21|
|agieval_lsat_rc               |      0|acc     |66.54|±  |  2.88|
|                              |       |acc_norm|65.80|±  |  2.90|
|agieval_sat_en                |      0|acc     |77.67|±  |  2.91|
|                              |       |acc_norm|77.67|±  |  2.91|
|agieval_sat_en_without_passage|      0|acc     |43.20|±  |  3.46|
|                              |       |acc_norm|43.20|±  |  3.46|
|agieval_sat_math              |      0|acc     |32.27|±  |  3.16|
|                              |       |acc_norm|30.45|±  |  3.11|

Average: 45.16%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |68.43|±  |  1.36|
|             |       |acc_norm|68.34|±  |  1.36|
|arc_easy     |      0|acc     |87.54|±  |  0.68|
|             |       |acc_norm|82.11|±  |  0.79|
|boolq        |      1|acc     |88.20|±  |  0.56|
|hellaswag    |      0|acc     |69.76|±  |  0.46|
|             |       |acc_norm|87.40|±  |  0.33|
|openbookqa   |      0|acc     |40.20|±  |  2.19|
|             |       |acc_norm|49.60|±  |  2.24|
|piqa         |      0|acc     |83.68|±  |  0.86|
|             |       |acc_norm|85.36|±  |  0.82|
|winogrande   |      0|acc     |83.11|±  |  1.05|

Average: 77.73%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |58.87|±  |  1.72|
|             |       |mc2   |74.62|±  |  1.44|

Average: 74.62%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|60.00|±  |  3.56|
|bigbench_date_understanding                     |      0|multiple_choice_grade|63.14|±  |  2.51|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|47.67|±  |  3.12|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|22.56|±  |  2.21|
|                                                |       |exact_str_match      | 0.84|±  |  0.48|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|33.20|±  |  2.11|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|23.00|±  |  1.59|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|59.67|±  |  2.84|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|47.40|±  |  2.24|
|bigbench_navigate                               |      0|multiple_choice_grade|56.10|±  |  1.57|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|71.25|±  |  1.01|
|bigbench_ruin_names                             |      0|multiple_choice_grade|56.47|±  |  2.35|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|35.27|±  |  1.51|
|bigbench_snarks                                 |      0|multiple_choice_grade|73.48|±  |  3.29|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|75.46|±  |  1.37|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|52.10|±  |  1.58|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|22.64|±  |  1.18|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|19.83|±  |  0.95|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|59.67|±  |  2.84|

Average: 48.83%

Average score: 61.58%

Elapsed time: 02:37:39
# [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_macadeliccc__MBX-7B-v3-DPO)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.13|
|AI2 Reasoning Challenge (25-Shot)|73.55|
|HellaSwag (10-Shot)              |89.11|
|MMLU (5-Shot)                    |64.91|
|TruthfulQA (0-shot)              |74.00|
|Winogrande (5-shot)              |85.56|
|GSM8k (5-shot)                   |69.67|



