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---
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
## 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
## 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|
|