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MBX-7B-v3-DPO

This model is a finetune of flemmingmiguel/MBX-7B-v3 using jondurbin/truthy-dpo-v0.1

MBX-v3-orca

Code Example

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

GGUF

Available here

Exllamav2

Quants are available from bartowski, check them out here

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

Branch Bits lm_head bits VRAM (4k) VRAM (16k) VRAM (32k) Description
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 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 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 4.25 6.0 5.3 GB 6.7 GB 8.7 GB GPTQ equivalent bits per weight, slightly higher quality.
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

----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
---------------

Original Model

----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
---------------
Model AGIEval GPT4All TruthfulQA Bigbench Average
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

Detailed results can be found here

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
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Dataset used to train macadeliccc/MBX-7B-v3-DPO

Evaluation results