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