MBX-7B-v3-DPO

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.

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>

AGIEval

Average: 45.16%

GPT4All

Average: 77.73%

TruthfulQA

Average: 74.62%

Bigbench

Average: 48.83%

Average score: 61.58%

Elapsed time: 02:37:39

Open LLM Leaderboard Evaluation Results

Detailed results can be found here