EDIT:

Always check my space for the latest benchmark results for my models!

https://huggingface.co/spaces/CultriX/Yet_Another_LLM_Leaderboard

Results:

T: 🟦 Model: CultriX/MistralTrix-v1 📑 Average: 73.39 ARC: 72.27 HellaSwag: 88.33 MMLU: 65.24 TruthfulQA: 70.73 Winogrande: 80.98 GSM8K: 62.77

Edit/Disclaimer:

Currently the #1 ranked 7B LLM on the LLM Leaderboards, woah! I did not expect that result at all and am in no way a professional when it comes to LLM's or computer science in general, just a guy that likes to nerd about and tinker around.

For those wondering how I achieved this, the answer is that I simply attempted to apply the techniques outlined in this amazing article myself: https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac Therefore, all credit basically goes to the guy who wrote that. He offers the exact Colab notebook I used to train this model for free, as well as a really nice GitHub page I hope he doesn't mind me sharing: https://github.com/mlabonne/llm-course/ So huge thank you to him for sharing his knowledge and learning me a thing or two in the process!

GGUF

I attempted to quantisize the model myself, which again I pretty much have no clue about, but it seems to run fine for me when I test them: https://huggingface.co/CultriX/MistralTrix-v1-GGUF

I'll say it one more time though: "I am a complete beginner to all of this, so if these do end up sucking don't be surprised."

You have been warned :)

Description:

(trained on a single Colab GPU in less than a few hours)

MistralTrix-v1 is an zyh3826/GML-Mistral-merged-v1 model that has been further fine-tuned with Direct Preference Optimization (DPO) using Intel's dataset for neural-chat-7b-v3-1. It surpasses the original model on several benchmarks (see results).

It is directly inspired by the RLHF process described by Intel/neural-chat-7b-v3-1's authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.

The code to train this model is available on Google Colab and GitHub. Fine-tuning took about an hour on Google Colab A-1000 GPU with 40GB VRAM.

TRAINING SPECIFICATIONS

LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] )

Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, load_in_4bit=True ) model.config.use_cache = False

Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, load_in_4bit=True )

Training arguments training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=200, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", )

Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, max_prompt_length=1024, max_length=1536, )