license: apache-2.0
language:
- en
pipeline_tag: text-generation
dtype: bfloat16
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, converted with exl2 quantization
Description:
Model: CultriX/MistralTrix-v1
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.
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, )