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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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dtype: bfloat16 |
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--- |
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# Results: |
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T: 🟦 |
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Model: CultriX/MistralTrix-v1 📑 |
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Average: 73.39 |
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ARC: 72.27 |
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HellaSwag: 88.33 |
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MMLU: 65.24 |
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TruthfulQA: 70.73 |
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Winogrande: 80.98 |
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GSM8K: 62.77 |
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# Edit/Disclaimer: |
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Currently the #1 ranked 7B LLM on the LLM Leaderboards, woah! |
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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, |
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just a guy that likes to nerd about and tinker around. |
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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 |
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Therefore, all credit basically goes to the guy who wrote that. |
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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/ |
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So huge thank you to him for sharing his knowledge and learning me a thing or two in the process! |
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# GGUF |
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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: |
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https://huggingface.co/CultriX/MistralTrix-v1-GGUF |
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I'll say it one more time though: |
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"I am a complete beginner to all of this, so if these do end up sucking don't be surprised." |
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You have been warned :) |
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# Description: |
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(trained on a single Colab GPU in less than a few hours) |
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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. |
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It surpasses the original model on several benchmarks (see results). |
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It is directly inspired by the RLHF process described by Intel/neural-chat-7b-v3-1's authors to improve performance. |
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I used the same dataset and reformatted it to apply the ChatML template. |
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The code to train this model is available on Google Colab and GitHub. |
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Fine-tuning took about an hour on Google Colab A-1000 GPU with 40GB VRAM. |
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# TRAINING SPECIFICATIONS |
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> LoRA configuration |
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peft_config = LoraConfig( |
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r=16, |
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lora_alpha=16, |
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lora_dropout=0.05, |
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bias="none", |
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task_type="CAUSAL_LM", |
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target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] |
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) |
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> Model to fine-tune |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.float16, |
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load_in_4bit=True |
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) |
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model.config.use_cache = False |
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> Reference model |
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ref_model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.float16, |
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load_in_4bit=True |
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) |
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> Training arguments |
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training_args = TrainingArguments( |
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per_device_train_batch_size=4, |
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gradient_accumulation_steps=4, |
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gradient_checkpointing=True, |
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learning_rate=5e-5, |
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lr_scheduler_type="cosine", |
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max_steps=200, |
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save_strategy="no", |
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logging_steps=1, |
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output_dir=new_model, |
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optim="paged_adamw_32bit", |
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warmup_steps=100, |
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bf16=True, |
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report_to="wandb", |
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) |
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> Create DPO trainer |
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dpo_trainer = DPOTrainer( |
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model, |
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ref_model, |
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args=training_args, |
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train_dataset=dataset, |
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tokenizer=tokenizer, |
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peft_config=peft_config, |
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beta=0.1, |
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max_prompt_length=1024, |
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max_length=1536, |
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) |
