--- language: - en license: apache-2.0 tags: - merge pipeline_tag: text-generation dtype: bfloat16 model-index: - name: MistralTrix-v1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 72.27 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MistralTrix-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.33 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MistralTrix-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MistralTrix-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 70.73 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MistralTrix-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.98 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MistralTrix-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 62.77 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MistralTrix-v1 name: Open LLM Leaderboard --- # 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, ) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_CultriX__MistralTrix-v1) | Metric |Value| |---------------------------------|----:| |Avg. |73.39| |AI2 Reasoning Challenge (25-Shot)|72.27| |HellaSwag (10-Shot) |88.33| |MMLU (5-Shot) |65.24| |TruthfulQA (0-shot) |70.73| |Winogrande (5-shot) |80.98| |GSM8k (5-shot) |62.77|