Update README.md

README.md

@@ -7,100 +7,7 @@ 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
+Currently the #2 ranked 7B LLM on the LLM Leaderboards, woah!
+https://huggingface.co/v1olet/v1olet_merged_dpo_7B
 
-# 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,
-)
+converted to exl2 5.0 bpw