|
--- |
|
license: apache-2.0 |
|
language: |
|
- en |
|
pipeline_tag: text-generation |
|
dtype: bfloat16 |
|
tags: |
|
- merge |
|
--- |
|
|
|
# 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, |
|
) |
