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!
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
Detailed results can be found here
| 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 |