Uploaded model

• Finetuned from model : alnrg2arg/blockchainlabs_7B_merged_test2_4

This is a SFT version of the model from blockchainlab test 2.4 - alnrg2arg/blockchainlabs_7B_merged_test2_4.

The project is running to make a small LLM for a on-device purpose.

Overall pipeline for this iteration is

1.Merging to make a base model (7B) 2.Prune the model to reduce the parameter (50% sparcity) 3.For recovery phase of the pruning, the DPO is chosen.

This model which is not pruned is intended to compare with the pruned model.

DPO consists of two parts : SFT and DPO - Now this model is the intermediate format (SFT) This model can also be compared to the DPO version of the model.

This is the code and parameters I chose for this model(SFT).

from transformers import TrainingArguments
from trl import SFTTrainer
from datasets import load_dataset
from unsloth import FastLanguageModel, FastMistralModel


max_seq_length = 2048 # Supports automatic RoPE Scaling, so choose any number

# Load model
model, tokenizer = FastMistralModel.from_pretrained(
    model_name = "alnrg2arg/blockchainlabs_7B_merged_test2_4,
    max_seq_length = max_seq_length,
    dtype = None, # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
    load_in_4bit = True, # Use 4bit quantization to reduce memory usage. Can be False
    #device_map = "balanced"
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

model = FastMistralModel.get_peft_model(
    model,
    r = 16,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 16,
    lora_dropout = 0, # Dropout = 0 is currently optimized
    bias = "none",    # Bias = "none" is currently optimized
    use_gradient_checkpointing = True,
    random_state = 3407,
    max_seq_length = max_seq_length,
)

The code and parameters are borrowed from https://colab.research.google.com/drive/1SKrKGV-BZoU4kv5q3g0jtE_OhRgPtrrQ?usp=sharing

Benchmark scores

Average 75.94