metadata

library_name: transformers
license: apache-2.0
datasets:
  - berkeley-nest/Nectar
pipeline_tag: text-generation
base_model: Na0s/Llama-3.1-8b-Pruned-4-Layers_LoRA-PEFT

> alt="Model-card-peft-lora-1.0" align="center">

Model Card for Na0s/Llama-3.1-8B-Pruned-4-Layers_LoRA-PEFT-1.0

Model Details

Model Description

Finetuned from model:[Na0s/Llama-3.1-8B-Pruned-4-Layers_LoRA-PEFT]

Training Details

Parameters used for Na0s/Llama-3.1-8B-Pruned-4-Layers_LoRA-PEFT-1.0

model = FastLanguageModel.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 = 0.05, 
bias = "none",    

use_gradient_checkpointing = "unsloth", 
random_state = 3407,
use_rslora = False,  
loftq_config = None,

)

trainer = SFTTrainer(

model = model,
tokenizer = tokenizer,
train_dataset = dataset,
dataset_text_field = "completion",

max_seq_length = max_seq_length,
dataset_num_proc = 2,
packing = False, 

args = TrainingArguments(
    per_device_train_batch_size = 6,
    gradient_accumulation_steps = 4,
    warmup_steps = 5,
    max_steps=5000,
    learning_rate = 2e-4,
    fp16 = not is_bfloat16_supported(),
    
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    optim = "adamw_8bit",
    weight_decay = 0.01,
    lr_scheduler_type = "linear",
    seed = 3407,
    output_dir = "outputs_2",
    push_to_hub=True,
    hub_always_push=True,
),

)

Dataset: Berkeley-nest/Nectar

Training Data

[berkeley-nest/Nectar]

Evaluation

MMLU Pro 0-shot:

Evaluation Data

[TIGER-AI-Lab/MMLU-Pro]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

[More