metadata

base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: Llama-3.1-8B-KAM
tags:
  - generated_from_trainer
  - trl
  - sft
licence: license

Model Card for Llama-3.1-8B-KAM

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MaRyAm1295/Llama-3.1-8B-KAM", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 3407
gradient_accumulation_steps: 16
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20
training_steps: 500
mixed_precision_training: Native AMP

Training results

Step Training Loss

50 2.155100
100 1.866100
150 1.836300
200 1.814600
250 1.810600
300 1.790900
350 1.774300
400 1.777800
450 1.787600
500 1.759000

Framework versions

PEFT 0.13.2
Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.0.2
Tokenizers 0.19.1