jackryder00/nadialowepoch

See axolotl config

axolotl version: 0.4.1

# CONFIG EXPERIMENTO 2: LR ALTO (2e-5), 4 ÉPOCAS
base_model: chuanli11/Llama-3.2-3B-Instruct-uncensored
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
special_tokens:
  pad_token: "<|eot_id|>"
datasets:
  - path: /root/nadia_finetune/dataset_v3.jsonl
    type: alpaca
val_set_size: 0.05
sequence_len: 4096
load_in_4bit: true
adapter: qlora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
learning_rate: 2e-5 # <<< CAMBIO AQUÍ
num_epochs: 4
micro_batch_size: 1
gradient_accumulation_steps: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
warmup_steps: 100
output_dir: /root/nadia_finetune/lora-output-exp2-lr_alto # <<< CAMBIO AQUÍ
save_strategy: steps
save_steps: 200
logging_steps: 10

root/nadia_finetune/lora-output-exp2-lr_alto

This model is a fine-tuned version of chuanli11/Llama-3.2-3B-Instruct-uncensored on the None dataset. It achieves the following results on the evaluation set:

• Loss: 3.1336

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: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
4.2332	0.9825	42	5.2464
3.6295	1.9883	85	4.0735
3.0969	2.9942	128	3.2357
2.9725	3.9298	168	3.1336

Framework versions

• PEFT 0.13.2
• Transformers 4.45.2
• Pytorch 2.3.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1