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headline_ministral_8b_8bits_r8

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metadata
library_name: peft
license: other
base_model: mistralai/Ministral-8B-Instruct-2410
tags:
  - generated_from_trainer
model-index:
  - name: workspace/FinLoRA/lora/axolotl-output/headline_mistral_8b_8bits_r8
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.10.0

base_model: mistralai/Ministral-8B-Instruct-2410
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
gradient_accumulation_steps: 2
micro_batch_size: 8
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0001
load_in_8bit: true
load_in_4bit: false
bnb_4bit_use_double_quant: false
bnb_4bit_quant_type: null
bnb_4bit_compute_dtype: null
adapter: lora
lora_model_dir: null
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
- path: /workspace/FinLoRA/data/train/headline_train.jsonl
  type:
    system_prompt: ''
    field_system: system
    field_instruction: context
    field_output: target
    format: '[INST] {instruction} [/INST]'
    no_input_format: '[INST] {instruction} [/INST]'
dataset_prepared_path: null
val_set_size: 0.02
output_dir: /workspace/FinLoRA/lora/axolotl-output/headline_mistral_8b_8bits_r8
peft_use_dora: false
peft_use_rslora: false
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false
wandb_project: finlora_models
wandb_entity: null
wandb_watch: gradients
wandb_name: headline_mistral_8b_8bits_r8
wandb_log_model: 'false'
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint: null
logging_steps: 500
flash_attention: false
deepspeed: deepspeed_configs/zero1.json
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
  pad_token: <|end_of_text|>

workspace/FinLoRA/lora/axolotl-output/headline_mistral_8b_8bits_r8

This model is a fine-tuned version of mistralai/Ministral-8B-Instruct-2410 on the /workspace/FinLoRA/data/train/headline_train.jsonl dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0555

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • total_eval_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • training_steps: 5033

Training results

Training Loss Epoch Step Validation Loss
No log 0 0 0.7341
No log 0.2503 315 0.0484
0.0704 0.5006 630 0.0517
0.0704 0.7509 945 0.0447
0.0402 1.0008 1260 0.0406
0.0352 1.2511 1575 0.0489
0.0352 1.5014 1890 0.0415
0.0321 1.7517 2205 0.0424
0.0279 2.0016 2520 0.0414
0.0279 2.2519 2835 0.0477
0.0229 2.5022 3150 0.0432
0.0229 2.7525 3465 0.0492
0.0225 3.0024 3780 0.0486
0.0162 3.2527 4095 0.0518
0.0162 3.5030 4410 0.0536
0.0131 3.7533 4725 0.0555

Framework versions

  • PEFT 0.15.2
  • Transformers 4.52.3
  • Pytorch 2.8.0.dev20250319+cu128
  • Datasets 3.6.0
  • Tokenizers 0.21.2