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coastalcph/
llama3-fr_alpaca-lora

generated_from_trainer

cfierro/fr_alpaca

NousResearch/Meta-Llama-3-8B

adapter

other

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See axolotl config

axolotl version: 0.11.0.dev0

yaml

base_model: NousResearch/Meta-Llama-3-8B
# optionally might have model_type or tokenizer_type
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

load_in_8bit: true
load_in_4bit: false

datasets:
  - path: cfierro/fr_alpaca
    type: alpaca
dataset_prepared_path: /workspace/axolotl-datasets/llama3-fr_alpaca
val_set_size: 0.05
output_dir: /workspace/axolotl-outputs/llama3-fr_alapaca-lora

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: weight-diff-ft
wandb_entity: cfierro
wandb_watch: all
wandb_name: llama3-fr_alpaca-lora
wandb_log_model: "false"
hub_model_id: cfierro/llama3-fr_alapaca-lora

gradient_accumulation_steps: 4
micro_batch_size: 2
max_steps: 1000  # 8k examples at most 
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: auto
tf32: false

gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_steps: 10
early_stopping_patience: 5
eval_steps: 60  # bs=4*2 -> eval every 8*60=480 examples  
save_steps: 60  # needed for config validation
save_total_limit: 1
load_best_model_at_end: true
weight_decay: 0.0
special_tokens:
   pad_token: <|end_of_text|>

llama3-fr_alapaca-lora

This model is a fine-tuned version of NousResearch/Meta-Llama-3-8B on the cfierro/fr_alpaca dataset. It achieves the following results on the evaluation set:

Loss: 0.8144

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
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: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0	0	1.0157
0.8517	0.3061	60	0.8213
0.7548	0.6122	120	0.7892
0.7335	0.9184	180	0.7639
0.553	1.2245	240	0.7702
0.5687	1.5306	300	0.7620
0.6222	1.8367	360	0.7538
0.4544	2.1429	420	0.7803
0.4577	2.4490	480	0.7835
0.4629	2.7551	540	0.7733
0.3282	3.0612	600	0.8035
0.3396	3.3673	660	0.8144

Framework versions

PEFT 0.15.2
Transformers 4.52.4
Pytorch 2.7.0+cu128
Datasets 3.6.0
Tokenizers 0.21.2

coastalcph/llama3-fr_alpaca-lora

llama3-fr_alapaca-lora

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

coastalcph/
llama3-fr_alpaca-lora