bloom-7b1-datasetSFT73_lora4

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<details><summary>See axolotl config</summary>

axolotl version: 0.17.0.dev0

base_model: bigscience/bloom-7b1
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

load_in_8bit: false
load_in_4bit: false


datasets:
  - path: philipperen55/datasetSFT74bloom 
    ds_type: json
    data_files: datasetSFT74bloom.jsonl
    type: input_output
  

train_on_inputs: false
add_eos_token: false

  
dataset_prepared_path: /workspace/prepared_data
val_set_size: 0.05
output_dir: /workspace/output


sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false
group_by_length: true





adapter: lora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.1
lora_target_modules:
  - query_key_value
  - dense
  - dense_h_to_4h
  - dense_4h_to_h


gradient_accumulation_steps: 4
micro_batch_size: 4 
num_epochs: 3
learning_rate: 5e-5
lr_scheduler: cosine
warmup_ratio: 0.05
optimizer: adamw_torch_fused
weight_decay: 0.01
max_grad_norm: 1.0


bf16: true
fp16: false
tf32: true



overrides_of_model_config:
  use_cache: false
  

gradient_checkpointing: false


seed: 42
#mettre 24 si ya plus de 24 vspu, sinon mettre 16 si ya 24vcpu
dataset_num_proc: 16


logging_steps: 5
save_steps: 50
eval_strategy: steps 
eval_steps: 50
save_total_limit: 1


wandb_project: datasetSFT73


hub_model_id: philipperen55/bloom-7b1-datasetSFT73_lora4
push_to_hub: true
hub_strategy: every_save

</details><br>

bloom-7b1-datasetSFT73_lora4

This model is a fine-tuned version of bigscience/bloom-7b1 on the philipperen55/datasetSFT74bloom dataset. It achieves the following results on the evaluation set:

• Loss: 2.2421
• Ppl: 9.4130
• Memory/max Active (gib): 103.37
• Memory/max Allocated (gib): 103.37
• Memory/device Reserved (gib): 162.69

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: 5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 14
• training_steps: 288

Training results

Framework versions

• PEFT 0.19.1
• Transformers 5.9.0
• Pytorch 2.10.0+cu128
• Datasets 4.8.5
• Tokenizers 0.22.2