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souging/
55a75b74-9898-4a91-a17f-8d2f65955b70

Name: 55a75b74-9898-4a91-a17f-8d2f65955b70
Availability: InStock

generated_from_trainer

HuggingFaceH4/tiny-random-LlamaForCausalLM

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: HuggingFaceH4/tiny-random-LlamaForCausalLM
bf16: auto
dataset_prepared_path: null
datasets:
- data_files:
  - 20ff8c2b3aea19b8_train_data.json
  ds_type: json
  format: custom
  path: /root/G.O.D-test/core/data/20ff8c2b3aea19b8_train_data.json
  type:
    field_input: original_version
    field_instruction: title
    field_output: french_version
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
eval_max_new_tokens: 128
eval_steps: 100
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: souging/55a75b74-9898-4a91-a17f-8d2f65955b70
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 6e-5
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 1500
micro_batch_size: 8
mlflow_experiment_name: /tmp/20ff8c2b3aea19b8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 0
saves_per_epoch: null
sequence_len: 2048
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
warmup_steps: 100
weight_decay: 0.01
xformers_attention: false

55a75b74-9898-4a91-a17f-8d2f65955b70

This model is a fine-tuned version of HuggingFaceH4/tiny-random-LlamaForCausalLM on the None dataset.

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: 6e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 64
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: 100
training_steps: 1500

Training results

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.3