See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: bigscience/bloom-560m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - c452a8f62092d721_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/c452a8f62092d721_train_data.json
  type:
    field_instruction: instruction
    field_output: response
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: leixa/caefd3b1-bcf8-49a3-8045-8aa6143ff9a6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
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: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/c452a8f62092d721_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: eb1b3b7e-205e-4952-aa9d-961daa655100
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: eb1b3b7e-205e-4952-aa9d-961daa655100
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

caefd3b1-bcf8-49a3-8045-8aa6143ff9a6

This model is a fine-tuned version of bigscience/bloom-560m on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.7478

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
• gradient_accumulation_steps: 4
• total_train_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: 200

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0146	1	2.2391
8.5008	0.2482	17	2.0717
7.9508	0.4964	34	1.9649
8.1261	0.7445	51	1.9006
7.4785	0.9927	68	1.8515
7.5754	1.2409	85	1.8145
7.3618	1.4891	102	1.7921
7.6284	1.7372	119	1.7698
7.2703	1.9854	136	1.7584
7.0179	2.2336	153	1.7514
7.3217	2.4818	170	1.7501
7.298	2.7299	187	1.7478

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.5.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1