See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: echarlaix/tiny-random-mistral
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 7e74a6d550157d47_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/7e74a6d550157d47_train_data.json
  type:
    field_instruction: anchor
    field_output: positive
    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: true
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/dd0059ae-69f3-42c2-982f-700ce6a74fa1
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: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/7e74a6d550157d47_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
special_tokens:
  pad_token: </s>
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: d312c45a-01ef-4b46-8176-553e264ca691
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d312c45a-01ef-4b46-8176-553e264ca691
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

dd0059ae-69f3-42c2-982f-700ce6a74fa1

This model is a fine-tuned version of echarlaix/tiny-random-mistral on the None dataset. It achieves the following results on the evaluation set:

• Loss: 10.3503

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0002	1	10.3771
41.5093	0.0016	9	10.3761
41.4928	0.0031	18	10.3733
41.4788	0.0047	27	10.3702
41.4604	0.0062	36	10.3667
41.4613	0.0078	45	10.3629
41.4384	0.0093	54	10.3590
41.441	0.0109	63	10.3554
41.4114	0.0124	72	10.3527
41.4077	0.0140	81	10.3511
41.4119	0.0155	90	10.3504
41.4163	0.0171	99	10.3503

Framework versions

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