See axolotl config

axolotl version: 0.12.0.dev0

adapter: lora
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
bf16: true
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - ecaedadc08522661_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_instruction: instruct
    field_output: output
    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_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: false
hub_model_id: cpheemagazine/9e2d0f4f-9d6c-4fa1-b64a-87a0795d7a9f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 100
micro_batch_size: 60
mlflow_experiment_name: /tmp/ecaedadc08522661_train_data.json
model_card: false
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
push_to_hub: true
resume_from_checkpoint: null
rl: null
s2_attention: null
sample_packing: false
save_steps: 437
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trl: null
trust_remote_code: true
val_set_size: 0.0
wandb_name: 6dd00401-5815-43fd-999d-6b30aedf53fa
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: 6dd00401-5815-43fd-999d-6b30aedf53fa
warmup_steps: 291
weight_decay: 0
xformers_attention: null

9e2d0f4f-9d6c-4fa1-b64a-87a0795d7a9f

This model is a fine-tuned version of deepseek-ai/deepseek-coder-6.7b-instruct on an unknown 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: 0.0002
• train_batch_size: 60
• eval_batch_size: 60
• seed: 42
• 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: 291
• training_steps: 100

Training results

Framework versions

• PEFT 0.15.2
• Transformers 4.53.1
• Pytorch 2.7.1+cu128
• Datasets 3.6.0
• Tokenizers 0.21.2