See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/Qwen2.5-Coder-7B-Instruct
bf16: true
chat_template: llama3
cosine_min_lr_ratio: 0.3
dataset_prepared_path: null
datasets:
- data_files:
  - b870f2efc62784b0_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/b870f2efc62784b0_train_data.json
  type:
    field_instruction: instruction
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 4
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Romain-XV/22500caa-2bbd-4af3-b84b-203da328e702
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 2150
micro_batch_size: 4
mlflow_experiment_name: /tmp/b870f2efc62784b0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 200
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04854416062291867
wandb_entity: null
wandb_mode: online
wandb_name: 0f3f41a6-d6fc-4e0e-96e4-e7fcdc965eb3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0f3f41a6-d6fc-4e0e-96e4-e7fcdc965eb3
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

22500caa-2bbd-4af3-b84b-203da328e702

This model is a fine-tuned version of unsloth/Qwen2.5-Coder-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2403

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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 2150

Training results

Training Loss	Epoch	Step	Validation Loss
0.4922	0.0002	1	0.5588
0.2211	0.0327	200	0.2636
0.2812	0.0653	400	0.2606
0.2185	0.0980	600	0.2610
0.1998	0.1306	800	0.2576
0.2312	0.1633	1000	0.2537
0.229	0.1959	1200	0.2504
0.2113	0.2286	1400	0.2471
0.1745	0.2612	1600	0.2444
0.1963	0.2939	1800	0.2417
0.1982	0.3265	2000	0.2403

Framework versions

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